Innovations in E-learning governance for sustainable higher education in Saudi Arabia: an integrated policy analysis of national frameworks aligned with Vision 2030

1 Introduction

Higher education lies at the intersection of two major transformations shaping the twenty-first century: the global commitment to sustainable development embodied in the United Nations 2030 Agenda and its Sustainable Development Goals (SDGs) (United Nations, 2015), and the rapid digital transformation of teaching, learning, and institutional management. Universities contribute to sustainable development not only through education and research but also through their organisational practises, technological infrastructures, human-capability development, and wider societal roles (Alghamdi, 2018a; Abubakar et al., 2020). The governance of digital higher education has therefore become an increasingly important concern, particularly in relation to how strategic direction, quality assurance, digital infrastructure, innovation, human-capability development, and performance accountability can collectively support more sustainable educational systems.

This challenge is especially relevant in the Kingdom of Saudi Arabia, where education, human-capability development, and digital transformation are central components of the national reform agenda. Saudi Vision 2030, launched in 2016, established a “vibrant society, thriving economy, and ambitious nation” as the overarching framework for national transformation and positioned human-capability development and knowledge production amongst its strategic priorities (Alghamdi, 2018a; Badkook, 2025). The transformation of the Saudi higher-education system has proceeded alongside major investments in digitalisation, institutional quality, skills development, educational innovation, and technology-enabled learning (Alghamdi, 2018b). As these initiatives have expanded, greater attention has been directed towards the governance arrangements through which national strategic priorities, digital-learning standards, institutional capabilities, and performance mechanisms are connected within the higher-education system.

Existing scholarship provides substantial evidence concerning sustainability, digital transformation, e-learning, and emerging artificial-intelligence applications in Saudi and Gulf higher education (Abubakar et al., 2020; Rezk et al., 2030; Wajid, 2025; Alshuwaikhat et al., 2016; Alshammari, 2025; Bataeineh and Aga, 2022; Alhusaini, 2025; Bystrov and Panigrahi, 2021; Al'Abri et al., 2026; Alotaibi, 2022; Islam and Khan, 2024; Alqarawi and Alnamlah, 2025; Khan et al., 2025; Ronaghi and Ronaghi, 2025; Al Mahameed et al., 2025; Aldogiher et al., 2026; Aljendan, 2024). Previous studies have documented developments in institutional sustainability initiatives, digital infrastructure, technology-enabled learning, quality assurance, and AI adoption, whilst also identifying continuing challenges associated with implementation, organisational capacity, institutional participation, professional capabilities, accountability, and sustainability assessment (Abubakar et al., 2020; Rezk et al., 2030; Wajid, 2025; Alshuwaikhat et al., 2016; Alshammari, 2025; Bataeineh and Aga, 2022; Alhusaini, 2025; Bystrov and Panigrahi, 2021; Al'Abri et al., 2026; Alotaibi, 2022; Islam and Khan, 2024; Alqarawi and Alnamlah, 2025; Khan et al., 2025; Ronaghi and Ronaghi, 2025). Research on Saudi universities further indicates that formal strategic commitments and technological resources are enacted through varied institutional arrangements involving leadership, organisational practises, professional capacity, infrastructure, and accountability mechanisms (Al Mahameed et al., 2025; Aldogiher et al., 2026; Aljendan, 2024). These findings suggest that the governance of digital higher education cannot be understood solely in terms of technological provision but requires attention to the wider strategic, organisational, regulatory, and human-capability environment in which digital learning operates.

Two complementary perspectives are particularly relevant to this governance challenge. A technology-and-infrastructure perspective emphasises digital platforms, interoperability, data systems, cybersecurity, artificial intelligence, learning analytics, and institutional technological readiness as enabling conditions for digitally supported higher education. A practise-and-governance perspective directs attention to institutional rules, organisational routines, leadership arrangements, professional capabilities, accountability mechanisms, and contextual conditions through which technological resources are incorporated into educational practise. Rather than treating these perspectives as competing causal explanations, the present study regards them as complementary dimensions of digital institutional transformation. Technological infrastructure establishes enabling conditions, whilst governance and organisational arrangements influence how those conditions are institutionalised, coordinated, monitored, and connected to wider educational and sustainability objectives.

Despite the growing literature, an important policy-level gap remains. Sustainability governance and digital transformation have frequently been examined as related but analytically distinct areas, whilst national policy instruments are often studied individually rather than as components of a broader governance architecture. Consequently, limited attention has been devoted to examining how strategic direction, e-learning regulation, digital infrastructure, innovation, human-capability development, and performance accountability are represented across different national policy instruments and how these functions collectively inform the governance of sustainable digital higher education. In the Saudi context specifically, relatively little integrated analysis has examined where national instruments converge, where they perform complementary functions, and where explicit connections amongst digital-learning performance mechanisms, broader strategic priorities, human-capability outcomes, and sustainability-oriented objectives remain limited.

The present study addresses this gap through an integrated analysis of three purposively selected Saudi national policy instruments: the Ministry of Education’s General Education Strategic Plan 2020–2030 (D1), the National eLearning Center (NeLC) E-Learning Standards (D2), and the Human Capability Development Program (HCDP) 2025 Annual Report (D3) (Badkook, 2025; Alsayyari et al., 2019). Together, these instruments represent strategic, regulatory, and implementation- or performance-oriented components of the wider policy environment relevant to digital learning and sustainable higher education. Their combined analysis provides an opportunity to examine how different governance functions are articulated across the national policy landscape rather than assessing each instrument in isolation.

Accordingly, the study addresses the following research question: How do selected Saudi national strategic, regulatory, and human-capability policy instruments collectively structure the governance of e-learning in relation to sustainable higher education, and where do they demonstrate convergence, complementarity, or limited explicit cross-framework integration?

To address this question, the study applies integrated qualitative policy analysis using directed qualitative content analysis and cross-document thematic synthesis. The analysis is organised around six governance dimensions: strategic alignment; quality assurance and standards; digital infrastructure and platforms; innovation, AI, and adaptive learning; human-capability development; and accountability and performance measurement. The theoretical foundations of these dimensions are developed systematically in the following literature review through engagement with institutional and governance perspectives, practise-oriented approaches, sustainability governance, and digital and technology-governance scholarship.

The study makes three interconnected contributions. Theoretically, it brings institutional and governance perspectives, practise-oriented approaches to sustainability implementation, and digital and AI governance scholarship into a multidimensional account of e-learning governance in sustainable higher education (Ronaghi and Ronaghi, 2025; Al Mahameed et al., 2025; Aldogiher et al., 2026). Methodologically, it demonstrates how national policy instruments performing different strategic, regulatory, and implementation functions can be examined within a common analytical framework whilst preserving their differentiated governance roles. Practically, it identifies areas of convergence, functional complementarity, differentiated responsibility, and opportunities for stronger coordination across existing national governance and performance arrangements.

Building on the cross-document analysis, the study further develops the Integrated E-Learning Sustainability Governance Framework (IELSGF). The IELSGF synthesises the governance functions identified across the selected policy corpus and conceptually connects strategic direction, quality assurance, digital infrastructure, innovation, human capability, and performance accountability. It is presented as an analytically derived and provisional conceptual framework that distinguishes empirically observed policy anchors from proposed integrative linkages. Accordingly, the framework provides a structured basis for policy reflection, future empirical validation, refinement, and comparative application rather than a definitive representation of the existing Saudi governance system.

The remainder of the article is structured as follows. Section 2 develops the theoretical foundations and reviews the relevant literature, including the conceptual derivation of the six governance dimensions. Section 3 presents the methodology, including document selection, coding procedures, cross-document synthesis, and analytical rigour. Section 4 reports the findings, including the governance-alignment analysis, emergent themes, and the derived IELSGF. Section 5 discusses the findings and their theoretical and policy implications and considers the limitations of documentary inference. Section 6 concludes by summarising the study’s contributions, limitations, and priorities for future empirical validation.

2 Theoretical and literature review

2.1 Sustainable higher education and multi-level governance

Sustainable higher education increasingly depends on governance arrangements that connect strategic priorities, institutional capabilities, digital transformation, human-capability development, and mechanisms of accountability. Universities are not merely sites of teaching and research; they are complex organisations operating within national regulatory, technological, and development systems. Sustainability governance in higher education therefore concerns not only the existence of sustainability-related objectives but also the institutional mechanisms through which such objectives are translated into standards, infrastructures, professional capabilities, organisational practises, and measurable outcomes.

The Saudi higher-education literature illustrates this distinction between formal policy commitment and institutional enactment. A growing body of research reports substantial progress in digitalisation and governance alongside continuing differences in institutional capacity, implementation quality, leadership, and accountability. For example, Ibrahim et al. (2021) and Ibrahim et al. (2022), examining the University of Ha’il from student and faculty perspectives, reported strong associations between e-governance and institutional excellence. Their findings indicate that infrastructure, transparency, accountability, participation, and empowerment are important components of digitally enabled institutional performance. Across the two studies, e-governance dimensions explained approximately 78.9–80.9% of the variance in institutional excellence, with infrastructure emerging as one of the strongest dimensions. These findings suggest that governance performance involves interacting organisational and technological conditions rather than technology adoption alone.

Related evidence comes from Elmoustafa (2025), who examined e-governance applications at Najran University and reported strong perceived effects of Learning Management Systems, human-resource systems, financial systems, and student portals on administrative processes aligned with Vision 2030. Similarly, national-level research on digital-transformation governance found high levels of practise across technological, administrative, resource, and accountability dimensions, although accountability ranked lower than several operational dimensions.

These studies reinforce an institutional-governance perspective in which national policy provides strategic direction whilst institutional outcomes depend on the interaction amongst regulation, organisational structures, technological systems, leadership, and performance mechanisms.

2.2 Institutional and practise-based perspectives

Institutional perspectives are particularly useful for examining Saudi e-learning governance because higher education institutions operate within overlapping national policy, regulatory, professional, and organisational environments. National strategies and standards establish formal expectations, whilst universities retain varying degrees of discretion in translating those expectations into institutional practises.

This distinction has become increasingly visible following reforms associated with Vision 2030. Alyami (2025), in a qualitative case study of governance, curriculum, and technology alignment, found that formal decentralisation did not automatically eliminate hierarchical organisational practises. The study illustrates how structural reforms can coexist with informal decision-making norms that continue to shape implementation. Likewise, Shoaib (2022), studying 350 faculty and staff across Saudi universities, reported an association between organisational culture and digital transformation and identified an indirect pathway through digital transformation linking organisational culture with job satisfaction.

Leadership research provides similar evidence. Kurdi (2026) reported associations amongst digital leadership, digital transformation, and employee engagement in universities in the Makkah region, with digital transformation identified as an intervening factor in the model. Shah et al. (2026), using structural equation modelling across nine Saudi universities, similarly found that digital proficiency, organisational support for innovation, entrepreneurial orientation, and AI adoption were associated with employee creativity, whilst transformational leadership was identified as an important contextual factor linking organisational and technological behaviours with performance outcomes.

These findings support a practise-oriented interpretation of governance. Formal policy and technological infrastructure establish enabling conditions, but institutional outcomes are shaped by leadership, organisational culture, professional capabilities, and implementation practises. Consequently, the present study does not treat policy alignment as equivalent to implementation success. Rather, it examines the formal architecture within which institutional practises subsequently occur.

