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Frontiers in Education ·

Volume 11 - 2026 | Issue 1 | https://doi.org/10.3389/feduc.2026.1789924

AI-Supported Learning and Academic Performance: The Mediating Role of Student Engagement

  1. 1Universidad Nacional San Luis Gonzaga de Ica
  2. 2Universidad Nacional Mayor de San Marcos
  3. 3Universidad Nacional Santiago Antúnez de Mayolo
  4. 4Universidad Nacional de San Cristóbal de Huamanga
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Abstract

Artificial intelligence (AI) is increasingly transforming teaching and learning in higher education, creating new opportunities for personalized instruction, academic support, and learning enhancement. However, unequal access to AI-based educational technologies may reinforce existing academic inequalities among students from different socioeconomic backgrounds and institutional contexts. This article examines how disparities in access to, use of, and support for AI-enhanced learning technologies may contribute to unequal educational opportunities in higher education. It develops a conceptual framework connecting digital access, AI literacy, institutional readiness, and students’ opportunities to benefit from AI-supported learning. The framework also considers the role of institutional policies, digital infrastructure, and educational support in shaping the relationship between AI adoption and academic inequality. Rather than presenting unverified empirical findings, the article identifies conceptual relationships and proposes directions for future empirical investigation. The framework aims to support researchers, educators, and higher education policymakers in evaluating the equity implications of AI integration and in designing strategies that promote inclusive, responsible, and equitable access to educational technologies.

Keywords

1. Introduction

Artificial intelligence (AI) is increasingly transforming higher education by expanding opportunities for personalized learning, automated feedback, academic assistance, and access to digital educational resources. AI-powered technologies, including intelligent tutoring systems, generative AI applications, adaptive learning platforms, and automated assessment tools, are changing how students access information, develop academic skills, and engage with learning activities. These technologies have the potential to support educational quality and improve learning experiences. However, their benefits may not be distributed equally across student populations. Differences in access to digital infrastructure, technological resources, AI literacy, institutional support, and opportunities for meaningful use may create new forms of academic inequality.

Academic inequality refers to differences in students’ educational opportunities, learning experiences, academic performance, and access to the resources necessary for educational success. Although higher education institutions have expanded access to digital learning environments, students continue to experience unequal conditions related to socioeconomic background, geographical location, institutional resources, and digital competence. The growing integration of AI into educational practices introduces an additional dimension to these inequalities because students differ not only in their access to technology but also in their ability to use AI tools effectively, critically, and responsibly.

The digital divide provides an important perspective for understanding these differences. Traditionally, the digital divide has been associated with unequal access to computers, internet connectivity, and digital resources. Nevertheless, access alone does not fully explain inequalities in technology-supported learning. Students may have access to similar devices and internet services while differing substantially in their digital skills, confidence, critical evaluation abilities, and capacity to integrate technological tools into academic tasks. These differences become particularly relevant in AI-supported learning, where effective use may require students to formulate appropriate prompts, evaluate generated information, recognize inaccuracies, and apply outputs to discipline-specific learning objectives.

The emergence of generative AI has intensified these concerns. Students who possess reliable internet access, paid subscriptions, advanced digital skills, and guidance from instructors may be better positioned to benefit from AI-supported learning than students who rely on limited

2. Hypotheses and objectives

Research objectives

The general objective of this article is to examine how unequal access to artificial intelligence (AI)-based learning enhancement technologies may contribute to academic inequality in higher education.

The specific objectives are:

1. To identify the main dimensions of unequal access to AI-based learning enhancement technologies among higher education students. 2. To examine the potential relationships between technological access, AI literacy, institutional readiness, and educational equity. 3. To develop a conceptual framework for understanding how individual and institutional factors may influence students' opportunities to benefit from AI-supported learning. 4. To identify implications for institutional policies and educational practices aimed at promoting equitable and responsible AI integration in higher education. 5. To propose directions for future empirical research on the relationship between AI adoption and academic inequality.

Research question

How does unequal access to AI-based learning enhancement technologies potentially contribute to academic inequality in higher education?

Propositions

Proposition 1: Unequal access to AI-based learning enhancement technologies may contribute to differences in students' learning opportunities.

Proposition 2: AI literacy may influence students' ability to translate access to AI technologies into meaningful educational benefits.

Proposition 3: Institutional readiness, including digital infrastructure, faculty support, and educational policies, may shape the relationship between

3. Materials and methods

Research design

This article adopts a conceptual research approach to examine the potential relationship between unequal access to artificial intelligence (AI)-based learning enhancement technologies and academic inequality in higher education. The approach focuses on identifying relevant concepts, examining their theoretical relationships, and developing a framework that can inform future empirical investigation.

Conceptual scope

The analysis focuses on four interrelated dimensions: access to AI technologies, AI literacy, institutional readiness, and educational equity. Access to AI technologies refers to students' opportunities to use suitable digital devices, reliable internet connectivity, and AI-enabled learning applications. AI literacy encompasses the knowledge and skills required to use AI tools critically, responsibly, and effectively. Institutional readiness includes digital infrastructure, faculty preparation, institutional policies, and pedagogical support. Educational equity concerns the extent to which students have fair opportunities to access learning resources and benefit from educational technologies.

Analytical approach

The conceptual analysis examines how these dimensions may interact to shape students' opportunities to benefit from AI-supported learning. Particular attention is given to the distinction between technological availability and effective educational use. The framework considers the possibility that students with comparable access to technology may experience different educational outcomes because of differences in digital competence, instructional guidance, socioeconomic circumstances, and institutional support.

