Artificial intelligence-enabled practices and management and digitalization of higher institutions in a changing world

1 Introduction

Artificial intelligence (AI) has grown quickly and altered many industries, including education, by finding new methods to handle old issues. As digital transformation progresses, more and more colleges and universities are embracing AI-powered tools to make data-driven decisions, improve teaching, and streamline administrative tasks. To stay competitive, make their operations more effective, and give students high-quality learning experiences, higher education institutions (HEIs) need to keep up with emerging technology as the world of education changes. This study looks at how AI could change the way higher education institutions are run and how they use technology. It focuses on three main areas: improving teaching, using data to make decisions, and making administration more efficient. People are particularly interested in using AI in higher education because it can make learning more personalized through adaptive technology, streamline administrative work, and deliver important information through advanced data analytics. The current literature underscores the theoretical benefits of AI in education; yet, practical information about its impact on institutional management and digital transformation remains limited. This study addresses this deficiency by assessing the efficacy of AI-powered solutions in enhancing teaching and learning, delivering data-driven insights, and facilitating administrative duties within higher education institutions. The rapid advancement of artificial intelligence (AI) presents unprecedented opportunities for transforming higher education institutions, particularly in how they manage operations and deliver educational services in an increasingly digital and dynamic global landscape. As higher education institutions face mounting demands to enhance pedagogical quality, optimize administrative processes, and leverage data for informed decision-making, integrating AI technologies emerges as a critical strategy for fostering adaptability and innovation. This study investigates the relationship between AI and the management and digitalization of higher education institutions, focusing on three pivotal dimensions: pedagogical enhancement through adaptive learning tools, the generation and application of data-driven insights to support decision-making, and the improvement of administrative efficiency via automation of routine tasks. By employing a quantitative research approach and analyzing survey responses from academic and administrative personnel through structural equation modeling using SmartPLS, the research empirically validates the role of AI in driving educational reforms and operational excellence. The findings provide strong evidence of AI's capacity to improve teaching and learning experiences, enable data-informed institutional governance, and streamline administrative workflows, all of which contribute to the broader digital transformation efforts in higher education. This study extends existing theoretical frameworks on AI integration in education and offers practical recommendations for higher institutions seeking to harness AI to meet the challenges of a rapidly evolving educational environment.

1.1 Statement of the problem

Artificial intelligence (AI) is rapidly transforming higher education by reshaping teaching and learning, administrative processes, data use, and institutional decision-making. While AI technologies offer significant opportunities to enhance educational quality and institutional efficiency, their adoption does not automatically translate into effective institutional digital transformation. Many higher education institutions continue to face challenges related to managing technological change, mobilising adequate resources, developing effective leadership and governance, and integrating emerging technologies into existing institutional systems. Although AI-driven applications such as personalised learning, intelligent tutoring systems, predictive analytics, automated administration, and AI-supported decision-making are increasingly available, there remains insufficient empirical evidence on how these applications contribute to the management and digitalization of higher education institutions. In particular, the extent to which Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness influence institutional digital transformation remains inadequately established.

Despite the increasing availability of AI technologies, the presence of these technologies does not necessarily translate into effective institutional digital transformation. Higher education institutions may adopt AI tools for teaching, analytics, or administrative automation without achieving corresponding improvements in organisational change processes, resource utilisation, leadership effectiveness, and governance. This creates an important empirical problem. The technological availability of AI may be increasing faster than the institutional capacity to integrate and manage it effectively. Consequently, there is a need to empirically determine whether specific AI-enabled applications contribute significantly to the management and digitalization of higher education institutions.

Furthermore, several studies have examined different applications and implications of artificial intelligence in higher education, but their emphases differ from the integrated institutional perspective adopted in the present study. Bhutoria, 2022 conducted a systematic review using a Human-In-The-Loop model to examine the use of artificial intelligence for personalised education in the United States, China, and India. The study highlighted the potential of AI-enabled personalised systems to provide student-centred and predictive learning support. This study is relevant to the present research because personalised and AI-supported learning represents an important aspect of Pedagogical Enhancement (PE). However, Bhutoria's review focused primarily on AI and personalised education and did not empirically examine how pedagogical applications relate to broader institutional management and digitalization or how they operate alongside data-driven and administrative AI applications. The present study therefore extends this line of inquiry by examining Pedagogical Enhancement together with Data-driven Insight and Administrative Effectiveness within an integrated institutional model.

Similarly, Crompton and Burke, 2023 conducted a systematic review of 138 studies on artificial intelligence in higher education. Their review identified several applications of AI, including assessment and evaluation, prediction, AI assistants, intelligent tutoring systems, and management of student learning. The study demonstrates the broad range of AI applications in higher education and provides evidence that AI research has expanded considerably. However, the review primarily synthesised existing evidence rather than statistically testing the relationships between distinct AI-enabled application domains and broader institutional management and digitalization. This distinction is important to the present study because the current research moves from identifying AI applications to empirically examining whether specific dimensions of AI-enabled practice, Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness, are associated with Management and Digitalization of Higher Institutions (MDHI).

Further evidence is provided by Bond et al., 2024, who conducted a meta-systematic review of research on artificial intelligence in higher education. Their synthesis of 66 reviews indicated that AI research has concentrated substantially on teaching and learning, while institutional and administrative applications have received comparatively less attention. This finding is particularly relevant to the present study because it suggests that the institutional and managerial implications of AI require greater empirical attention. The present study responds to this need by examining not only AI-enabled pedagogical applications but also Data-driven Insight and Administrative Effectiveness and their predictive relationships with broader institutional management and digitalization.

In addition, Ocen et al., 2025 conducted a systematic literature review of artificial intelligence innovations, opportunities, and challenges in higher education institutions. Their review analysed 54 documents and identified opportunities associated with AI, including enhanced research capabilities, personalised learning, and the automation and streamlining of administrative tasks. The review therefore establishes the relevance of AI to both academic and administrative functions within higher education institutions. However, because the study was a systematic review, it synthesised findings from existing literature rather than empirically testing the statistical relationships between specific AI-enabled practices and institutional management outcomes. The present study addresses this distinction by using primary data from academic and administrative staff in selected Nigerian universities to examine the predictive relationships between Pedagogical Enhancement, Data-driven Insight, Administrative Effectiveness, and Management and Digitalization of Higher Institutions.

Therefore, these studies establish that AI has applications across teaching and learning, personalisation, prediction, research, and administrative processes. However, important empirical and contextual gaps remain. First, previous research has often examined specific AI applications or synthesised existing evidence rather than testing Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness simultaneously in a single empirical model. Second, comparatively less attention has been given to the relationship between these distinct AI-enabled application domains and broader institutional management and digitalization. Third, evidence from the Nigerian higher education context remains limited regarding how AI-enabled practices are statistically associated with institutional management dimensions such as Change Management, Resource Management, and Effective Leadership and Governance.