2.3 Digital infrastructure and transformation capacity

Digital infrastructure represents one of the most persistent themes in Saudi e-learning research. Earlier studies by Naveed et al. (2017) and Al Gamdi and Samarji (2016) identified infrastructure, technology, hardware, software, and internet access amongst the principal barriers to e-learning implementation. Later studies continued to identify connectivity and resource inequalities, indicating that infrastructure development remains uneven despite substantial national investment.

At the institutional level, King Abdulaziz University demonstrated relatively strong readiness during the COVID-19 transition because of pre-existing LMS infrastructure and a dedicated e-learning governance structure. By contrast, institutions with weaker baseline readiness experienced greater difficulties. The pandemic therefore functioned as both an accelerant of digital adoption and a stress test of existing governance capacity.

More recent research has moved beyond simple adoption towards integrated transformation frameworks. Alshehri et al. (2025) developed the DXA-SPU framework for Saudi public universities, combining the Technology Acceptance Model and Technology–Organisation–Environment perspective. Their analysis of 447 participants reported associations between digital-transformation adoption and institutional performance, administrative efficiency, technical infrastructure, and teaching effectiveness. Perceived usefulness and institutional skills gaps emerged as particularly influential factors. Alsulami et al. (2024) similarly developed the DTA-SAU framework to address Saudi-specific digital-transformation barriers.

Enterprise architecture has also emerged as a governance mechanism. Alghamdi (2024) documented Al-Baha University’s implementation of enterprise architecture according to national Digital Government Authority standards. The initiative demonstrated the potential for structured digital alignment whilst simultaneously revealing challenges relating to IT-policy alignment, specialist expertise, legacy-system integration, and senior-management engagement.

Taken together, this literature shows that digital infrastructure is an essential enabling condition but cannot itself be treated as evidence of effective governance or sustainability. Its value depends on institutional integration, interoperability, organisational capability, and accountability.

2.4 Quality assurance, accountability, and data governance

Quality assurance constitutes another central component of Saudi e-learning governance. Alqahtani (2025), examining the adoption of Quality Matters standards at King Khalid University, showed how international quality frameworks were translated, contextualised, and incorporated into faculty development and e-course design. The study illustrates that effective quality governance requires institutional adaptation rather than simple formal adoption of external standards.

Abdel-Haq and Asfoura (2021) similarly proposed the V-E-learning model at Dar Al-Uloom University, combining national e-learning standards with international Quality Matters principles and embedding performance indicators across stages of e-course development. Al-Otaibi and Albaroudi (2023), reviewing 36 studies on digital quality management in Saudi universities, nevertheless concluded that digitally oriented quality-management systems remained comparatively underdeveloped and required further institutional capacity building.

Alghamdi et al. (2026) provide further evidence of variation in quality-assurance implementation. Their mixed-methods study across four regions found that quality assurance was prominent in strategic planning and leadership discourse, but institutional integration differed between established and newer institutions. One university demonstrated stronger integration of quality principles into strategic planning, accreditation, and faculty development, whereas another displayed a more procedural compliance orientation.

Digital governance also increasingly involves data and information integrity. Omar and Almaghthawi (2020), examining data governance across nine Saudi universities, identified substantial challenges associated with unstructured and underutilised institutional data and recommended dedicated governance teams, internal audits, clear priorities, training, and regular assessment of data-governance plans. Al-Momani et al. (2026) similarly found that training, policy clarity, institutional support, and IT-security culture were positively associated with compliance behaviour, with training and policy clarity showing particularly strong effects.

Cybersecurity research reinforces the same governance principle. Studies of Saudi higher education report that strong technical controls can coexist with weaknesses in human-factor management, organisational awareness, and security culture. Such findings demonstrate that quality, accountability, and information governance require both technical controls and institutional capabilities.

2.5 Human capital, leadership, and organisational readiness

Human-capability development is consistently identified as a critical bridge between digital infrastructure and meaningful organisational transformation. Alhabeeb and Rowley (2017) identified instructor and student knowledge of learning technologies amongst the most important critical success factors for e-learning. Naveed et al. (2017) likewise found that technological factors were important but operated alongside human readiness and organisational support.

The COVID-19 period further demonstrated the interaction between infrastructure and human capacity. Qasem et al. (2021), studying King Abdulaziz University, reported relatively high levels of human-resource and content readiness but lower scores for standards and management, alongside continuing problems relating to internet connectivity and access to devices. Subsequent analyses argued that post-pandemic governance should institutionalise blended learning, digital assessment, staff development, and participatory practises rather than treat emergency digitalisation as a temporary response. Sharadgah and Sa’di (2022) and Sa’di et al. (2022), in particular, highlighted the need to address persistent faculty skill gaps, resistance to change, student motivation, and limited digital experience.

Human capability therefore operates at several levels: individual digital competence, faculty development, leadership capability, institutional learning, and organisational readiness. This literature indicates that successful digital governance requires the alignment of technical systems with the skills and organisational conditions required to use them effectively.

2.6 Artificial intelligence and emerging technology governance

Artificial intelligence has recently become an important component of Saudi higher-education transformation. Alqarawi and Alnamlah (2025), drawing on 400 participants across 68 higher education institutions, found positive associations between AI adoption and teaching efficiency, personalised learning, student engagement, and sustainable-learning outcomes, whilst also demonstrating the moderating role of institutional ICT infrastructure.

Khan and Alshalaan (2025) proposed an AI-blockchain framework for academic accreditation that combined automated quality assessment, secure credential verification, and real-time analytics. Although the framework reported substantial improvements in processing speed and data accuracy, implementation was constrained by legacy systems, skills shortages, and resistance to change.

Sheikhidries (2025) likewise reported associations between AI integration, strategic management, and educational quality, whilst research on emerging immersive technologies at King Saud University identified gaps between innovation ambitions and formal institutional strategies for next-generation learning environments.

These studies reinforce the need to treat AI as a governance object rather than merely as a technological tool. Responsible adoption requires strategic alignment, quality assurance, risk governance, human oversight, workforce development, data management, and performance monitoring. The connection between AI adoption and sustainability should therefore be treated as proposed and empirically contingent rather than assumed from technological deployment alone.

2.7 From the literature to the six governance dimensions

The reviewed literature suggests that e-learning governance in Saudi higher education is best understood as an interconnected system rather than as a single technological or regulatory process. Existing studies repeatedly identify six governance functions.

Strategic alignment (G1) reflects the role of national transformation objectives, institutional strategy, leadership priorities, and Vision 2030 alignment in shaping digital transformation. Studies such as Alyami (2025), Alghamdi (2024), and Alshehri et al. (2025) demonstrate that technological initiatives are embedded within wider strategic and organisational environments.

Quality assurance and governance (G2) is supported by work on national standards, Quality Matters, digital-quality systems, accreditation, information governance, and institutional controls, including Alqahtani (2025), Abdel-Haq and Asfoura (2021), Al-Otaibi and Albaroudi (2023), and Alghamdi et al. (2026).

Digital infrastructure and accessibility (G3) derives from the persistent importance of infrastructure, connectivity, interoperability, enterprise architecture, and digital readiness identified by Naveed et al. (2017), Al Gamdi and Samarji (2016), Alshehri et al. (2025), Alsulami et al. (2024), and Alghamdi (2024).

Innovation and adaptive learning (G4) reflects the growing governance significance of AI, adaptive learning, blockchain, emerging platforms, and data-intensive technologies documented by Alqarawi and Alnamlah (2025), Khan and Alshalaan (2025), Sheikhidries (2025), and related emerging-technology studies.

Human capital and learning outcomes (G5) follows from the consistent emphasis on digital competence, faculty development, leadership, organisational culture, readiness, participation, and capability development in the work of Alhabeeb and Rowley (2017), Shah et al. (2026), Kurdi (2026), Shoaib (2022), and others.

Finally, accountability and performance measurement (G6) reflects the literature on institutional excellence, KPIs, benchmarking, compliance, quality assessment, audit, data governance, and performance reporting. The University of Ha’il e-governance studies, the quality-assurance literature, and recent smart-governance studies demonstrate that performance measurement is a core component of digital governance rather than a peripheral administrative activity. Smart-governance research, for example, has linked governance to institutional sustainability through institutional performance, whilst national digital-governance research has simultaneously identified accountability as a comparatively less prominent dimension than operational technologies and administrative authority.

The six dimensions therefore represent a theoretically and empirically informed synthesis of recurring governance functions identified across the literature. They are not treated as six independent causal determinants of sustainability. Rather, they provide a deductive analytical structure for examining how strategic, regulatory, technological, innovative, human-capability, and accountability functions are represented across the selected Saudi policy instruments.

The literature further suggests that technological and governance perspectives should be understood as complementary rather than competing. Infrastructure and digital capability create enabling conditions, whilst leadership, organisational culture, regulatory mechanisms, human capabilities, and accountability influence how those conditions are institutionalised. Evidence across Saudi universities repeatedly indicates that policy adoption and technical capacity alone do not determine institutional transformation; their institutional implications depend partly on organisational and human factors.

This synthesis provides the conceptual foundation for the subsequent qualitative policy analysis. The study therefore uses G1–G6 as a frozen deductive coding framework whilst allowing additional cross-cutting themes to emerge during cross-document synthesis. The later Integrated E-Learning Sustainability Governance Framework (IELSGF) is consequently distinguished from the initial coding framework: G1–G6 provide the theoretically grounded analytical lens, whereas the IELSGF represents the subsequent synthesis of relationships observed across the selected policy corpus.

Taken together, Table 1 establishes the conceptual chain from theoretical perspective to governance construct and analytical category. The six dimensions were therefore not generated retrospectively from the policy documents; rather, they constituted the deductive framework used to structure the initial analysis. Cross-document thematic synthesis subsequently examined how these dimensions interacted across the selected policy instruments and allowed additional themes to emerge. This distinction separates the theoretically grounded coding framework (G1–G6) from the IELSGF, which represents a later, author-constructed synthesis of the relationships identified through the analysis and remains provisional pending empirical validation.

Table 1. Theoretical foundations of the six-dimensional analytical framework.

The six dimensions constitute deductive analytical categories rather than independent causal variables or empirically validated determinants of sustainability. Their theoretical grounding informed the coding framework applied to the selected policy corpus. The subsequent IELSGF is analytically distinct: it synthesises cross-document relationships and emergent themes identified through the analysis.

3 Materials and methods

3.1 Research design

This study employed a qualitative integrated policy analysis (IPA) design, combining directed qualitative content analysis (Hsieh and Shannon, 2005) with cross-document thematic synthesis (Thomas and Harden, 2008) to examine how three Saudi national frameworks jointly govern e-learning innovations for sustainable higher education. IPA is a document-based, non-interventional design in which a small, purposively selected set of authoritative policy instruments is read as a coherent governance system rather than in isolation (Bowen, 2009; Cardno, 2018). It is well suited to research questions in which the object of inquiry is the policy architecture itself—how vision, standards, and delivery mechanisms interlock—rather than the perceptions of individual actors or the observed behaviour of institutions.

The design was chosen for three reasons. First, the study’s research question—how do national frameworks translate innovations in e-learning into governance arrangements that advance sustainable higher education?—is answerable only at the policy layer, where the frameworks themselves define what is required, measured, and enforced. Second, prior empirical work on Saudi higher education sustainability has been dominated by single-institution case studies and faculty perception surveys (Alshammari, 2025; Bataeineh and Aga, 2022; Alhusaini, 2025), leaving the cross-framework level of analysis under-examined. Third, an IPA design keeps the study fully reproducible from publicly available materials, requiring neither human participants nor institutional access.

No interventions were conducted; no human or animal participants were involved; and all materials analysed are publicly available government publications. Consequently, no ethical approval was required.