The present study addresses these gaps by developing and empirically testing an integrated predictive model in selected higher education institutions in Kwara State, Nigeria. Specifically, the study examines whether Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness are significantly associated with Management and Digitalization of Higher Institutions. Thus, unlike studies that primarily review AI applications or focus on individual educational applications, the present study brings together three distinct AI-enabled application domains and examines their relationships with institutional management and digitalization within a TOE-informed analytical framework. This study seeks to address these gaps by investigating the following:

• To examine the relationship between AI-enabled pedagogical enhancement and management and digitalization of higher institutions
• To assess the impact of data-driven insights on management and digitalization of higher institutions
• To determine the relationship between AI-enabled administrative efficiency and management and digitalization of higher institutions

1.2 Research questions

• How does AI-enabled pedagogical enhancement affect the management and digitalization of higher institutions?
• How do data-driven insights affect the management and digitalization of higher institutions?
• How does AI-enabled administrative efficiency affect the management and digitalization of higher institutions?

1.3 Research hypotheses

The following hypotheses were created and put to the test:

• H1: There is a significant positive relationship between AI-enabled pedagogical enhancement and management and digitalization of higher institutions
• H2: There is a significant positive relationship between data-driven insights and management and digitalization of higher institutions
• H3: There is a significant positive relationship between AI-enabled administrative efficiency and management and digitalization of higher institutions.

1.4 Review of related literature

Artificial intelligence has emerged as a significant technological development with substantial implications for higher education. With advancements in machine learning, natural language processing, predictive analytics, intelligent tutoring systems, generative AI, and automated decision-support tools, we're seeing exciting new ways to reshape how colleges and universities teach, manage resources, deliver services, and make decisions. This surge in AI adoption is part of a larger digital transformation, where institutions are weaving digital technologies into the very fabric of their academic, administrative, and management operations (Buetow & Lovatt, 2024; Ocen et al. 2025). What sets AI apart from traditional information technologies is its ability to do more than just store, process, and transmit information. AI systems can spot patterns, make predictions, automate decisions, and adjust their responses based on the data they gather (Abdullahi, 2025; Reina Marín et al., 2025). This means that AI has the potential to elevate higher education from merely using digital tools to creating smarter, more responsive, and data-driven institutional systems. In the realm of higher education, AI is increasingly being harnessed for personalized learning, automated assessments, intelligent student support, predictive analytics, administrative automation, and informed decision-making (Ocen et al., 2025). While these applications can enhance the efficiency and responsiveness of institutions, their success hinges on factors like institutional capacity, digital infrastructure, staff expertise, leadership, governance, and the overall environment in which the technology is deployed. Research indicates that the importance of AI in higher education goes beyond just the classroom. Its influence can be seen across three key areas: enhancing pedagogy, providing data-driven insights, and improving administrative effectiveness. These areas are the main independent constructs explored in this study.

Pedagogical Enhancement is all about using AI technologies to elevate teaching, learning, assessment, and overall educational experiences for students. With AI stepping onto the scene, we're moving away from one-size-fits-all teaching methods and embracing more personalized and adaptive learning environments (Abdullahi, 2022). AI-driven adaptive learning systems can dive into how students perform, engage, and progress, allowing them to tailor content and activities to fit individual learning styles. Likewise, intelligent tutoring systems offer instant feedback, guide students through their learning journeys, and adjust the difficulty level based on what each student needs. Plus, automated assessment tools can save time on routine grading and give students quicker feedback. AI in education also lends a helping hand to teachers by providing insights into student performance and learning challenges. These AI-enabled systems can help educators pinpoint where students might need extra support and craft more personalized teaching strategies. Generative AI tools can further aid in content creation, brainstorming, simulations, and interactive learning experiences, though they do bring up important questions about accuracy, academic integrity, bias, and the essential role of human educators. The potential benefits of AI-driven pedagogical enhancement are especially crucial for the digital transformation of institutions, as teaching and learning are at the heart of higher education (Ouyang and Zhang, 2024). When AI is woven into instructional practices, it can create new types of learning data, spark digital innovation, and push institutions to develop fresh systems for staff training, curriculum management, assessment, and quality assurance. That said, simply adopting AI-based pedagogy doesn't guarantee better education outcomes. Its success often hinges on the digital skills of the staff, the technological infrastructure in place, the institution's readiness, and ethical governance.

Data-driven insight is all about how AI systems can dive into institutional and educational data to pull out valuable information that aids in planning, decision-making, forecasting, and resource management (Molek, 2023). Higher education institutions churn out a massive amount of data from various sources like student records, learning management systems, exams, admissions, enrollments, staff activities, financial operations, and overall administration. With the help of AI and advanced analytics, these extensive datasets can be sifted through to spot patterns and uncover insights that traditional methods might miss. For example, predictive analytics can assist institutions in identifying students who may be at risk of poor academic performance or withdrawal. It can help identify students who might be struggling or at risk of dropping out, allowing institutions to step in early with support. AI can also play a role in forecasting enrollments, planning curricula, analyzing student engagement, managing resources, and monitoring institutional performance. Learning analytics can provide insights into how students interact with educational systems and how those interactions impact their academic success. Leveraging data-driven insights can significantly enhance evidence-based management in institutions. Instead of relying solely on gut feelings or past practices, managers can turn to data-driven information to guide their strategic planning and operational choices (Barakina et al., 2021; Hashim et al., 2022). This approach can boost higher education institutions' ability to adapt to shifting student needs, allocate resources more efficiently, and pinpoint emerging challenges. However, just having access to data doesn't automatically lead to effective decision-making. The benefits of AI-generated insights can be hampered by issues like poor data quality, fragmented information systems, limited analytical capabilities, weak data governance, and a lack of willingness within institutions to embrace evidence-based decision-making. The disconnect between generating analytical information and actually using it in decision-making at institutions might be why having AI analytics doesn't always lead to better performance. With that in mind, this study looks into whether Data-driven Insight plays a significant role in the Management and Digitalization of Higher Education Institutions.

Administrative Effectiveness is all about using AI technologies to boost the efficiency, accuracy, responsiveness, and overall quality of administrative and operational processes in higher education institutions. In institutions, countless routine administrative tasks need to be handled, such as admissions, student inquiries, scheduling, records management, document processing, facilities management, and campus security. By leveraging AI, many of these tasks can be automated or supported, which not only cuts down on repetitive manual work but also enhances service delivery (Crompton and Burke, 2023; Kris et al., 2023). Take AI-powered chatbots, for instance; they can instantly answer common questions from students and staff. Automated scheduling systems can help manage the allocation of classrooms, personnel, and other resources. AI-assisted document processing can significantly speed up the handling of administrative information, while predictive maintenance systems can spot potential infrastructure issues before they escalate into major problems. AI can also play a crucial role in admissions and records management by streamlining the processing and organization of institutional data. Likewise, AI-enabled monitoring systems can enhance campus security and help manage facilities more effectively. However, simply automating administrative tasks doesn't guarantee a broader digital transformation within the institution. The success of AI in administration often hinges on how well systems are integrated, the competence of staff, the institution's readiness, leadership support, and the presence of suitable digital infrastructure (Ovrelid, 2022). This study, therefore, aims to explore the connection between Administrative Effectiveness and the Management and Digitalization of Higher Education Institutions.

1.5 Management and digitalization of higher institutions

Digitalization in higher education goes beyond just turning traditional information into digital formats. It's about weaving digital technologies into the very fabric of institutional processes, structures, services, decision-making, and strategic management (Abdullahi and Tijani, 2019). When higher education institutions embrace digitalization, they can enhance their responsiveness, boost operational efficiency, improve communication, increase flexibility, and sharpen their competitive edge. However, this digital transformation isn't just a simple switch; it demands significant organizational changes. Institutions might need to rethink their current processes, build new capabilities, reallocate resources, and tackle any pushback against technological advancements. This study explores the Management and Digitalization of Higher Institutions (MDHI) through three key dimensions: Change Management, Resource Management, and Effective Leadership and Governance.

Change Management is all about how institutions plan, implement, coordinate, and maintain the organizational changes that come with digital transformation. Embracing AI, for instance, may necessitate shifts in work processes, staff roles, teaching methods, administrative systems, and even the overall institutional culture. That's why effective change management is crucial, it ensures that the adoption of AI goes beyond just acquiring new technology and leads to genuine, sustainable transformation within the institution (Lauer, 2021).

Resource Management is all about effectively mobilizing and utilizing the resources needed to drive digital transformation. This encompasses financial resources, human talent, technological infrastructure, and various institutional capabilities. When it comes to implementing AI, there's a need for investment in tech systems, staff training, ongoing maintenance, cybersecurity, data management, and system integration. If resource management isn't up to par, it can really hold institutions back from reaping the benefits of AI technologies.

Effective Leadership and Governance involve the leadership skills, strategic vision, policies, and institutional frameworks that steer digital transformation. Leaders within institutions play a crucial role in setting priorities, allocating resources, fostering innovation, managing change, and ensuring that AI is rolled out responsibly (Ehule and Ehule, 2021). Governance is equally vital for tackling issues related to data privacy, ethical AI practices, accountability, transparency, academic integrity, and institutional security. In essence, strong leadership and governance lay down the direction and structures necessary to make sure that AI plays a key role in achieving sustainable transformation within institutions.

The literature reviewed clearly shows that AI holds significant promise for transforming higher education. It can enhance personalized teaching and learning experiences, provide data-driven insights for decision-making at institutions, and boost administrative efficiency. However, the effectiveness of adopting AI really hinges on several factors, including how ready the institution is, the skills of its people, the infrastructure in place, leadership quality, governance, and the broader environment. Even with the increasing amount of research on AI in education, there are still three key gaps that need addressing. First, many studies tend to look at pedagogical, analytical, and administrative applications in isolation, rather than exploring how they all work together to drive digitalization in institutions. Second, a lot of the existing literature emphasizes the technological aspects of AI, often overlooking the management strategies that are essential for sustaining digital transformation. Lastly, there's a lack of empirical evidence on how AI-driven applications impact change management, resource management, and the effectiveness of leadership and governance in higher education settings. This study aims to fill those gaps by investigating how Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness contribute to the Management and Digitalization of Higher Education Institutions. We'll be using the Technology Organization Environment (TOE) framework as our guide, which highlights that technological innovation is shaped by the technology itself, the internal capabilities of the organization, and the surrounding environmental context.

1.6 Theoretical framework

The theoretical foundation of this study is grounded in the Technology-Organization-Environment (TOE) framework postulated by Tornatzky and Fleischer, 1990, which explains the factors that influence the adoption and implementation of technological innovations within organisations. The framework proposes that an organisation's decision to adopt, implement, and use a technology is influenced by three broad contextual dimensions: Technology, Organization, and Environment. A key strength of the TOE framework is that it does not view technology adoption as a purely technological decision. Rather, it recognises that the adoption and successful implementation of an innovation depend on the interaction between the characteristics of the technology, the internal characteristics of the organisation, and the external environment in which the organisation operates.

1.7 Technology-organization-environment (TOE framework) theoretical lens

This study is anchored in the Technology Organization Environment (TOE) framework developed by Tornatzky and Fleischer (1990). The framework explains the process through which organisations adopt and implement technological innovations. It proposes that technology adoption is influenced by three interrelated factors: the technology factor, the organisation factor, and the environment factor. The TOE framework is used in this study as a theoretical lens rather than as a complete empirical model in which all three TOE contexts are independently operationalised and statistically tested. The original TOE framework identifies Technology, Organization, and Environment as broad contextual dimensions that shape the adoption and implementation of technological innovations. Consistent with this perspective, the present study uses TOE to provide the theoretical rationale for examining how AI-enabled technological applications can contribute to institutional management and digital transformation.

Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness represent three major manifestations of AI-enabled technological application in higher education and constitute the predictor constructs in the empirical model. Management and Digitalization of Higher Institutions (MDHI) represents the institutional transformation outcome associated with the integration and use of these AI applications. Its dimensions of Change Management, Resource Management, and Effective Leadership and Governance describe organisational capabilities and transformation processes that are theoretically consistent with the Organization context of TOE; however, MDHI is not treated as a direct measurement of the Organization context itself.

The Environment factor is also theoretically relevant but is not directly operationalised in the present empirical model. External factors such as government policies and regulations, funding conditions, external stakeholders, competition, and broader societal expectations are recognised as contextual conditions that may influence AI adoption and implementation. The empirical model represents a focused examination of selected AI-enabled technological applications and their relationship with institutional management and digitalization, informed by the broader theoretical propositions of TOE.

The TOE framework is relevant to the present study because the adoption and institutional impact of artificial intelligence in higher education cannot be explained by technological availability alone. Effective AI-driven transformation requires appropriate technologies, organisational capacity, and a supportive external environment. Accordingly, the framework provides a suitable theoretical foundation for examining how AI-driven Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness relate to the Management and Digitalization of Higher Institutions, particularly through Change Management, Resource Management, and Effective Leadership and Governance. The framework therefore helps explain why the availability of AI applications may produce different institutional outcomes depending on the organisation's capacity to implement and manage technological change and the wider environmental conditions within which the institution operates.

The TOE framework serves as the theoretical lens for the present study rather than as a complete empirical specification of all TOE determinants. The framework provides the theoretical rationale for understanding AI adoption and institutional transformation as processes shaped by technological, organisational, and environmental conditions. Within this broader perspective, the present study develops a focused empirical model comprising three AI-enabled technological application constructs: Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness as predictors of Management and Digitalization of Higher Institutions, as shown in Figure 1.