3.2 Document sample

The documentary corpus was constructed using purposive, criterion-based sampling (Patton, 2015). The objective was not to compile an exhaustive inventory of Saudi education-policy documents, but to identify a focused set of authoritative national instruments that collectively represent the strategic, regulatory, implementation, and performance-reporting functions relevant to e-learning governance in higher education. Documents were included when they satisfied all of the following criteria:

• (C1) Authoritative status. Issued by a Saudi national authority or Vision Realisation Program with a formal mandate relevant to education, e-learning regulation, quality assurance, digital learning, or human-capability development.
• (C2) Substantive governance relevance. Contained substantive policy provisions, regulatory requirements, implementation evidence, or performance information directly relevant to national e-learning, digital higher education, educational innovation, or human-capability development. This criterion was applied independently of the six analytical dimensions subsequently used for coding, thereby separating corpus selection from analytical classification.
• (C3) Vision 2030 alignment. Demonstrated an explicit institutional or programmatic relationship to Saudi Vision 2030, its objectives, or one of its Vision Realisation Programs.
• (C4) Temporal relevance and currency. Governed, regulated, or reported developments relevant to the study period whilst representing the authoritative version available for the analytical purpose for which it was included. Where a document reported performance for a completed year but was published subsequently, the reporting period and publication or version date were distinguished explicitly. Similarly, subsequently updated regulatory material was not assumed retrospectively to have governed the entire observation period.
• (C5) Public availability and version traceability. Publicly accessible through an official government source in a form that permitted the specific version used for coding to be identified, recorded, and retained for reproducibility.

Candidate instruments were identified from official Saudi government and national-policy sources relevant to education, digital learning, quality assurance, Vision 2030 implementation, and human-capability development. The screening process focused on national-level strategic documents, regulatory and quality-assurance instruments, and implementation or performance-reporting documents with substantive relevance to e-learning and higher education. Candidate documents were assessed against criteria C1–C5 before application of the six-dimensional analytical coding framework. This separation between document selection and analytical classification was adopted to reduce the risk of circularity in corpus construction.

Application of these criteria yielded a three-document corpus (Table 2). The selected documents perform complementary rather than interchangeable governance functions. D1 represents the strategic layer through the Ministry of Education’s long-term education strategy; D2 represents the national regulatory, quality-assurance, and digital-learning performance layer through the National eLearning Center (NeLC) E-Learning Standards; and D3 represents the implementation and performance-reporting layer through the Human Capability Development Program (HCDP) Annual Report 2025. This layered design enabled comparison across strategic direction, regulatory expectations, institutional performance requirements, and reported implementation and outcome evidence.

Table 2. Fixed documentary corpus and version-control information.

*The NeLC standards are maintained as an online regulatory document. The 41-page count refers to the fixed PDF copy archived from the official webpage for the purposes of this study.

The corpus was therefore designed for analytical depth and cross-layer comparison rather than documentary exhaustiveness. The resulting Integrated E-Learning Sustainability Governance Framework (IELSGF) should accordingly be interpreted as a conceptual framework derived from this bounded policy corpus and as a basis for subsequent empirical validation, rather than as a definitive representation of the entire Saudi national policy ecosystem. Other Vision 2030 initiatives and national policy instruments were considered during corpus construction but were not retained when they did not satisfy one or more of the predefined eligibility criteria or did not perform a sufficiently direct national governance function in relation to the study’s research question.

The Education and Training Evaluation Commission (ETEC), including the National Center for Academic Accreditation and Evaluation (NCAAA), was considered because of its important role in Saudi higher-education quality assurance. Its accreditation instruments, however, primarily establish institutional and programme-level quality and evaluation requirements rather than serving as one of the core national instruments representing the strategy–digital-learning regulation–implementation structure examined in this study. These instruments were therefore treated as part of the broader policy environment rather than included in the three-document analytical corpus.

To prevent version drift and enhance reproducibility, the analytical corpus was frozen at the document-version level. For each document, the exact title, issuing authority, policy function, temporal coverage, version used for analysis, source language, and document length were recorded. The versions identified in Table 2 constitute the fixed document-level corpus used for segmentation, coding, and cross-document comparison. During revision, a source audit confirmed the identity of D1 as the General Education Strategic Plan 2020–2030 and distinguished the historical analytical unit record from directly source-verified excerpts. Accordingly, Supplementary File S2 preserves the stable D1 analytical record whilst explicitly identifying unit-level records that require source reconstruction; no unverified analytical rendering is presented as a verbatim source quotation. Local archival copies of all three document versions were retained as part of the study audit trail, and subsequent amendments to the source documents were not incorporated into the coded dataset.

Particular care was taken with D2 because the NeLC E-Learning Standards are maintained as an online regulatory instrument subject to periodic revision. The version used in the revised analysis displays a last-update date of 29 June 2026. Accordingly, requirements appearing in this version were not treated retrospectively as having governed the entire study period unless their prior applicability could be independently established. Instead, the document was used as a contemporaneous national regulatory benchmark for interpreting the direction and current architecture of e-learning governance. This temporal distinction was maintained throughout the analysis when comparing regulatory expectations with implementation evidence.

Arabic-language documents (D1 and D3) were coded in their original Arabic by the author to preserve terminological and contextual fidelity. Relevant coded passages were subsequently translated into English by the author for reporting purposes. D2 was coded using the fixed English-language version retained for the study. Details of the coding procedure, translation process, and analytical consistency cheques are provided in §2.5.

3.3 Analytical framework

Coding proceeded against a six-dimension governance framework (Table 3) developed a priori through theoretical synthesis rather than derived from the policy corpus itself. The framework combined three complementary theoretical and empirical strands: (i) Alshuwaikhat’s five-component framework for higher-education sustainability assessment, encompassing education, research, campus operations, community outreach, and financial management (Alshuwaikhat et al., 2016); (ii) the ESG-governance model applied to Saudi HEIs by Alhusaini (2025), which foregrounds institutional governance, accountability, performance monitoring, and sustainability-related management; and (iii) research on technology and AI governance in Middle Eastern higher education, which highlights digital infrastructure, technological innovation, institutional capacity, and responsible technology adoption (Khan et al., 2025; Ronaghi and Ronaghi, 2025).

Table 3. Six-dimension analytical framework and deductive coding criteria.

These theoretical domains were translated into six governance functions relevant to national e-learning policy. G1 (Strategy & Vision) captures long-term strategic direction and policy alignment, drawing primarily on the strategic and institutional dimensions of sustainability and governance frameworks (Alshuwaikhat et al., 2016; Alhusaini, 2025). G2 (Quality Assurance & Standards) operationalises the regulatory, compliance, and quality-control functions emphasised in institutional and ESG governance (Alshuwaikhat et al., 2016; Alhusaini, 2025). G3 (Digital Infrastructure & Platforms) represents the technological enabling conditions required for scalable and resilient digital education (Khan et al., 2025; Ronaghi and Ronaghi, 2025). G4 (Innovation: AI & Adaptive Learning) captures the governance and institutional adoption of emerging educational technologies, including AI, adaptive learning, analytics, and responsible innovation (Khan et al., 2025; Ronaghi and Ronaghi, 2025). G5 (Human Capital & Capacity Building) integrates the educational and institutional-capacity components of sustainability frameworks with the skills, professional-development, and capability requirements associated with digital transformation (Alshuwaikhat et al., 2016; Khan et al., 2025; Ronaghi and Ronaghi, 2025). G6 (Accountability & Performance) captures monitoring, reporting, performance measurement, oversight, and accountability mechanisms derived principally from governance and ESG perspectives (Alhusaini, 2025), whilst allowing examination of how digital-learning performance is connected to broader strategic and sustainability-oriented objectives.

Together, G1–G6 operationalise “e-learning governance for sustainability” as an analytical construct spanning strategic direction, regulatory quality, enabling infrastructure, innovation governance, human capability, and performance accountability. The dimensions were employed as deductive coding categories rather than as predetermined measures of policy quality, governance effectiveness, or realised sustainability performance. Accordingly, the presence or frequency of a code was not interpreted as evidence of the relative importance or effectiveness of that governance dimension. Sustainability-related relationships identified through the framework were treated as proposed policy linkages unless corresponding outcomes were explicitly specified in the source documents.

The framework was therefore analytically derived and applied deductively, whilst remaining open to inductively identified patterns that extended beyond the six predefined dimensions. Evidence that did not fit the deductive categories was retained for the inductive analytical stage described in Section 2.4 rather than being forced into an existing category. This combination allowed the analysis to preserve the theoretical structure of the coding framework whilst remaining sensitive to governance relationships and cross-cutting themes emerging from the policy corpus.

3.4 Coding procedure

Coding followed a five-step protocol adapted from directed qualitative content analysis (Hsieh and Shannon, 2005) and framework analysis (Gale et al., 2013). The procedure combined deductive coding based on the six-dimensional analytical framework with an inductive stage designed to identify cross-cutting governance patterns not adequately captured by the predefined categories. Throughout the analysis, traceability was maintained between each analytical interpretation and its corresponding source text.

3.4.1 Step 1—familiarisation and preparation

Each document was read in full at least twice to establish familiarity with its scope, structure, terminology, governance function, and policy context. The source documents were coded and analysed in their original languages: the Arabic-language policy documents (D1 and D3) were analysed directly in Arabic, whilst the English-language document (D2) was analysed in English. No translated versions were used as the basis for coding or analytical interpretation. Where selected Arabic passages required presentation in English in the manuscript or Supplementary material, the English rendering was prepared by the sole author for reporting purposes only and was checked against the original Arabic text to preserve the substantive meaning of the source provision. Translation therefore did not constitute a separate stage of the coding or analytical procedure. Particular attention was given to differences in document genre and function so that strategic statements, regulatory requirements, and implementation- or performance-reporting provisions were interpreted within their respective policy contexts rather than treated as functionally equivalent.

Accordingly, the absence of a governance theme from a particular document was not interpreted in itself as evidence of policy failure, fragmentation, or incoherence. Cross-document comparison distinguished amongst (a) expected functional differentiation attributable to document genre, institutional mandate, and policy purpose; (b) substantive convergence or complementarity where different instruments addressed related governance functions from their respective positions; and (c) limited explicit cross-framework integration where relationships that could reasonably connect strategic, regulatory, implementation, or performance-reporting functions were not specified in the selected corpus. This distinction was maintained throughout the interpretation of the findings.

Each document was then segmented into discrete meaning units. A meaning unit was operationally defined as a self-contained span of policy text expressing a single governance-relevant idea. In practise, this typically corresponded to a sentence, numbered clause, or bullet point, but could extend to a short paragraph when two or more sentences were necessary to preserve the meaning of a single policy proposition, such as an objective together with its associated indicator. Bullet lists were coded item by item unless the parent stem was required to make an individual bullet interpretable. Each meaning unit was assigned a stable identifier and recorded in Supplementary File S2 together with its source locator, source-text reference and analytical rendering, English translation where required for reporting, primary and secondary governance codes, emergent-theme coding, analytical notes, evidence-status fields, and reliability-related coding records where applicable.

3.4.2 Step 2—deductive coding and pilot calibration

Coding was guided by the six predefined governance dimensions presented in Table 3 and operationalised in a detailed codebook containing definitions, inclusion and exclusion criteria, worked examples, boundary rules, and decision rules for multi-code cases. The six dimensions were deductively derived from the literature and specified before full-corpus coding.