The empirical model therefore does not test the three TOE contexts as three sets of independent variables. Instead, it examines selected technology-related manifestations of AI and their direct relationship with an institutional transformation outcome. The organisational capabilities represented by Change Management, Resource Management, and Effective Leadership and Governance provide important theoretical context for interpreting the outcome, but they are not statistically modelled as independent TOE predictors. Also, the Environment context is recognised as theoretically relevant but was not measured in the present study. The model should consequently be understood as a TOE-informed empirical model that addresses a specific aspect of AI-enabled institutional transformation rather than as a comprehensive test of the TOE framework.

2 Research method

2.1 Research design

This study adopted a quantitative research design. The quantitative approach was considered appropriate because the study sought to empirically examine the relationships between artificial intelligence-related dimensions, Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness, and the management and digitalization of higher education institutions. Data were collected from respondents at a single point in time using a structured questionnaire. The cross-sectional design was appropriate because it enabled the researchers to obtain respondents' perceptions regarding the current use and perceived effects of AI-driven technologies in higher education institutions. The design also supported the statistical examination of hypothesised relationships among the study constructs using Partial Least Squares Structural Equation Modelling (PLS-SEM). The study's analytical model comprised three predictor constructs, Administrative Effectiveness, Data-driven Insight, and Pedagogical Enhancement, and the dependent construct, Management and Digitalization of Higher Institutions.

2.2 Population and sampling procedure

The target population comprised academic and administrative staff in three selected universities in Kwara State, Nigeria (University of Ilorin, Kwara State University, and Al-Hikmah University). The total population across the three institutions was 6,807 staff. A multi-stage sampling procedure was adopted. At the first stage, the three universities were selected purposively because of their relevance to the study focus on artificial intelligence applications in pedagogical activities, data-driven decision-making, administrative processes, and institutional management and digitalization. Thus, purposive sampling was applied specifically to the selection of institutions and not to the selection of individual respondents.

At the second stage, the respondent population was stratified according to the relevant staff categories, namely academic and administrative staff. This stratification was intended to ensure that both categories of personnel whose roles are directly relevant to teaching, administration, data use, and institutional management were represented in the study. At the third stage, proportional allocation was used to determine the number of respondents to be targeted from each institution based on its proportion of the total population. From the total population of 6,807 staff, 500 respondents were targeted, comprising 331 from the University of Ilorin, 146 from Kwara State University, and 23 from Al-Hikmah University. The allocation was therefore based on the relative population size of each institution rather than equal allocation.

At the fourth stage, respondents who met the study eligibility criteria were selected from the identified staff categories using the specified random-selection procedure within the allocated institutional and staff strata. This multistage process therefore comprised purposive selection of institutions, stratification of respondents, proportional allocation of the target sample, and random selection of eligible respondents within the resulting strata. The target sample of 500 was the planned number of respondents for proportional distribution across the three institutions. Of the 500 targeted respondents, 450 returned usable questionnaires that satisfied the requirements for analysis. Therefore, the final analytical sample comprised 450 respondents, representing 90.0% usable-response rate. The final distribution was as follows: University of Ilorin had a population of 4,500, a target sample of 331, and 318 valid responses; Kwara State University had a population of 2,000, a target sample of 146, and 111 valid responses; while Al-Hikmah University had a population of 307, a target sample of 23, and 21 valid responses. The final analytical sample comprised 450 usable respondents, consisting of 225 academic staff (50.0%) and 225 administrative staff (50.0%). The equal representation of the two categories ensured that both groups were adequately represented in the study.

The unit of analysis in this study was the individual staff member. Although the participating universities constituted the institutional setting and sampling stage, the empirical data were obtained from individual academic and administrative personnel. The inclusion of both categories was necessary because the study examined AI applications spanning pedagogical enhancement, data-driven insight, administrative effectiveness, and institutional management and digitalization. Academic and administrative personnel may, however, differ in their exposure to specific AI applications. Academic staff are more directly involved in teaching, learning, assessment, and related pedagogical activities, whereas administrative staff are more directly involved in administrative processes, records management, scheduling, student services, and institutional operations. Data-driven insight and institutional management and digitalization, however, cut across both categories because both groups interact with institutional information, decision-making processes, digital systems, and organisational change. Therefore, the study treats the individual staff member as the unit of analysis while recognising staff category as a potential source of heterogeneity. The PLS-SEM results represent the combined perceptions of academic and administrative personnel and should not be interpreted as assuming identical exposure to every study construct across the two groups.

Table 1 presents the distribution of the study population, proportionate sample allocation, and valid responses across the three selected universities. The total population of the study was 6,807, comprising 4,500 respondents from the University of Ilorin, representing 66.11% of the total population; 2,000 respondents from Kwara State University, representing 29.38%; and 307 respondents from Al-Hikmah University, representing 4.51%. Based on the proportionate distribution of the population, a total sample of 500 respondents was allocated, consisting of 331 respondents from the University of Ilorin, 146 from Kwara State University, and 23 from Al-Hikmah University. Following the administration and screening of the instruments, 450 valid responses were obtained and retained for analysis. These comprised 318 responses from the University of Ilorin, 111 from Kwara State University, and 21 from Al-Hikmah University, giving an overall valid response rate of 90.0%. The proportionate allocation ensured that each university was represented in the sample according to its relative share of the total study population.

Table 1. Sample participants.

2.3 Instrumentation

This study used a structured questionnaire as the principal instrument for data collection. The questionnaire comprised 25 items covering four constructs: Pedagogical Enhancement (PE), Data-driven Insight (DI), Administrative Effectiveness (AE), and Management and Digitalization of Higher Institutions (MDHI). Participants responded using a five-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. The decision to use a 5-point scale was intentional. A 5-point format helps lighten the load for respondents, minimizes the central tendency bias that often comes with 7-point scales, and encourages clearer, more decisive answers. This approach is supported by earlier studies from Diamantopoulos et al., 2012, which emphasize that shorter scales can enhance the quality of responses. The instrument was developed through the adaptation of previously established measurement items. Six items measuring Pedagogical Enhancement were adapted from Abdullahi, 2022 and Nazaretsky et al., 2022; six items measuring Data-driven Insight were adapted from Seo et al., 2021 and Torres-Cruz and Yucra-Mamani, 2022; six items measuring Administrative Effectiveness were adapted from Kris et al., 2023; and seven items measuring Management and Digitalization of Higher Institutions were adapted from Abdullahi and Tijani, 2019. The use of established measures provided a conceptual and empirical basis for operationalising the four constructs.

The adaptation process involved examining the original items against the operational definitions of the constructs adopted in the present study. The wording of the items was reviewed and, where necessary, modified to reflect the context of higher education institutions and the application of artificial intelligence to teaching and learning, data-informed decision-making, administrative operations, and institutional management and digitalization. The modifications were made to improve contextual relevance while retaining the underlying meaning of the original measurement items. Before the main data collection, the draft questionnaire was subjected to content validation by two experts who held Ph.D. qualifications in Educational Measurement and Educational Management. The experts reviewed the items for relevance to the constructs, clarity of expression, adequacy of construct coverage, and contextual appropriateness. Their observations were considered in refining the wording and presentation of the questionnaire before its administration.