Before full-corpus coding, an approximately 10% random pilot subsample (n = 41 meaning units), drawn across the three documents, was independently coded by the sole author and the independent second coder. Cohen’s κ was calculated for the primary governance-dimension code. The pilot was used to identify ambiguous category boundaries and to refine the operational definitions, inclusion and exclusion criteria, and decision rules for potentially overlapping governance functions. Following pilot calibration, the codebook was finalised and frozen before full-corpus coding and the final reliability assessment. The pilot procedure and resulting codebook refinements are documented in Supplementary File S1.

Following pilot calibration and finalisation of the codebook, the full corpus of 412 unique meaning units was coded by the sole author using the final coding framework. Each unit received exactly one primary governance-dimension code (G1–G6), representing its dominant governance function. A secondary governance-dimension code could additionally be assigned when the same unit substantively addressed another governance function. Thus, the total of 412 reported in the descriptive coding results represents the number of unique meaning units and the distribution of their primary codes rather than the combined number of primary and secondary code assignments. Secondary codes were retained separately to preserve multidimensional governance relationships during cross-document interpretation and were not added to primary-code frequency totals. This primary/secondary coding rule was specified in the final codebook and applied consistently throughout the corpus. Independent double coding for the final reliability assessment was conducted on the 62-unit subsample described below.

Coding was recorded in a structured spreadsheet containing the document identifier, source locator, stable meaning-unit identifier, source-text reference and analytical rendering, English translation where required for reporting, primary code, secondary code where applicable, emergent-theme coding, analytical notes, evidence-status fields, and reliability-related coding fields where applicable. This structure provided an auditable link between the source material, categorical coding, reliability assessment, cross-document synthesis, and subsequent interpretation.

3.4.3 Step 3—inductive extension

Meaning units containing substantively relevant governance content that could not be adequately represented by the six predefined dimensions were provisionally marked for inductive review rather than being forced into an existing deductive category. Following the first full coding pass, recurrent cross-cutting patterns were examined across documents and formalised as emergent themes. These themes were recorded separately from the primary G1–G6 codes and were subsequently applied consistently during a second analytical pass.

A candidate emergent theme was retained only when it was supported by identifiable source passages from more than one policy document, represented a concept or relationship not adequately captured by a single deductive dimension, and demonstrated sufficient cross-document analytical relevance. Potentially contradictory or disconfirming evidence was retained when determining the scope and interpretation of each theme. The resulting themes therefore supplemented rather than replaced the deductive governance framework.

3.4.4 Step 4—cross-document thematic synthesis

Coded meaning units were synthesised across the three documents according to the six governance dimensions to construct a governance-alignment matrix (six dimensions × three documents). For each matrix cell, the analysis recorded: (a) the presence or absence of relevant policy provisions; (b) their policy function and normative strength, including whether provisions were aspirational, regulatory, implementation-oriented, or performance-reporting in character; (c) explicitly stated KPIs or measurable targets; (d) explicit linkages to Saudi Vision 2030, the Sustainable Development Goals, or other stated national outcomes; and (e) identifiable governance, implementation, monitoring, reporting, or accountability mechanisms.

The evidentiary basis for the document-level classifications reported in Table 4 is documented in Supplementary File S2 (Table 4—Evidence sheet), which links each matrix-level classification to identifiable coded meaning units and their corresponding source locators. This evidence mapping was used to preserve traceability between the cross-document synthesis and the underlying coded corpus.

Table 4. Distribution of coded meaning units by document and primary governance dimension.

Cross-document interpretation took account of differences in document purpose, genre, length, and institutional mandate. Consequently, the number of meaning units assigned to a dimension was treated as a descriptive characteristic of the coded corpus rather than as a measure of governance importance, policy strength, implementation effectiveness, or sustainability performance. Likewise, complementarity was identified only where the substantive functions of provisions across documents could be meaningfully related, rather than inferred solely from the parallel presence of codes. Patterns of convergence, complementarity, divergence, incomplete linkage, and policy silence were then examined across the matrix.

3.4.5 Step 5—framework development

Building on the governance-alignment matrix and the inductively identified cross-cutting themes, the findings were synthesised into the Integrated E-Learning Sustainability Governance Framework (IELSGF). The IELSGF was developed as an analytically derived conceptual framework representing relationships observed or proposed on the basis of the selected policy corpus. It does not constitute an empirically validated explanatory model of Saudi Arabia’s national e-learning governance system. Relationships directly supported by the analysed documents were distinguished analytically from integrative relationships proposed through synthesis, with the latter treated as policy-level propositions requiring subsequent empirical investigation. The framework therefore provides a structured basis for future refinement, validation, and comparative application rather than evidence of causal relationships or realised sustainability impacts.

3.4.5.1 Inter-coder reliability

Final inter-coder reliability was assessed on a 15% subsample of the full coded corpus (n = 62 of 412 meaning units; 15.05%). The reliability sample was randomly selected from the complete list of stable meaning-unit identifiers using the randomisation procedure documented in Supplementary File S2. The selected identifiers were fixed before independent reliability coding and retained in S2 to permit verification of sample membership and reproduction of the reliability analysis.

The sole author and the independent second coder independently assigned primary G1–G6 codes to the 62 selected meaning units, without access to one another’s classifications and prior to any discussion or reconciliation. Their independent pre-reconciliation classifications are reported at the unit level in Supplementary File S2. Agreement was obtained for 53 of the 62 units, with disagreements on 9 units, yielding an observed agreement (Po) of 53/62 = 0.8548 (85.48%). Expected agreement (Pe), calculated from the marginal distributions of the two coders’ classifications, was 0.1860. Cohen’s κ was therefore calculated as κ = (Po − Pe)/(1 − Pe) = 0.8217 (reported as 0.82), exceeding the a priori acceptability threshold of κ ≥ 0.75. A bootstrap 95% confidence interval of 0.703–0.920 was obtained from 100,000 pair-resamples (computational seed = 20,260,827). The contingency matrix, marginal distributions, unit-level pre-reconciliation classifications, and full reliability calculation are provided in Supplementary File S2.

Following calculation of reliability, the nine disagreements were retained as part of the pre-reconciliation reliability record and were used to characterise the extent and pattern of coder disagreement. The codebook had been finalised and frozen before the final reliability assessment and was not modified on the basis of these disagreements. Accordingly, the reported agreement statistics and Cohen’s κ are based exclusively on the independent classifications recorded before any subsequent interpretive consideration of discrepant cases. The reliability exercise was used to assess the reproducibility of the primary G1–G6 coding framework rather than to introduce post hoc modifications to the codebook or to imply independent double coding of the full corpus.

The sole author conducted corpus preparation, full-corpus coding, cross-document analysis, substantive interpretation, preparation of English renderings of selected Arabic passages for reporting purposes, development of the IELSGF, and manuscript preparation. An independent second coder, who was not an author of the manuscript, participated exclusively in the pilot calibration and inter-coder reliability assessments. Both the author and the independent second coder are native Arabic speakers with familiarity in qualitative policy analysis and the Saudi higher-education policy context. During the reliability exercises, primary G1–G6 classifications were assigned independently, and neither coder had access to the other’s classifications before the respective coding records had been completed and locked. The second coder did not participate in full-corpus coding, preparation of English renderings, cross-document synthesis, substantive interpretation of the findings, development of the IELSGF, or manuscript preparation. The second coder’s contribution was therefore limited to methodological calibration and independent reliability assessment and did not extend to the substantive intellectual contributions underlying the manuscript. This division of responsibilities preserves the sole-author status of the study whilst transparently documenting the independent coder’s methodological role.

Supplementary File S2 provides the coded-corpus record and supporting audit trail, including stable meaning-unit identifiers, source locators, source-text references and analytical renderings, English translations where required for reporting, primary and secondary governance codes, emergent-theme coding, analytical notes, evidence-status fields, and the evidence mapping supporting the document-level classifications reported in Table 4. Its Reliability sheet additionally provides the final reliability-sample membership, both coders’ independent classifications used for the reliability assessment, agreement status, contingency matrix, observed and expected agreement, Cohen’s κ calculation, and the corresponding 95% confidence interval. Supplementary File S1 documents the final codebook, coding and decision rules, coder instructions and roles, pilot-calibration procedure, codebook change history, and reproducibility guidance.

3.5 Trustworthiness

Rigour was addressed against the four Lincoln and Guba criteria (Lincoln and Guba, 1985):

3.5.1 Credibility

Credibility was strengthened through repeated engagement with each document, independent coding of the designated pilot and final reliability subsamples by the sole author and an independent second coder, both native Arabic speakers familiar with qualitative policy analysis, and systematic cross-document comparison during the interpretation of patterns. The second coder’s role was limited to pilot calibration and inter-coder reliability assessment and did not extend to full-corpus coding, substantive interpretation, framework development, or manuscript preparation.

3.5.2 Transferability

Transferability was supported through detailed description of the document corpus, document functions, coding rules, and analytical framework, enabling other researchers to apply the same protocol to different Vision 2030 programmes or comparable regional reform contexts.

3.5.3 Dependability

Dependability was supported through a version-controlled audit trail comprising the codebook, meaning-unit spreadsheet, governance-alignment matrix, and supporting analytical records provided in the Supplementary material accompanying this article.

3.5.4 Confirmability

Confirmability was supported through a source-linked audit trail connecting coded meaning units to their source documents, analytical classifications, cross-document synthesis, and reported findings. Supplementary File S2 distinguishes analytical renderings from directly source-verified excerpts and records the verification status of the underlying evidence. Where verbatim excerpts are reported, they are traceable to the corresponding primary source. Records requiring further source reconstruction are explicitly identified rather than presented as source-verified quotations.

3.6 Ethical considerations

This study analysed exclusively publicly available policy documents issued by Saudi national authorities. It did not involve human participants, animals, personal data, or identifiable information, and no institutional access was required. Accordingly, ethical review and approval were not required under the journal’s research ethics policy because the study was based solely on the analysis of publicly available documentary sources.

3.7 Data availability

All primary materials analysed in this study are publicly available from the issuing authorities:

• D1. Ministry of Education, Kingdom of Saudi Arabia. Strategic Plan 2020–2030. Available from the Ministry of Education official portal.
• D2. National eLearning Center. E-Learning Standards. Available from the NeLC official portal.
• D3. Human Capability Development Program. Annual Report 2025. Available from the HCDP official portal and the Vision 2030 portal.

The de-identified coding spreadsheet, codebook, cross-document governance-alignment matrix, and supporting analytical materials generated during this study are available as Supplementary material accompanying this article. The policy documents analysed are publicly available from their respective official sources. No new empirical datasets requiring deposition in a public repository were generated; therefore, no accession numbers apply.

3.8 Software

Coding and cross-document synthesis were performed in a spreadsheet application (Microsoft Excel/Google Sheets) with document-level annotation in a PDF reader. No specialised qualitative-analysis software was required given the compact corpus; the same protocol is fully reproducible in NVivo, ATLAS.ti, or MAXQDA at the reader’s option.

4 Results

This section presents the findings of the integrated policy analysis. It is organised in three parts: (i) an overview of the coded corpus and coding reliability (§3.1); (ii) dimension-by-dimension findings across the three documents, following the six-dimension analytical framework (§3.2–§3.7); and (iii) the cross-document governance-alignment matrix (§3.8), the emergent themes not captured by the a priori framework (§3.9), and the derived Integrated E-Learning Sustainability Governance Framework—IELSGF (§3.10).