A pilot study was subsequently conducted with 40 respondents from the relevant higher education context. The pilot exercise was undertaken to assess the clarity, comprehensibility, and contextual appropriateness of the questionnaire and to identify potentially ambiguous or difficult-to-understand items before the main survey. The pilot test yielded an overall Cronbach's alpha coefficient of 0.780, indicating acceptable preliminary internal consistency of the instrument. Following the pilot study and necessary refinement, the final questionnaire was administered to the main study respondents. The psychometric properties of the final instrument were assessed using SmartPLS 4.0. Indicator reliability was examined through factor loadings, while internal consistency was assessed using Cronbach's alpha and composite reliability. Convergent validity was assessed using average variance extracted (AVE), while discriminant validity was examined using the Fornell–Larcker criterion and the heterotrait–monotrait ratio (HTMT). The results reported in Table 2 provide empirical evidence concerning the reliability and validity of the final measurement model. All four constructs recorded Cronbach's alpha and composite reliability values above 0.70, while their AVE values exceeded 0.50. Thus, the instrument development process comprised complementary stages of item adaptation, expert content validation, pilot testing, refinement, and empirical psychometric assessment. This process provided both pre-administration evidence concerning the clarity and appropriateness of the questionnaire and post-administration evidence concerning the reliability and validity of the measurement.

Table 2. Summary of measurement model (construct reliability and validity) (p < 0.01).

2.4 Data collection technique

Data were collected through the administration of a structured questionnaire to academic and administrative staff in the selected higher education institutions. The questionnaire was distributed through an online platform. After retrieval, the returned questionnaires were screened for completeness and usability before being subjected to statistical analysis. A total of 500 questionnaires were gathered. 450 of the questionnaires that were filled out correctly were used in this study. Ethical principles were observed throughout the study. Participation was voluntary, respondents were informed of the purpose of the study, and informed consent was obtained before questionnaire completion. The confidentiality and anonymity of respondents were maintained, and participants were informed that they could withdraw from the study without penalty. Where applicable, ethical approval was obtained from the University of Ilorin Ethics Review Committee. The survey did not contain any personal identifiers that may be used to track down or identify specific people, to protect the participants’ security and privacy. These guidelines were followed in adopting this strategy (Neuman, 2013; Choy, 2014).

2.5 Data analysis

Following data collection, the information was analysed using the SmartPLS 4.0 program. Convergent validity, indicator reliability, discriminant validity, and composite reliability are the primary prerequisites for the reflective measurement model. Reflective constructs can be used with PLS-SEM studies if all of these requirements are satisfied (Hair et al., 2017). In addition, the structural model's evaluations include effect size (f²), path coefficients, and coefficient of determination (R²).

3 Result

3.1 Measurement model assessment

Table 2 displays the validity and reliability of the measurement model, evaluated to ensure that the predictions are tested. Reliability is defined as the internal consistency scaled by the coefficient of convergent validity, while validity is defined as the sum of discriminant validity and convergent validity (Flake et al., 2022). The aggregate reliability value of the latent variables should be greater than 0.70, which indicates that each notion has good internal consistency (Hair et al., 2021). Additionally, the measurement index's factor loadings must all be greater than the lowest critical value of 0.60. AVE, on the other hand, must be greater than 0.5. This meant that the constructs had relevant composite reliability that was relevant. According to Hair et al., 2020, to establish discriminant validity, the square root of every AVE of the variables in the model should be bigger than the significant coefficient of this variable to explain if there is a significant difference among variables. As a result, the reliability and validity of the variables studied can all be used to evaluate further hypotheses. Table 2 summarizes the measuring model used in this study. Cronbach's Alpha and Composite Reliability (CR) were used to determine construct reliability.

Table 2 presents a detailed evaluation of the measurement model used to assess four main constructs: Administrative Effectiveness, Data-driven Insight, Pedagogical Enhancement, and Management and Digitalization of Higher Institutions. Each construct's reliability and validity are carefully examined through several key indicators. First, reliability is assessed through Cronbach's Alpha and Composite Reliability. All four constructs demonstrate strong internal consistency, with values well above the commonly accepted threshold of 0.7. This means that the items within each construct consistently measure the same underlying concept.

In terms of validity, the Average Variance Extracted (AVE) values for all constructs exceed 0.5, indicating good convergent validity. This suggests that the constructs explain a substantial portion of the variance in their associated measurement items. Looking at the factor loadings, which represent how well each item correlates with its construct, the majority are comfortably above 0.7. This further reinforces the strength of the measurement model,

Although most indicator loadings exceeded the recommended threshold of 0.70, DI4 recorded a loading of 0.534. Indicator loadings between 0.40 and 0.70 may be retained when the construct demonstrates satisfactory composite reliability and AVE, and when deletion does not produce a meaningful improvement in construct validity. Since the Data-driven Insight construct achieved acceptable internal consistency and convergent validity, DI4 was retained to preserve the content coverage of the construct. Nevertheless, the relatively low loading should be acknowledged as a measurement limitation.

Figure 2 presents the construct measurement model developed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model comprises four latent constructs: Administrative Effectiveness (AE), Pedagogical Enhancement (PE), Data-driven Insight (DI), and Management and Digitalization of Higher Institutions (MDHI). Each construct is measured reflectively by a set of observed indicators.

Administrative Effectiveness (AE) is measured by six items (AE1-AE6), with standardized factor loadings ranging from 0.704 to 0.878, indicating strong item reliability and good convergent validity. Pedagogical Enhancement (PE) is also measured by six items (PE1-PE6), with loadings between 0.621 and 0.894, demonstrating a high level of internal consistency.

The Data-driven Insight (DI) construct was measured using six indicators (DI1–DI6). Most indicators demonstrated satisfactory outer loadings; however, DI4 recorded a relatively low loading of 0.534. DI4 measures the extent to which AI-generated prediction models enhance the accuracy of enrolment forecasting. Although this loading is below the commonly preferred threshold of 0.60, the indicator was retained because it represents an important predictive-analytics dimension of institutional data use that contributes to the content coverage of the construct.

At the construct level, Data-driven Insight demonstrated satisfactory internal consistency and convergent validity, with Cronbach's alpha of 0.765, composite reliability of 0.837, and AVE of 0.547. These results indicate that the construct as a whole achieved acceptable reliability and convergent validity despite the relatively low loading of DI4. The retention decision was therefore based on a combination of statistical evidence and theoretical/content considerations rather than the loading of a single indicator alone. The dependent construct, Management and Digitalization of Higher Institutions (MDHI), is measured by seven indicators (MDHI1-MDHI7), all with satisfactory loadings between 0.648 and 0.871.