4.1 Coded corpus and coding reliability

Coding produced 412 unique meaning units across the three documents: 96 units from the Ministry of Education Strategic Plan 2020–2030 (D1), 231 units from the National eLearning Center (NeLC) E-Learning Standards (D2), and 85 units from the Human Capability Development Program (HCDP) Annual Report 2025 (D3). Each meaning unit received exactly one primary governance-dimension code (G1–G6); therefore, the frequencies reported in Table 4 represent unique meaning units classified by their primary codes and exclude secondary-code assignments. Table 4 presents the distribution of these primary codes across the six analytical dimensions.

Final inter-coder reliability was assessed on a 15% random subsample of the full coded corpus (n = 62 of 412 meaning units; 15.05%). The two coders independently assigned primary G1–G6 codes before viewing one another’s classifications and before any reconciliation. Independent pre-reconciliation coding resulted in agreement on 53 of the 62 units and disagreement on 9, yielding an observed agreement of 85.48% and Cohen’s κ = 0.82 (unrounded κ = 0.8217), exceeding the a priori acceptability threshold of κ ≥ 0.75. The nine disagreements were subsequently resolved by consensus through joint review of the relevant source passages; no third-analyst adjudication was required. The codebook was not modified following the final reliability assessment, and the full corpus was not globally recoded as a consequence of reconciliation. Supplementary File S2 provides the unit-level independent pre-reconciliation classifications, agreement and reconciliation records, contingency matrix, observed and expected agreement, Cohen’s κ calculation, and 95% confidence interval, whilst Supplementary File S1 documents the coding rules, coder instructions, pilot-calibration procedure, and codebook change history.

Table 4 provides a descriptive distribution of primary governance codes across the three documents. The coding profiles vary substantially across the corpus, consistent with differences in document purpose, genre, length, mandate, and level of operational detail. Within D1, G6 (accountability and performance) and G1 (strategy and vision) account for the largest shares of coded meaning units, whilst no D1 unit met the operational definition of G4 (innovation, AI, and adaptive learning). D2 contains comparatively larger numbers of units classified under G2 (quality assurance and standards) and G3 (digital infrastructure and platforms), consistent with its regulatory and standards-setting function. Within D3, G4 (innovation) and G5 (human capital and capacity building) account for comparatively larger shares of its coded units, consistent with its implementation- and performance-reporting function. These distributions are reported solely to describe the composition of the coded corpus and are not interpreted as measures of policy importance, governance strength, institutional priority, implementation intensity, or sustainability performance. Because the three documents differ in length, genre, mandate, and level of operational detail, substantive conclusions are based on the content and function of the coded provisions and the cross-document synthesis rather than on raw frequency comparisons.

4.2 G1—strategy and vision

All three documents anchor e-learning within an explicit sustainability and Vision 2030 frame, but with different scope.

D1 embeds education (including higher education) inside Vision 2030’s three top-level goals—vibrant society, thriving economy, and ambitious nation—and cascades these into second- and third-level objectives (e.g., “developing human capital in line with labour-market needs” and “improving the ranking of educational institutions”). E-learning is not treated as a standalone axis but as an enabler of these strategic objectives, and the ministry’s plan is presented under a unified organisational structure covering general education, higher education, and technical and vocational training.

D2 requires each provider to “provide a strategic plan for eLearning either independently or integrated into the institution’s strategy that includes the vision, mission, strategic goals, and operational plans” and to “provide a detailed implementation plan derived from the strategy, including clear performance indicators, set timelines, and relevant stakeholders.” Sustainability appears explicitly as a strategic concern (“allocate sufficient human and financial resources to implement the eLearning strategic plan and ensure its sustainability”) rather than as an environmental one.

D3 frames its work through four sequential “stations” (identity, foundation, readiness, renewed leadership) linked to sixteen third-level Vision 2030 objectives assigned to the programme, positioning higher education, technical and vocational training, and lifelong learning as strategic vehicles for global competitiveness.

4.2.1 Finding G1

The three instruments converge on Vision 2030 alignment as a strategy anchor, but only D2 obliges an institution-level e-learning strategy with measurable indicators. D1 and D3 treat e-learning as an enabler embedded in broader human-capability strategy rather than as a governed object in its own right.

4.3 G2—quality assurance and standards

Quality assurance is D2’s centre of gravity. D2 mandates policies covering data privacy, digital security, accessibility, academic integrity, attendance and participation, learner-to-educator ratios, intellectual property, communication, and AI use, and requires that “documented operational procedures … regulate the conduct of eLearning practises, and ensure their efficiency, effectiveness, and sustainability.” Alignment with the National Qualifications Framework (NQF) and labour-market requirements is required, and instructional design must follow “an approved instructional design methodology based on design models and educational theories.” Assessment must integrate diagnostic, formative, and summative types and “uphold academic integrity … whilst providing verification tools appropriate to the level of risk.”

D1 addresses quality assurance largely through system-level objectives (improving basic education outcomes; improving institutional rankings) rather than through detailed e-learning standards. D3 references quality through reported outputs—for example, the World Bank’s endorsement of Saudi improvements in “Enhancing Education in Saudi Arabia through Evaluation and Measurement,” and the publication of 17,000 school-performance reports.

4.3.1 Finding G2

Detailed, prescriptive quality-assurance requirements for e-learning exist only in D2. D1 and D3 speak to quality at the aggregate outcomes level, leaving an implicit division of labour in which NeLC governs process quality and MOE/HCDP govern outcome quality—without an explicit coordination mechanism between the two.

4.4 G3—digital infrastructure and platforms

D2 requires an “accredited learning platform” with interoperability across devices, “governance mechanisms for data access and data availability according to roles, authorities, and approved regulatory policies,” and “technical integration with the National eLearning Platform … [with] educational data shar[ing] with NeLC in accordance with the policies and requirements set by NeLC.” It further mandates identity management (multi-factor authentication, single sign-on, least-privilege access), incident-response procedures, business continuity plans, backup and disaster recovery, and minimum internet-connectivity requirements with fallback provisions.

D1 references e-government development (Objective 5.2.4) as a supporting government-effectiveness track, but does not specify e-learning infrastructure. D3 reports concrete infrastructure investments and outputs: the launch of the Education and Training Status Room as a national digital analytics platform, the “Skills Portal” as a unified national skills platform, sectoral MoUs, and the Mustaqbali programme (in partnership with the Ministry of Communications and Information Technology, Oracle, and NeLC) targeting 50,000 citizens in AI and digital skills by end-2027.

4.4.1 Finding G3

D2 defines the architecture of the national digital infrastructure (platforms, interoperability, cybersecurity), whilst D3 reports its build-out (national platforms, sectoral integrations, targeted training pipelines). D1 provides only a light strategic reference. The three documents are complementary but not connected by an explicit oversight or reporting loop tying infrastructure build-out back to NeLC-governed standards.

4.5 G4—innovation (AI and adaptive learning)

Innovation, and specifically AI, is the most striking area of convergence.

D2 requires each institution to “establish an approved framework that defines the scope of the permitted and prohibited AI practises in eLearning, the criteria for approving AI tools, and the mechanisms for reviewing them periodically,” to assess AI risks before and during use, to document accreditation decisions and incidents, and to “adhere to the national frameworks and requirements regulating the use of AI issued by the competent authorities.” It embeds AI in instructional design (“ensure that AI utilisation is aligned with the learning outcomes and with AI governance frameworks”), assessment (“design assessments assuming AI tools are readily available”), and analytics (“monitor the impact of AI-driven analytics on equity and implement corrections whenever bias or deficiencies are detected”). It also mandates innovation governance at the institutional level, requiring “an institutional framework for innovation in eLearning that defines roles, responsibilities, and governance mechanisms, and links innovative initiatives to strategic priorities.”

D3 reports that 79% of Saudi universities have adopted courses in technology and AI, with more than 30 universities designating AI as a mandatory course; 750 + trainees graduated from AI training camps and hackathons (including AgentX in agentic AI); 22 specialised AI programmes and diplomas were launched; 400,000 + beneficiaries were reached through seven national digital-capability initiatives; and 90 + short university programmes were built under the MicroX initiative in partnership with Saudi universities and employers, including the first stackable-credentials digital diplomas.

D1 is comparatively silent on AI at the standards level, mentioning e-government development and innovation-culture support as objectives, but not e-learning AI specifically.

4.5.1 Finding G4

AI-in-e-learning is heavily governed at the institutional level by D2 and heavily reported at the national level by D3, but the two are not tied together by an accountability loop. D1 does not yet reflect AI as a first-class strategic object of governance.

4.6 G5—human capital and capacity building

D1 assigns human-capital development to Vision 2030 Objective 4.1 (“developing human capital in line with labour-market needs”), with sub-objectives covering integrated educational journeys, equal access, basic-education outcomes, institutional rankings, and TVET expansion. It positions higher education, general education, and TVET under a single ministerial umbrella with a shared framework.

D2 requires faculty professional development in e-learning (“establish an annual professional development plan based on educator needs assessments”), including “specialised professional development modules on the responsible use of AI in teaching,” and requires the institution to “provide sufficient and qualified human resources to lead and implement eLearning.”

D3 reports 2025 outputs against these ambitions: 28,500 scholarship students placed in the world’s top 200 universities (exceeding the 2025 target by 5,029); 39.9% localisation in high-skill jobs (against a 40% target); 40.5% adult participation in education and/or training (against a 31.1% target, up from a 5.3% 2017 baseline); 13,000 + teachers upskilled in AI-in-education; 3,800 + students identified as gifted; and 29,100 + special-needs students enrolled in educational institutions, exceeding the 2025 target. Three new international university branches (New Haven, Strathclyde, and the local University of Business and Technology, UBT) were approved.

4.6.1 Finding G5

Human-capital targets are the most quantitatively developed layer of the corpus. D3 reports concrete progress against D1’s strategic objectives, but very few of these targets are tied back to D2’s institutional e-learning quality standards; the KPI system is largely national, not institutional-e-learning-governed.

4.7 G6—accountability and performance

D2 establishes the most explicit accountability and performance architecture within the documentary corpus. It requires institutions to establish an effective e-learning governance system with clearly defined roles and responsibilities, compliance mechanisms, and processes for continuous monitoring and improvement. Institutions are also required to prepare periodic reports on the performance of the e-learning governance ecosystem and communicate relevant results to the responsible authorities.

At the institutional level, Standard 8.2, Measuring Performance, requires institutions to define key performance indicators (KPIs) for e-learning, including indicators relating to interaction, attendance, achievement, completion, assessment quality, and the effectiveness of support services. Institutions must also specify calculation methods and data sources, use learning analytics systematically, compare performance against targets and baselines, analyse differences across beneficiary groups, and use the resulting evidence to inform improvement actions. Standard 8.3 extends this performance orientation to the achievement of intended learning outcomes and the evaluation of improvement effects.

Importantly, the revised analysis identified a national-level performance mechanism that qualifies the initial interpretation of accountability fragmentation. The NeLC framework incorporates the National Digital Learning Indicator (NDLI), an annual national mechanism for assessing digital-learning performance across participating institutions, including higher education institutions. In its current form, the NDLI operates as a data-driven governance mechanism through which institutional digital-learning data are integrated with NeLC systems and used for performance assessment, benchmarking, and continuous improvement. This provides an identifiable national mechanism connecting institutional digital-learning performance data with a centralised monitoring architecture and therefore indicates a greater degree of vertical integration than was recognised in the initial analysis.

D1 operates at a different level of accountability. It establishes national strategic ambitions relating to educational performance, institutional competitiveness, human-capital development, and labour-market alignment, but does not itself specify an operational e-learning performance-measurement architecture comparable to that provided by D2. D3, in turn, reports national outcome indicators against 2025 targets, including a 39.9% localisation rate in high-skill occupations, 40.5% adult participation in education and/or training, a Human Development Index score of 0.900, international university-ranking performance, and scholarship placements. These indicators provide evidence of national human-capability and educational outcomes, but they are not presented in D3 as outputs derived directly from the institutional e-learning KPIs or the NDLI measurement architecture.

4.7.1 Finding G6

Accountability and performance governance is therefore better characterised as partially integrated rather than institutionally fragmented or lacking a national consolidation mechanism. D2 establishes both institutional e-learning performance requirements and an emerging national consolidation mechanism through the NDLI, whilst D1 defines broader strategic ambitions and D3 reports national human-capability and educational outcomes. The remaining governance issue identified in the corpus is not the absence of national digital-learning performance monitoring, but the limited explicit linkage between the NDLI’s digital-learning measurement architecture and the broader strategic, human-capability, and sustainability-oriented outcome frameworks represented in D1 and D3. The documentary evidence therefore suggests an incomplete cross-framework integration of performance and outcome measurement, rather than an absence of national accountability infrastructure.

4.8 Cross-document governance-alignment matrix

Building on the findings reported in §§3.2–3.7, Table 4 synthesises the cross-document analysis across the six governance dimensions and three policy instruments. Each cell indicates the presence (●), partial presence (◐), or absence (○) of relevant provisions or evidence. The notation in parentheses identifies the primary normative or evidentiary function of the relevant content: S = strategic/aspirational, R = required/regulatory, and O = reported outcome or implementation evidence. These classifications are used to distinguish the functional role of provisions across documents rather than to rank documents by the frequency of coded references.

First, is most evident in G1 and G5. Across their different policy functions, the three documents position education and digital transformation within the broader Vision 2030 agenda and emphasise human-capability development as a central national objective. This convergence occurs at different governance levels: D1 establishes strategic direction, D2 translates aspects of that direction into institutional requirements, and D3 reports implementation and national-level outcomes.

Second, functional complementarity characterises G2, G3, and G4. D1 primarily establishes strategic direction, D2 specifies detailed regulatory and quality-assurance requirements for institutional e-learning, and D3 documents implementation activities and reported outcomes. These differences should not be interpreted as deficiencies based solely on the presence or frequency of provisions, because the three documents perform different policy functions. Rather, their combined analysis illustrates how strategic, regulatory, and implementation instruments contribute distinct components to the wider governance architecture.

Third, G6 reveals a more differentiated pattern of partial vertical integration combined with incomplete cross-framework integration. Contrary to an interpretation of complete fragmentation, D2 provides an identifiable connection between institutional e-learning performance measurement and national-level monitoring. Standard 8.2 requires institutional e-learning KPIs, defined calculation mechanisms and data sources, learning analytics, periodic reporting, benchmarking against targets and baselines, and evidence-based improvement. In addition, the National Digital Learning Indicator (NDLI) provides a national mechanism for consolidating and assessing digital-learning performance across participating institutions. The documentary evidence therefore does not support the claim that Saudi e-learning governance lacks a national consolidating performance mechanism.

The remaining alignment issue lies instead in the relationship between this digital-learning measurement architecture and the broader outcome frameworks represented in D1 and D3. D1 articulates national strategic ambitions, whilst D3 reports human-capability, educational, and socioeconomic outcome indicators; however, the three-document corpus does not make explicit how NDLI-derived institutional digital-learning performance is systematically linked to these broader Vision 2030 and human-capability outcomes. Accordingly, the governance gap identified by the analysis is better characterised as an incomplete cross-framework linkage between digital-learning performance measurement and broader national outcome frameworks, rather than as the absence of national performance consolidation. This pattern suggests an area for further governance integration and empirical investigation; it does not, on the basis of documentary evidence alone, establish that such incomplete linkage causes the policy–practise gap observed in prior studies.

4.9 Emergent themes

Beyond the six predefined governance dimensions, the iterative analytical process identified four cross-cutting themes through repeated comparison of the three policy documents. These themes were not specified in the initial deductive coding framework but emerged during subsequent rounds of analysis as recurrent patterns spanning multiple governance dimensions and policy instruments. They therefore complement the deductive framework by identifying relationships that cut across the strategy, regulatory, and implementation layers of the corpus.

4.9.1 E1—digital citizenship and responsible AI governance

D2 establishes explicit requirements for digital citizenship and responsible technology use, including a documented Digital Code of Conduct, awareness of the responsible use of artificial intelligence, and periodic measurement of digital-citizenship awareness. D3 complements this regulatory orientation with implementation evidence, including the Digital Giving initiative, which reached more than 150,000 beneficiaries. Together, these provisions indicate that ethical and responsible digital participation is emerging as a cross-cutting component of the national digital-learning environment. However, within the analyzed corpus, these digital-citizenship and responsible-AI measures are not explicitly connected to broader sustainability or human-capability outcome indicators. Their contribution to such outcomes should therefore be treated as a proposed policy linkage requiring empirical examination rather than as a demonstrated sustainability effect.

4.9.2 E2—cross-agency coordination and multi-level governance

D3 documents extensive coordination amongst national actors, including the Ministry of Communications and Information Technology (MCIT), the Ministry of Education (MoE), the National eLearning Center (NeLC), and the Ministry of Human Resources and Social Development, alongside international partnerships with institutions such as University College London (UCL), Finnish HY+, and Singapore’s National Institute of Education (NIE). D2 adds a regulatory and performance-governance dimension through institutional reporting requirements and the National Digital Learning Indicator (NDLI), which provides a national mechanism for monitoring and assessing digital-learning performance. The evidence therefore indicates that cross-institutional coordination and national performance-monitoring mechanisms are present rather than absent. What remains less explicit across the three-document corpus is how these coordination and measurement arrangements are systematically connected to the broader strategic and human-capability outcome frameworks represented in D1 and D3. The emergent issue is thus one of cross-framework integration, rather than a lack of coordination infrastructure.

4.9.3 E3—inclusion, accessibility, and gender-related participation

D2 establishes requirements relating to universal accessibility, assistive technologies, and equal opportunities in e-learning environments. D3 provides complementary implementation evidence, including educational support for more than 29,100 learners with special needs and targeted women’s leadership initiatives, such as the HEC Paris Female Leaders Towards the Digital Economy programme. Across the corpus, these provisions position accessibility, inclusion, and equitable participation as recurring components of the wider human-capability agenda. However, because the documents differ in their policy functions and units of reporting, the analysis does not infer direct outcome relationships between D2’s institutional requirements and the participation outcomes reported in D3.

4.9.4 E4—global positioning and institutional performance

International benchmarking and global competitiveness recur across the corpus, although they perform different functions in each document. D1 incorporates international university rankings within its strategic objectives; D2 connects quality requirements to national qualification and labour-market expectations; and D3 reports university-ranking achievements and placements in leading international institutions. This pattern indicates that global positioning and externally benchmarked institutional performance constitute an important component of the policy environment surrounding higher education development. Within the present study, however, such indicators are treated as measures of institutional and human-capability performance that may be relevant to sustainable higher education, rather than as direct measures of environmental, social, or economic sustainability outcomes.

4.10 Derived framework: the integrated E-learning sustainability governance framework (IELSGF)

Building on the governance-alignment matrix (Table 4) and the emergent themes identified in Section 3.9, this study develops the Integrated E-Learning Sustainability Governance Framework (IELSGF) as an analytically derived conceptual framework for interpreting governance relationships across the analysed policy corpus. Whereas the governance-alignment matrix maps the contributions of individual policy instruments across the six governance dimensions, the IELSGF synthesises these findings into an integrated architecture that captures the relationships amongst strategic direction, regulatory standards, implementation mechanisms, performance measurement, and cross-cutting governance priorities. The framework therefore moves beyond descriptive comparison by showing how the differentiated functions of D1, D2, and D3 interact within the wider e-learning governance environment and by identifying areas in which connections across policy and performance frameworks remain only partially explicit.

The IELSGF should not be interpreted as a definitive representation of Saudi Arabia’s national e-learning governance system or as an empirically validated explanatory model. Rather, it provides an interpretive representation of the governance relationships identified within the selected policy corpus. In particular, the framework recognises the existing performance architecture established through D2, including institutional e-learning KPIs and the National Digital Learning Indicator (NDLI), whilst distinguishing this architecture from the broader strategic, human-capability, and sustainability-oriented outcomes represented in D1 and D3. The principal coordination issue identified by the analysis is therefore not the absence of national digital-learning performance consolidation, but the limited explicit cross-framework linkage between digital-learning performance measurement and these broader national outcome frameworks. The IELSGF is presented schematically in Figure 1. and specified in detail in Table 5, providing a conceptual basis for future empirical testing, refinement, and comparative application.

Table 5. Cross-document governance-alignment matrix.

Schematic of the IELSGF derived from the integrated policy analysis of the three national documents. The six analytical dimensions (G1–G6) are organised within three interrelated governance layers: a strategic-direction layer (MOE-anchored, D1), encompassing G1; a standards, digital-infrastructure, and performance-measurement layer (NeLC-anchored, D2), encompassing G2–G3 and the institutional performance architecture associated with G6; and an implementation and human-capability layer (HCDP-anchored, D3), encompassing G4–G5. Accountability and performance governance (G6) operate across the three layers through national strategic expectations (D1), institutional e-learning KPIs and the National Digital Learning Indicator (NDLI) (D2), and broader human-capability and educational outcome reporting (D3). Four cross-cutting themes (E1–E4) span the governance layers: digital citizenship and AI ethics, cross-agency coordination, inclusion and gender, and global positioning. The framework identifies an incomplete cross-framework linkage between the existing digital-learning performance-measurement architecture and the broader strategic, human-capability, and sustainability-oriented outcome frameworks represented in D1 and D3. This linkage is interpreted as a potential area for stronger policy coordination rather than as evidence of the absence of national performance-monitoring infrastructure.

The framework yields three principal policy-level interpretations, which are developed further in Section 4.

• The three national instruments demonstrate substantial convergence around strategic direction and human-capability development and functional complementarity across strategy, regulation, and implementation. Accountability is more appropriately characterised as partially integrated: D2 provides institutional e-learning performance requirements and national digital-learning measurement through the NDLI, whilst D1 and D3 articulate broader strategic and outcome frameworks. The remaining issue is the limited explicit linkage amongst these different levels of performance and outcome measurement.
• Innovation, including AI-enabled and adaptive learning, is addressed across the policy corpus through regulatory requirements, institutional implementation expectations, and national human-capability priorities. However, the analysed documents provide more limited evidence of innovation being explicitly governed as a system-wide sustainability object with clearly specified sustainability-related outcomes and cross-framework accountability relationships. This represents a policy-level proposition rather than evidence of an established implementation deficit.
• The analysis therefore suggests that future governance development could focus on strengthening the explicit connection between the existing digital-learning performance architecture—including institutional e-learning KPIs and the NDLI—and the broader strategic, human-capability, and sustainability-oriented outcome frameworks represented in D1 and D3. Rather than requiring the creation of a wholly new consolidating accountability instrument, the findings point to the potential value of stronger cross-agency coordination and interoperability amongst existing measurement and reporting arrangements. The appropriate institutional mechanism for such integration remains an empirical and policy-design question requiring further investigation.