The model also displays the structural relationships among the constructs. Administrative Effectiveness (AE) shows the strongest direct effect on MDHI, with a path coefficient of 0.620. Pedagogical Enhancement (PE) has a moderate influence on MDHI (0.198), while the path from Data-driven Insight (DI) to MDHI is weak (0.053). The R² value for MDHI is 0.674, indicating that 67% of the variance in MDHI is explained jointly by AE, PE, and DI. This suggests that the model has good explanatory power and provides meaningful insights into the factors contributing to higher education outcomes.

Figure 3 presents the results of the bootstrapping analysis for the structural model, which evaluates the statistical significance of both the measurement and structural relationships among the latent constructs. The model consists of four key constructs: Administrative Effectiveness (AE), Pedagogical Enhancement (PE), Data-driven Insight (DI), and Management and Digitalization of Higher Institutions (MDHI). The analysis is based on t-values derived from bootstrapping, which are shown along the paths linking indicators to their respective constructs (outer loadings) and between the constructs themselves (path coefficients).

The measurement model demonstrates that each latent construct is reliably and validly measured by its indicators. Administrative Effectiveness (AE) is assessed through six indicators (AE1 to AE6), all of which show strong statistical significance, with t-values ranging from 13.651 to 56.775. Pedagogical Enhancement (PE) is also measured by six indicators (PE1 to PE6), with t-values between 10.657 and 53.409, confirming the strong of the measurement items. Similarly, Data-driven Insight (DI), measured by six indicators (DI1 to DI6), produces t-values ranging from 8.213 to 45.678. Although some of these values approach the lower bound of acceptability, they generally support the construct's measurement validity. Management and Digitalization of Higher Institutions (MDHI), the dependent construct, is measured by seven indicators (MDHI1 to MDHI7), all of which show statistically significant t-values ranging from 9.175 to 42.275, indicating solid indicator reliability.

In the structural model, the significance of the hypothesized paths among the latent constructs is also evaluated. The path from Administrative Effectiveness (AE) to MDHI yields a t-value of 8.584, indicating a strong and statistically significant effect. The path from Pedagogical Enhancement (PE) to MDHI is also significant, with a t-value of 2.301, although the effect is comparatively weaker. In contrast, the path from Data-driven Insight (DI) to MDHI produces a t-value of 0.723, which is not statistically significant. This suggests that, within this model, Data-driven Insight does not exert a meaningful direct influence on Management and Digitalization of Higher Institutions. The model explains 67.4% of the variance in MDHI, as reflected in an R² value of 0.674. Administrative Effectiveness emerged as the strongest predictor of Management and Digitalization of Higher Institutions, followed by Pedagogical Enhancement. Data-driven Insight did not demonstrate a statistically significant direct effect.

3.2 Discriminant validity

Discriminant validity was assessed using the Fornell–Larcker criterion and the heterotrait–monotrait ratio (HTMT). These procedures were used to determine whether Administrative Effectiveness (AE), Data-driven Insight (DI), Pedagogical Enhancement (PE), and Management and Digitalization of Higher Institutions (MDHI) represent empirically distinct constructs. The Fornell–Larcker results presented in Table 2 indicate a potential concern involving Administrative Effectiveness and Management and Digitalization of Higher Institutions. The correlation between AE and MDHI is 0.803, whereas the square root of AVE is 0.751 for AE and 0.776 for MDHI. Thus, the AE–MDHI correlation exceeds the corresponding diagonal values, indicating substantial shared variance between the two constructs. The other construct relationships provide comparatively stronger evidence of discriminant validity. This finding is therefore treated as a limitation requiring careful interpretation rather than being disregarded. Discriminant validity is established when the value of the square root of the AVE of each construct is higher than the construct's highest correlation with any other latent construct (Hair et al., 2014; Henseler et al., 2009).

The Fornell and Larcker criterion compares the square root of the Average Variance Extracted (AVE) for each construct (displayed on the diagonal) to the correlations between constructs (off-diagonal values). For good discriminant validity, the diagonal values should be greater than any corresponding off-diagonal correlations in their rows and columns. Diagonal values (square root of AVE): AE: 0.751, DI: 0.739, MDHI: 0.776, and PE: 0.782. For AE, the square root of AVE (0.782) is less than its correlation with MDHI (0.803). This suggests that AE and MDHI may not be sufficiently distinct, indicating a potential problem with discriminant validity between these two constructs. For DI, the diagonal value (0.739) is greater than its correlations with all other constructs (AE: 0.673, MDHI: 0.632, PE: 0.617). This supports good discriminant validity for DI. For MDHI, the diagonal value (0.776) is greater than its correlations with DI (0.632) and PE (0.702), but less than its correlation with AE (0.803). This again points to an overlap between MDHI and AE. For PE, the diagonal value (0.782) is higher than its correlations with DI (0.617) and MDHI (0.702), but is slightly lower than its correlation with AE (0.743). Though closer, it still mostly supports distinctiveness. Ensuring discriminant validity is crucial because it underpins the confidence that each construct uniquely captures a distinct concept, which is essential before proceeding to structural model analysis or interpreting relationships among constructs. If needed, this finding can be triangulated with other validity tests, like HTMT, for a comprehensive assessment.

Table 3 presents the Fornell–Larcker results, which indicate a potential discriminant validity concern between Administrative Effectiveness and Management and Digitalization of Higher Institutions because the correlation between the two constructs (0.803) exceeds the square root of the AVE for Administrative Effectiveness (0.751) and Management and Digitalization of Higher Institutions (0.776). This suggests that the constructs may share substantial variance and should therefore be examined alongside the HTMT results.

Table 3. Fornell and Lacrker criterion.

The diagonal is the square root of AVE, while the off-diagonal numbers are the correlations between latent variables.

The HTMT results provide additional evidence concerning construct distinctiveness. The reported HTMT values were below the commonly accepted threshold of 0.85, with the highest values remaining within the acceptable range (Ab-Hamid et al., 2017). Taken together, the results provide evidence that the constructs are sufficiently distinct for structural model assessment, subject to the corrected Fornell–Larcker values being verified against the original SmartPLS output.

Table 4 shows the HTMT values range from 0.725 to 0.832. The highest value is observed between Administrative Effectiveness and Management and Digitalization of Higher Institutions (HTMT = 0.832), followed by AE-PE (0.822) and AE-DI (0.806). All observed HTMT coefficients are below the conservative threshold of 0.85. Thus, the HTMT results support discriminant validity, although the relatively high AE-MDHI value warrants cautious interpretation.

Table 4. HTMT values of the construct.

HTMT, Heterotrait–Monotrait ratio; values are presented for each pair of constructs.

The theoretical distinction between AE and MDHI is important in interpreting this result. Administrative Effectiveness refers to the extent to which AI improves the efficiency and effectiveness of specific administrative functions, including student enquiries, scheduling, document processing, predictive maintenance, admissions and records management, and security and facility management. In contrast, Management and Digitalization of Higher Institutions represents the broader institutional outcome of AI-enabled transformation, reflected in change management, resource management, and effective leadership and governance. The former therefore represents an AI-enabled functional domain, whereas the latter captures broader institutional management and digitalization outcomes. The study design also reflects this distinction by specifying AE as the independent construct and MDHI as the dependent construct.