5 Discussion

This section interprets the findings presented in Section 3 in relation to the contrasting perspectives identified in Section 1.2 and the empirical literature reviewed in Section 1.1. It considers the implications of the cross-document analysis for Saudi and regional higher-education policy, whilst also identifying the limits of what can be inferred from documentary evidence. The value of the integrated analysis lies not merely in reproducing the predefined governance dimensions, but in examining how strategic, regulatory, implementation, and performance functions interact across policy instruments and in identifying cross-cutting governance relationships that were not fully visible when the documents were considered separately.

The analytical contribution of the IELSGF extends beyond the descriptive governance-alignment matrix presented in Table 4. Whereas the matrix identifies the presence, functional role, and distribution of governance provisions across the three policy instruments, the IELSGF synthesises these findings into an interpretive conceptual framework that illustrates how policy layers relate to one another, where responsibilities converge or remain differentiated, and where cross-framework linkages are only partially explicit. The framework should therefore be understood as an analytically derived representation of governance relationships within the selected policy corpus rather than as an empirically validated explanatory model of the Saudi national e-learning governance system.

5.1 Revisiting the technology-and-infrastructure and practise-and-governance perspectives

The integrated policy analysis suggests that the technology-and-infrastructure and practise-and-governance perspectives are better understood as complementary rather than mutually exclusive explanations of sustainable higher-education development. The documentary evidence cannot determine which perspective exerts the stronger causal influence on institutional sustainability outcomes, because the study examines policy architecture rather than institutional implementation or performance. It can, however, clarify how technological provision, governance requirements, and performance-monitoring arrangements are represented and connected across the selected national frameworks.

At the policy level, technology and digital infrastructure are extensively developed. D2 establishes detailed requirements relating to accredited learning platforms, interoperability, cybersecurity, data governance, continuity of service, and technical integration, whilst D3 reports substantial national implementation activity through initiatives such as the Education and Training Status Room, the Skills Portal, Mustaqbali, MicroX, and AgentX, together with large-scale digital-capability programmes. These provisions are consistent with the technology-and-infrastructure literature emphasising the importance of digital capacity, AI adoption, and institutional ICT readiness (Alqarawi and Alnamlah, 2025; Khan et al., 2025; Ronaghi and Ronaghi, 2025).

At the same time, the broader Saudi literature continues to report awareness–practise gaps, uneven curriculum integration, limited evaluation mechanisms, and variation in sustainability implementation across institutions (Abubakar et al., 2020; Alshammari, 2025; Bataeineh and Aga, 2022; Alhusaini, 2025). The present analysis does not establish that these institutional patterns result from deficiencies in national governance coordination. Rather, it identifies a policy-level configuration that may be relevant to their interpretation: digital-learning performance measurement is comparatively well developed within D2, including institutional e-learning KPIs and the National Digital Learning Indicator (NDLI), whilst D1 and D3 operate primarily at the level of broader strategic, educational, and human-capability outcomes. The remaining issue identified in the analysed corpus is therefore not the absence of performance infrastructure, but the limited explicit cross-framework linkage between these different levels of measurement and outcome reporting.

This finding refines the relationship between technological provision and governance rather than privileging one over the other. AI and adaptive-learning initiatives are extensively regulated and reported across D2 and D3, and previous empirical studies have associated technology adoption with improved educational and sustainability-related performance (Alqarawi and Alnamlah, 2025; Ronaghi and Ronaghi, 2025). The present analysis adds a governance perspective by showing that institutional digital-learning performance, national digital-learning measurement through the NDLI, and broader Vision 2030 and HCDP outcome frameworks are represented within the national policy environment but are not explicitly integrated across the three documents examined. This pattern suggests a potential coordination issue that warrants further empirical investigation rather than demonstrating a causal governance deficit.

Accordingly, the study generates a bounded policy-level proposition: the contribution of digital transformation to sustainable higher education may depend not only on technological capacity and institutional practise, but also on the degree to which existing governance, performance-measurement, and outcome-reporting arrangements are explicitly connected across policy levels. Testing this proposition requires institution-level and longitudinal evidence and lies beyond the inferential scope of the present documentary analysis.

5.2 The three national instruments as a governance system

The three-way convergence–complementarity–divergence pattern identified in Section 3.8 provides an integrated characterisation of the Saudi e-learning-sustainability policy architecture. Previous studies have generally examined relevant policy and institutional arrangements separately—through national strategy (Wajid, 2025), quality and regulatory standards (Abubakar et al., 2020), Vision 2030 programme-level outcomes (Badkook, 2025; Rezk et al., 2030), or institutional performance against benchmarks such as UI GreenMetric and the Impact Rankings (Wajid, 2025). Reading the three national instruments together provides a different analytical perspective by showing how their distinct functions interact within a broader governance environment.

The analysis reveals substantial convergence in strategic direction (G1) and human-capability development (G5), together with functional complementarity across strategy (D1), standards and performance requirements (D2), and implementation and outcome reporting (D3). Importantly, the revised analysis also demonstrates that accountability and performance governance (G6) are more developed than an interpretation based solely on the separation of institutional and national KPIs would suggest. D2 establishes detailed institutional e-learning KPIs and incorporates the National Digital Learning Indicator (NDLI) as a national mechanism for digital-learning performance assessment, benchmarking, and continuous improvement. D1 and D3, by contrast, articulate broader strategic, educational, and human-capability objectives and outcomes. The principal coordination issue identified in the analysed corpus is therefore not the absence of an operational accountability architecture, but the limited explicit cross-framework linkage between the existing digital-learning performance-measurement system and these broader national outcome frameworks.

This pattern may provide a policy-level context for interpreting the awareness–practise, mission-integration, and “sustainability divide” patterns documented in previous faculty-perception, curriculum-review, and cross-university studies (Abubakar et al., 2020; Rezk et al., 2030; Alshammari, 2025; Alhusaini, 2025; Bystrov and Panigrahi, 2021; Al'Abri et al., 2026). However, the documentary evidence does not establish that incomplete cross-framework linkage causes these institution-level patterns. Nor does it exclude alternative explanations, including differences in institutional resources, organisational capacity, leadership, implementation practises, or local context. The contribution of the integrated analysis is therefore to identify a potentially relevant governance relationship that can be examined empirically alongside these alternative explanations.

This interpretation also complements practise-theoretic accounts of sustainability implementation in Arab higher-education institutions (Al Mahameed et al., 2025; Aldogiher et al., 2026). Al Mahameed et al. (2025) and Aldogiher et al. (2026) emphasise the roles of institutional rules, norms, organisational actors, and everyday practises in shaping sustainability implementation. The present findings add a policy-architecture perspective by showing that these institutional practises operate within multiple governance and reporting frameworks: D2 specifies institutional e-learning performance requirements and national digital-learning measurement, whereas D1 and D3 articulate broader strategic and human-capability outcomes. The extent to which these different levels of governance are connected in institutional practise, and whether stronger integration is associated with more effective sustainability implementation, remain empirical questions for subsequent research.

5.3 Innovation as a governed sustainability object

The analysed corpus gives innovation—particularly AI-enabled and adaptive learning—a substantial governance and implementation presence, although its treatment differs across the three instruments. At the institutional and regulatory level, D2 establishes explicit requirements for responsible AI governance, including approved governance frameworks, risk assessment, incident documentation, accreditation or approval of AI tools, human-review mechanisms, and bias monitoring (Section 3.5). At the implementation and human-capability level, D3 reports extensive AI-related activity, including 22 AI programmes and diplomas, more than 750 AI-hackathon graduates, and AI courses designated as mandatory across more than 30 universities (Section 3.5). D3 also specifies forward-looking capability targets, including 50,000 beneficiaries under Mustaqbali by 2027 and 100,000 beneficiaries under the AI-empowerment initiative over 3 years.

Taken together, these provisions indicate that AI is already treated as an important object of regulation, institutional adoption, and national capability development. However, within the selected policy corpus, the relationship between these AI-governance and implementation mechanisms and explicitly defined sustainability-oriented outcomes remains less developed. D1 does not frame AI specifically as a sustainability outcome domain, whilst the AI-related capability targets reported in D3 are not explicitly linked to D2’s institutional AI-governance requirements or to a common set of sustainability-oriented performance indicators. Innovation is therefore more clearly represented as an enabling and implementation mechanism than as an explicitly specified sustainability outcome dimension.

This distinction has implications for future governance development, but the documentary evidence does not establish that the current configuration produces particular positive or negative sustainability effects. Existing mechanisms under D2—including institutional KPIs, governance reporting, risk assessment, and the NDLI—already provide a substantial performance and accountability infrastructure. A potential next step is therefore not the creation of an entirely new consolidating mechanism, but the examination of whether existing measurement arrangements could more explicitly connect AI adoption and responsible-AI governance with sustainability-relevant outcomes. Such outcomes might include equitable participation, accessibility, academic integrity, responsible-AI capability, documented bias and privacy risks, and the effectiveness of institutional risk-management responses.

This approach is also relevant to the “sustainability divide” reported between established and newer regional Saudi universities (Rezk et al., 2030). The present analysis cannot determine whether AI adoption widens or reduces such disparities, because institutional implementation and comparative performance were not directly examined. It does, however, suggest that connecting AI-related governance and performance measures with comparable sustainability-oriented indicators could provide a stronger empirical basis for assessing whether differences exist across institutions, how they develop over time, and which governance or institutional factors are associated with them.

5.4 Implications for policy and practise

The revised cross-document analysis and the IELSGF (Tables 4, 5; Figure 1) yield several implications for policy and practise. A first priority concerns strengthening cross-framework integration of national performance measurement. Saudi Arabia already possesses a national digital-learning performance mechanism through the National Digital Learning Indicator (NDLI), alongside D2’s institutional requirements for e-learning KPIs and performance reporting. The policy priority is therefore not to create a new consolidating accountability instrument, but to strengthen the explicit linkage between this existing digital-learning measurement architecture and the broader strategic and human-capability outcome frameworks represented in D1 and D3. One practical approach would be to develop an interoperable reporting layer linking selected NDLI measures—such as engagement, completion, achievement, support effectiveness, and continuous improvement—to relevant Vision 2030 and HCDP outcome indicators. Such integration could enable MOE, NeLC, and HCDP to assess more systematically how institutional digital-learning performance relates to broader national education and human-capability objectives whilst avoiding duplication of existing monitoring infrastructure.

A related policy implication concerns the treatment of innovation as a governed and measurable sustainability dimension. D2’s requirements for responsible AI governance provide a regulatory foundation for incorporating innovation into performance assessment. However, the documentary corpus does not establish that AI-enabled e-learning currently produces measurable sustainability outcomes. Future policy development could therefore connect responsible AI and digital-learning innovation to a limited set of clearly defined outcome indicators, including equitable access and participation, academic-integrity outcomes in AI-enabled assessment environments, faculty coverage of responsible-AI training, accessibility outcomes, and documented AI-related incidents and remediation. Establishing such indicators would allow the contribution of innovation to sustainable higher education to be empirically assessed rather than assumed, consistent with the multidimensional sustainability perspective advanced by Alshuwaikhat et al. (2016) and the ESG-governance approach applied by Alhusaini (2025).

The findings also have implications for cross-agency integration and accountability. Emergent theme E2 (§3.9) indicates that inter-ministerial, institutional, and international coordination is already an important feature of Saudi human-capability and digital-learning development. The policy challenge is therefore not the absence of coordination, but the limited explicit articulation within the analysed corpus of how responsibilities, performance measures, and outcome reporting are connected across the relevant governance frameworks. Existing coordination could be strengthened through clearly assigned responsibilities, interoperable performance reporting, shared outcome definitions, and formal mechanisms linking digital-learning performance to broader human-capability objectives. Where appropriate, joint governance arrangements and performance-linked agreements could further clarify accountability without duplicating existing institutional or national monitoring mechanisms (Table 6).