3.3 Assessment of the structural model

The Structural model is illustrated graphically. Figure 4 illustrates the directions of the arrows connecting the study's constructs, as determined by the framework's proposed hypotheses. The single-headed arrows indicate the significance of the study concept. The factor loadings for each item are shown in Figure 4.

Figure 4 presents the bootstrapping results used to evaluate the structural measurement model. This analysis assesses the statistical significance of both the outer loadings of the indicators and the path coefficients linking the latent constructs. The model comprises four key constructs: Administrative Effectiveness (AE), Pedagogical Enhancement (PE), Data-driven Insight (DI), and Management and Digitalization of Higher Institutions (MDHI). Each construct is measured reflectively by a set of observed indicators, with t-values provided to determine the strength and significance of these relationships. For Administrative Effectiveness (AE), all six indicators (AE1–AE6) exhibit strong and statistically significant loadings, with t-values ranging from 13.651 to 56.775. Similarly, the six indicators measuring Pedagogical Enhancement (PE) (PE1–PE6) demonstrate high reliability, with t-values spanning from 10.657 to 53.409.

Data-driven Insight (DI) is also measured by six indicators (DI1–DI6), which yield t-values between 8.213 and 45.678. While most of these values fall within acceptable significance thresholds, a few lower values suggest the need for cautious interpretation. The dependent construct, Management and Digitalization of Higher Institutions (MDHI), is measured by seven indicators (MDHI1–MDHI7), with t-values ranging from 9.175 to 42.275, thereby affirming the construct's reliability. The structural model further reveals the significance of the hypothesized relationships between constructs.

Table 5 presents the results of the structural model assessment, including the path coefficients, bootstrapped t-values, p-values, and hypothesis decisions. The analysis examined the hypothesised relationships between Pedagogical Enhancement (PE), Data-driven Insight (DI), Administrative Effectiveness (AE), and Management and Digitalization of Higher Institutions (MDHI).

• H1: The relationship between AI-enabled Pedagogical Enhancement and Management and Digitalization of Higher Institutions was positive and statistically significant (β = 0.193, t = 2.301, p = 0.022). Therefore, H1 was supported. This finding indicates that AI-enabled pedagogical applications, including adaptive learning technologies, intelligent tutoring systems, automated assessment, AI-supported teaching feedback, personalised content delivery, and simulation-based learning, contribute positively to institutional management and digitalization.
• H2: The relationship between Data-driven Insight and Management and the Digitalization of Higher Institutions was positive but not statistically significant (β = 0.050, t = 0.723, p = 0.470). Therefore, H2 was not supported. The result suggests that the availability or perceived usefulness of AI-generated data insights alone may not be sufficient to produce measurable improvements in institutional management and digitalization. The effective use of data-driven insight may depend on complementary conditions such as institutional readiness, data infrastructure, data governance, digital competence, leadership capacity, and the ability to translate analytical information into strategic action. Nevertheless, the relatively low loading of DI4 constitutes a measurement limitation and is interpreted cautiously. This is particularly relevant because Data-driven Insight did not have a statistically significant direct relationship with Management and Digitalization of Higher Institutions (β = 0.050, t = 0.723, p = 0.470). The non-significant structural relationship should not, however, be attributed directly to DI4 alone because the hypothesis was tested using the overall latent construct rather than an individual indicator. Also, the reason for not deleting DI4 was that, the Average Variance Extracted (AVE) values for all the Data-driven Insight constructs exceed 0.5, indicating good convergent validity
• H3: The relationship between AI-enabled Administrative Effectiveness and Management and Digitalization of Higher Institutions was positive, strong, and statistically significant (β = 0.627, t = 8.584, p < 0.001). Therefore, H3 was supported. Administrative Effectiveness emerged as the strongest predictor of MDHI. This finding indicates that AI-enabled administrative applications, including chatbots, automated scheduling, document processing, predictive maintenance, admissions and records management, and AI-supported security and facility management, are substantially and positively associated with institutional management and digitalization. Therefore, the structural model shows that Administrative Effectiveness was the strongest predictor of Management and Digitalization of Higher Institutions, followed by Pedagogical Enhancement. Data-driven Insight, although positively related to MDHI, did not demonstrate a statistically significant direct effect. The model explained 67.4% of the variance in MDHI (R² = 0.674), indicating substantial explanatory power.

Table 5. Results of the structural model (path coefficient).

4 Discussion

This study examined the influence of artificial intelligence-enabled pedagogical enhancement, administrative effectiveness, and data-driven insight on the management and digitalization of higher education institutions. The findings demonstrate that AI-enabled applications contribute differently to the management and digitalization of higher education institutions. Administrative Effectiveness emerged as the strongest predictor of Management and Digitalization of Higher Institutions (β = 0.627, t = 8.584, p < 0.001), followed by Pedagogical Enhancement (β = 0.193, t = 2.301, p = 0.022). In contrast, Data-driven Insight had a positive but statistically non-significant direct effect on MDHI (β = 0.050, t = 0.723, p = 0.470). The finding indicates that the contribution of AI to institutional transformation is uneven and may be more immediately evident in administrative process improvement and pedagogical applications than in the direct availability of data-driven analytical capabilities.

The finding for H1 shows that AI-enabled Pedagogical Enhancement has a positive and statistically significant effect on Management and Digitalization of Higher Institutions (β = 0.193, t = 2.301, p = 0.022). This indicates that AI applications directed toward teaching and learning can contribute to broader institutional digital transformation. Relevant applications include adaptive learning technologies, intelligent tutoring systems, automated assessment, AI-supported teaching feedback, personalised content delivery, and simulation-based learning. These applications can encourage institutions to develop new digital systems for teaching, assessment, curriculum management, staff development, and quality assurance. However, the relatively smaller coefficient compared with Administrative Effectiveness suggests that pedagogical applications, although important, may have a more moderate direct association with institutional management and digitalization. Their broader institutional effect may depend on supporting factors such as staff digital competence, technological infrastructure, institutional readiness, and appropriate governance. This aligns with the growing body of research emphasizing how AI-powered pedagogical tools improve personalized learning experiences and teaching effectiveness, thus facilitating digital transformation in education management. Several studies corroborate this finding. For instance, Abdullahi, 2025 emphasized that AI integration in pedagogical activities, such as adaptive learning, fosters personalized education that meets individual student needs, thereby enhancing institutional digital maturity. Similarly, Alfredo et al., 2024 reported that AI-driven intelligent tutoring systems boost learner engagement and autonomy, which indirectly improves institutional management by promoting data-rich environments for decision-making. Therefore, the positive association found in this study reinforces the theoretical postulate that pedagogical innovation through AI is a crucial driver for digital transformation in HEIs. Conversely, some scholars urge caution, noting that pedagogical enhancement alone may not suffice without complementary systemic or infrastructural digital readiness Williamson and Piattoeva, 2019 argue that while advanced AI pedagogies are transformative, their impact depends on institutional capacity, governance, and staff digital literacy. Although this study reports a significant effect, the moderate path coefficient (0.193) suggests PE's impact might require synergistic factors to maximize its influence on digitalization.

The finding for H2 indicates that Data-driven Insight has a positive but statistically non-significant direct effect on Management and Digitalization of Higher Institutions (β = 0.050, t = 0.723, p = 0.470). Thus, H2 was not supported. The result suggests that access to AI-generated analytical information does not automatically translate into institutional digital transformation. The non-significant relationship may indicate a gap between the generation of analytical information and its actual use in institutional decision-making. Factors such as data quality, fragmented information systems, limited analytical capacity, weak data governance, institutional readiness, and limited ability to translate analytical outputs into strategic action may constrain the institutional benefits of AI-generated insights. Therefore, the finding does not imply that data-driven insight is unimportant, rather, it suggests that its direct contribution to MDHI was not statistically established in the present study. The finding is in line with Otto et al., 2023, who advocate AI-driven automation and workflow optimization in administration as key efficiency enhancers crucial for digital transformation. On the other hand, Vial, 2019 highlights limitations in assuming that administrative digitalization alone can drive transformative change, pointing out that bureaucratic inertia and complexity often impede the full realization of AI benefits. This study's finding that AE is not a significant predictor may reflect the contextual realities of HEIs, where administrative systems are often fragmented or constrained by regulatory and organizational challenges (Patricio and Ferreira, 2024). Additionally, adoption of AI in administrative processes may improve operational efficiency but may not be immediately evident in strategic digitalization metrics, which encompass broader digitally-enabled management innovations. Chen and Cheung, 2025 propose that data-driven decision-making is a core capability enabling agile and evidence-based management in education. Also Ovrelid, 2022 and Chen and Cheung, 2025 argue that data analytics facilitates proactive student support and strategic institutional planning. The lack of significance might arise from practical considerations within the surveyed institutions, such as immature data infrastructures, low integration of analytic outputs into policy, or limited staff capacity to act on insights. Studies by Cota-Rivera et al., 2022 illustrate that the effective exploitation of data insights depends heavily on organizational culture and strategic alignment, factors that may be underdeveloped in the sample HEIs.

The finding for H3 demonstrates that AI-enabled Administrative Effectiveness has a strong, positive, and statistically significant effect on Management and Digitalization of Higher Institutions (β = 0.627, t = 8.584, p < 0.001). H3 was therefore supported. Importantly, Administrative Effectiveness emerged as the strongest predictor among the three AI-related constructs examined in the study. This result suggests that administrative applications of AI may provide a particularly visible and immediate pathway to institutional digitalization. Applications such as AI-enabled chatbots, automated scheduling, document processing, predictive maintenance, admissions and records management, and AI-supported security and facility management can reduce repetitive administrative processes, improve service responsiveness, support operational coordination, and enhance institutional efficiency. Because these applications are directly connected to routine institutional processes, their effects may be more readily translated into observable improvements in management and digitalization. The strength of the relationship also suggests that AI-enabled administrative transformation may be particularly important for higher education institutions seeking to improve operational efficiency and digital service delivery. Nevertheless, the finding should not be interpreted as meaning that administrative automation alone guarantees comprehensive institutional transformation. Effective implementation still requires appropriate infrastructure, staff competence, institutional readiness, leadership support, governance, and effective change management.

5 Conclusion

This study concludes that AI-enabled applications contribute unevenly to the management and digitalization of higher education institutions. Administrative Effectiveness emerged as the strongest and statistically significant predictor of Management and Digitalization of Higher Institutions (β = 0.627, p < 0.001). Pedagogical Enhancement also demonstrated a positive and statistically significant effect (β = 0.193, p = 0.022), although its effect was considerably weaker than that of Administrative Effectiveness. In contrast, Data-driven Insight had a positive but statistically non-significant direct effect on MDHI (β = 0.050, p = 0.470). The findings therefore indicate that AI-enabled administrative applications may provide the most immediate contribution to institutional digitalization, followed by AI-enabled pedagogical applications. The non-significant finding for Data-driven Insight further suggests that the availability of AI-generated analytical information does not necessarily produce institutional transformation unless institutions possess the capacity to convert such information into effective decisions and actions. The findings further suggest that AI-driven institutional transformation should not be understood solely in terms of technology availability. Effective change management, resource mobilisation, leadership, governance, institutional readiness, and staff digital competence remain important conditions for translating AI applications into sustainable institutional transformation. Within the scope of the present empirical model, however, the strongest direct evidence is for Administrative Effectiveness and Pedagogical Enhancement as predictors of MDHI.

5.1 Implications and limitations

The findings have important implications for higher education managers and policymakers. Since Administrative Effectiveness demonstrated the strongest relationship with institutional management and digitalization, higher education institutions should give priority to AI applications that improve administrative workflows, service delivery, records management, scheduling, admissions, facilities management, and other routine institutional processes.

The significant effect of Pedagogical Enhancement also indicates the importance of investing in AI-enabled teaching and learning applications, including adaptive learning, intelligent tutoring, automated assessment, and AI-supported feedback. Such investments should be accompanied by staff development, appropriate infrastructure, and institutional policies that support responsible and effective use. The non-significant effect of Data-driven Insight suggests that institutions should not equate access to AI-generated analytics with effective evidence-based management. Institutions should strengthen data governance, analytical capacity, interoperability of information systems, digital competence, and mechanisms for translating analytical outputs into strategic and operational decisions. The results support an integrated approach in which AI applications are combined with appropriate institutional capabilities. Administrative effectiveness should be prioritised because it demonstrated the strongest direct association with MDHI, while pedagogical applications should be strengthened as an important complementary pathway to institutional digitalization. Data-driven applications should be supported by the organisational and technical capabilities required to convert analytical information into actionable institutional decisions.

A limitation concerns the potential heterogeneity between academic and administrative personnel. Although the study included an equal number of academic and administrative staff (225 each), the two groups may differ in their exposure to specific AI applications. Academic personnel are more directly involved in AI-enabled teaching, learning, and assessment, whereas administrative personnel may have greater exposure to AI-supported administrative processes and institutional operations. Aggregating the two categories may therefore mask differences in how particular AI applications are perceived or experienced. Future studies should examine academic and administrative personnel separately and, where appropriate, conduct measurement-invariance assessment and multigroup analysis to determine whether the structural relationships differ significantly between the two categories.

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Footnotes

Edited by: Noluntu Mpekoa, University of South Africa, South Africa

Reviewed by: Amit Kumar Srivastava, Parul University, India

John Cliford Alvero, San Pablo Colleges, Philippines