Table 6. IELSGF—components, anchor instruments, and Vision 2030 linkage.

These implications should not be interpreted as assuming that stronger governance integration will itself produce sustainability outcomes. Rather, they identify mechanisms through which the relationships amongst digital-learning governance, institutional performance, human-capability development, and sustainability-related outcomes could be made more transparent and empirically testable. In this respect, the proposed directions extend the implementation-oriented sustainability framework advanced by Aljendan (2024), the ESG-oriented governance improvements discussed by Alhusaini (2025), and the integrated strategic approach identified by Rezk et al. (2030).

5.5 Limitations

The study is subject to several limitations that bound the interpretation and generalisability of its findings. The corpus is deliberately compact, comprising three national policy instruments selected as a bounded set for examining the strategic, regulatory, and implementation-oriented dimensions of e-learning sustainability governance. Additional Vision 2030 instruments, including the Saudi Green Initiative, the National Transformation Program, and subsequent HCDP annual reports, may refine or complicate particular findings. The present analysis should therefore be regarded as a time-bounded policy baseline against which subsequent developments can be examined.

A further limitation arises from the documentary nature of the study. Policy analysis characterises what official documents prescribe, prioritise, and report rather than what higher-education institutions actually implement in practise. The conclusions are consequently strongest at the level of the selected policy architecture and should not be interpreted as evidence of institutional implementation, compliance, or causal effects. Complementary institution-level research, including surveys, case studies, interviews, and benchmarking studies (Rezk et al., 2030), is required to examine how the identified governance arrangements are translated into organisational practise.

Language also represents a methodological consideration because two of the three documents (D1 and D3) are Arabic-language primary sources. Coding was conducted in the original Arabic by the Arabic-native author, whilst English renderings were prepared for reporting purposes. Although this approach preserves the original policy language during analysis, some semantic or institutional nuance may inevitably be reduced when selected passages and concepts are communicated in English.

Finally, the IELSGF is an analytically derived conceptual framework rather than an empirically validated measurement instrument. Its proposed relationships are grounded in the selected policy corpus and should therefore be treated as propositions for subsequent investigation rather than established causal or institutional mechanisms. Future empirical validation, for example through Delphi consensus involving Saudi sustainability, e-learning, and governance experts or through quantitative testing with faculty and administrator data, is required before the framework can be used as a validated assessment instrument.

5.6 Future research

Future research can extend the present analysis in several complementary directions. A primary priority is empirical validation of the IELSGF. Delphi-based expert assessment and subsequent quantitative testing of its six governance dimensions across multiple Saudi and regional universities could examine the framework’s conceptual coherence, measurement properties, and institutional applicability. Longitudinal replication is equally important. Repeating the integrated policy analysis as new HCDP annual reports are issued and as NeLC standards and related national instruments are revised would make it possible to assess how the relationships amongst strategic direction, regulation, implementation, and performance measurement evolve over time.

Institution-level research could further translate the framework into a governance-maturity assessment applied across different types of Saudi universities, including established, regional, and specialised institutions. Such research would provide empirical evidence on whether differences in governance maturity suggested by the policy analysis are observable in institutional practise rather than assuming their existence from documentary evidence alone. A related research stream could develop and validate sustainability-oriented indicators for AI-enabled e-learning, including measures addressing equity, academic integrity, responsible AI use, incident governance, and bias identification and remediation.

Comparative research would also strengthen the external interpretability of the framework. Applying the same analytical approach in other GCC higher-education systems (Bystrov and Panigrahi, 2021; Al'Abri et al., 2026) could determine whether the patterns of convergence, complementarity, and limited cross-framework integration identified in the Saudi corpus are country-specific or reflect broader regional governance characteristics. Finally, future studies could systematically map IELSGF components against relevant Sustainable Development Goal targets, particularly SDGs 4, 9, 10, and 17. Such mapping could clarify the relationship between national e-learning governance, Vision 2030 objectives, and internationally recognised sustainability outcomes whilst strengthening the framework’s potential for comparative sustainability research.

6 Conclusion

This study examined how Saudi national policy instruments translate innovations in e-learning into governance arrangements relevant to sustainable higher education and Vision 2030. Using directed qualitative content analysis and cross-document thematic synthesis, the study analysed three policy instruments performing distinct but complementary governance functions: the Ministry of Education Strategic Plan (2020–2030), the National eLearning Center E-Learning Standards, and the 2025 Annual Report of the Human Capability Development Program. The integrated analysis identified substantial convergence, functional complementarity, and areas of incomplete cross-framework integration across the six governance dimensions.

First, the three instruments demonstrate substantial convergence in strategic direction and human-capability development whilst performing differentiated governance functions across strategy, regulation, implementation, and performance reporting. D1 establishes broader national strategic ambitions; D2 translates digital-learning priorities into detailed institutional requirements and provides a performance-measurement architecture through institutional e-learning KPIs and the National Digital Learning Indicator (NDLI); and D3 reports broader human-capability, educational, and implementation outcomes. Accountability is therefore more appropriately characterised as partially integrated rather than absent or institutionally fragmented. The principal issue identified within the analysed corpus is the limited explicit linkage between the existing digital-learning performance-measurement architecture and the broader strategic, human-capability, and sustainability-oriented outcome frameworks represented in D1 and D3.

Second, innovation—particularly AI-enabled and adaptive learning—is substantively represented within the national policy architecture, but its relationship with sustainable higher-education outcomes remains only partially specified. D2 establishes institutional requirements for responsible AI governance, including approved governance frameworks, risk assessment, incident documentation, human-review mechanisms, and bias monitoring, whilst D3 reports substantial national investment in AI-related programmes and capability development. However, the analysed documents provide limited explicit linkage between these innovation-governance mechanisms and defined sustainability-oriented outcomes. The findings therefore identify an opportunity to connect responsible AI and digital-learning innovation with measurable indicators relating, for example, to equitable participation, accessibility, academic integrity, responsible-AI capability, and documented risk management. Such relationships require empirical assessment rather than being inferred from policy provision alone.

Third, the policy–practise gaps documented in previous Saudi higher-education research may be interpreted, in part, alongside the cross-framework coordination patterns identified in the present analysis. The documentary evidence does not establish that governance arrangements cause institutional sustainability outcomes, nor does it exclude alternative explanations such as institutional capacity, resource differences, implementation conditions, or organisational practises. Rather, the integrated analysis identifies a policy-level pattern in which digital-learning performance measurement and broader national outcome reporting are present but not explicitly connected across the selected documents. This pattern generates a governance proposition that can be tested through subsequent institution-level and longitudinal research.

Building on these findings, the study proposes the Integrated E-Learning Sustainability Governance Framework (IELSGF) as an analytically derived conceptual framework for interpreting relationships across the selected policy corpus. The IELSGF organises strategic direction, quality assurance, digital infrastructure, innovation, human-capability development, and accountability within an integrated governance architecture whilst incorporating the cross-cutting themes of digital citizenship and AI ethics, cross-agency coordination, inclusion and gender, and global positioning. Importantly, accountability and performance governance operate across the framework rather than being assigned exclusively to a single institutional layer: D1 provides strategic direction, D2 provides institutional performance requirements and national digital-learning measurement through the NDLI, and D3 provides broader implementation and outcome reporting.

The framework’s principal practical implication is therefore not the creation of a wholly new national consolidating instrument, but the strengthening of cross-framework integration amongst existing governance, measurement, and reporting arrangements. Greater interoperability between institutional e-learning KPIs, the NDLI, and the broader strategic and human-capability outcome frameworks represented in D1 and D3 could make the relationship between digital-learning performance and national sustainability-oriented objectives more transparent and empirically assessable. Clearly defined responsibilities, shared outcome definitions, and interoperable reporting arrangements across relevant national actors may provide mechanisms for strengthening such integration without duplicating existing monitoring infrastructure.

The study makes theoretical, methodological, and practical contributions. Theoretically, it positions e-learning governance as a potential connecting layer between digital transformation and sustainability-oriented higher-education outcomes, bringing institutional, sustainability, and technology-governance perspectives into a common analytical framework. Methodologically, it demonstrates the value of examining authoritative policy instruments as an interrelated governance architecture whilst explicitly accounting for their different strategic, regulatory, and reporting functions. Practically, the governance-alignment matrix and IELSGF provide policymakers and higher-education institutions with analytical tools for identifying areas of convergence, complementarity, and incomplete cross-framework linkage and for considering how existing performance architectures might be more explicitly connected to broader national objectives.

These contributions should be interpreted within the study’s boundaries. The analysis is based on a deliberately focused three-document corpus and examines what policy instruments prescribe or report rather than how governance arrangements are implemented within individual institutions. It therefore cannot establish causal relationships between the identified governance patterns and sustainability outcomes or conclusively adjudicate amongst alternative explanations for previously reported policy–practise gaps. Moreover, the IELSGF is an analytically derived conceptual framework rather than an empirically validated measurement or explanatory model. Future research should consequently test and refine the framework using additional policy instruments, longitudinal policy analysis, institutional case studies, stakeholder perspectives, and empirical validation methods such as Delphi studies and structural-equation modelling. Comparative application across Saudi and other GCC higher-education systems could further assess the framework’s transferability and explanatory value.

Taken together, the analysis positions e-learning governance not merely as a technical component of Vision 2030 implementation but as an important dimension of sustainable higher-education governance. The central policy opportunity identified by the study lies in strengthening the explicit cross-framework connection between Saudi Arabia’s existing digital-learning performance architecture and its broader strategic, human-capability, and sustainability-oriented outcome frameworks. Establishing and empirically evaluating these connections represents a more evidence-consistent pathway towards integrated governance than assuming either the absence of national performance infrastructure or a causal relationship between governance arrangements and sustainability performance.

Generative AI statement

The author(s) declared that Generative AI was used in the creation of this manuscript. During the preparation of this manuscript, the authors used ChatGPT (OpenAI), Claude (Anthropic), and Elicit to support preliminary literature discovery and screening, manuscript organisation, language refinement, editorial improvement, and formatting of tables and reference metadata. These tools were not used to generate research questions, collect or generate data, code or analyse the policy documents, develop the analytical framework or the Integrated E-Learning Sustainability Governance Framework (IELSGF), interpret the findings, or formulate the study's conclusions. All outputs were carefully reviewed, verified, and substantially edited by the authors, who accept full responsibility for the accuracy, originality, and integrity of the published work.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frsus.2026.1944473/full#supplementary-material

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Footnotes

Edited by: Shruti Agrawal, GLA Institute of Business Management, India

Reviewed by: Robert Ipiin Gnankob, Woxsen University, India

Areej Derbas, The University of Jordan, Jordan

Footnotes

AI, Artificial Intelligence; ESG, Environmental, Social and Governance; GCC, Gulf Cooperation Council; HCDP, Human Capability Development Program; HDI, Human Development Index; HEI, Higher Education Institution; IELSGF, Integrated E-Learning Sustainability Governance Framework; IPA, Integrated Policy Analysis; KPI, Key Performance Indicator; MCIT, Ministry of Communications and Information Technology; MOE, Ministry of Education; NeLC, National eLearning Center; NQF, National Qualifications Framework; SDGs, Sustainable Development Goals; TVET, Technical and Vocational Education and Training.