The analysis also considers potential mechanisms through which AI integration may influence academic inequality. These include differences in the quality and frequency of AI use, the ability to evaluate AI-generated information, the availability of appropriate learning support, and the extent to which institutions provide inclusive technological environments.

Framework development

The proposed framework connects technological access, AI literacy, and institutional readiness with educational equity. It is intended to organize relevant concepts and guide the formulation of research questions, indicators, and hypotheses for future empirical studies.

Methodological limitations

Because this article is conceptual, its propositions should not be interpreted as empirically verified relationships. The framework requires subsequent validation through appropriate empirical designs, such as student surveys, institutional case studies, interviews, or mixed-methods research. The present approach does not establish causal effects or estimate the magnitude of relationships among the proposed dimensions.

4. Results

A multidimensional perspective on AI access

The conceptual analysis identifies four dimensions relevant to understanding potential inequalities in AI-supported higher education: technological access, AI literacy, institutional readiness, and educational equity. These dimensions provide a structure for examining why students may experience different opportunities to benefit from similar educational technologies.

Technological access and learning opportunities

Access to suitable devices, reliable internet connectivity, and appropriate AI applications represents a basic condition for participation in AI-supported learning. However, access alone may not ensure that students obtain equivalent educational benefits. Differences in affordability, availability, and continuity of access may influence the extent to which students can incorporate AI tools into their academic activities.

AI literacy and effective use

AI literacy represents a second dimension of potential inequality. Students may differ in their ability to formulate appropriate requests, evaluate generated information, identify inaccuracies, and use AI outputs in accordance with academic requirements. These differences may influence whether access to AI technologies translates into meaningful learning opportunities.

Institutional readiness and educational support

Institutional readiness encompasses infrastructure, faculty preparation, guidance on responsible AI use, and educational policies. Institutions with stronger support systems may be better positioned to integrate AI into teaching while addressing accessibility and inclusion. Conversely, limited resources or unclear guidance may create barriers to effective and equitable implementation.

Implications for educational equity

The proposed framework suggests that academic inequality associated with AI should be examined through the interaction of individual and institutional conditions rather than through technological access alone. It provides a conceptual basis for future research investigating whether differences in access, literacy, and institutional support are associated with disparities in learning opportunities.

These findings represent a conceptual synthesis rather than the results of a completed empirical investigation. The proposed relationships require further examination using appropriate data and research methods.

5. Discussion

The integration of artificial intelligence into higher education raises important questions about the distribution of educational opportunities. Although AI technologies may support personalized learning, access to information, and academic assistance, their educational value depends on the conditions under which students can access and use them. The conceptual framework developed in this article emphasizes that technological availability, AI literacy, and institutional readiness should be considered together when examining potential academic inequalities.

First, access to AI technologies should be understood as a multidimensional issue. The availability of an internet connection or a digital device does not necessarily imply that students have equal opportunities to use advanced educational tools. Differences in affordability, reliability, accessibility, and the availability of appropriate applications may influence students' participation in AI-supported learning. Consequently, policies focused exclusively on providing basic digital infrastructure may be insufficient to address all forms of inequality.

Second, AI literacy may be an important factor in determining whether students can benefit from AI-supported learning. Effective use requires more than the ability to access an application. Students must also be able to evaluate the reliability of generated information, recognize limitations, protect personal information, and integrate AI assistance into their own learning processes. Educational institutions can support these capabilities through digital literacy initiatives, instructional guidance, and transparent academic policies.

Third, institutional readiness may shape the educational consequences of AI adoption. Universities differ in their technological infrastructure, financial resources, faculty development opportunities, and approaches to responsible AI use. These differences may influence whether AI tools are incorporated into learning environments in ways that are accessible and pedagogically appropriate. Institutional strategies should therefore consider not only the adoption of new technologies but also the distribution of support and learning opportunities among students.

The framework also highlights the importance of distinguishing between the availability of AI tools and the educational outcomes associated with their use. Students may use the same applications for different purposes and with different levels of guidance. Future research should examine whether differences in access and AI literacy are associated with measurable differences in academic engagement, learning experiences, or performance, while accounting for relevant individual and institutional characteristics.

Several implications follow for higher education policy. Institutions may need to combine infrastructure investment with AI literacy development, faculty training, accessible educational design, and clear guidance on responsible use. Such measures could help create conditions in which students have more equitable opportunities to benefit from AI-supported learning. Nevertheless, the effectiveness of these strategies should be evaluated empirically rather than assumed.

This article has limitations. Its conceptual approach does not provide empirical estimates of the relationship between AI access and academic inequality. The proposed framework should therefore be understood as a basis for future research rather than as a validated explanatory model. Subsequent studies could use surveys, interviews, institutional comparisons, and longitudinal designs to investigate the proposed relationships and determine which interventions are most effective in different educational contexts.

Overall, the discussion underscores the need to consider the social and institutional conditions surrounding AI adoption. The central issue is not simply whether AI is available in higher education, but whether students have fair opportunities to use these technologies effectively, critically, and responsibly.

6. Conclusions

Artificial intelligence is creating new opportunities for teaching and learning in higher education, but its integration also raises concerns about the equitable distribution of educational resources and benefits. This article presents a conceptual framework for examining the relationship between unequal access to AI-based learning enhancement technologies and academic inequality.

The framework emphasizes four interconnected dimensions: technological access, AI literacy, institutional readiness, and educational equity. Considering these dimensions together may provide a more comprehensive understanding of why students experience different opportunities to benefit from AI-supported learning. Access to technology alone may be insufficient when students face differences in digital competence, instructional guidance, and institutional support.

For higher education institutions, the framework highlights the importance of combining technological investment with inclusive educational policies, AI literacy development, faculty training, and responsible implementation practices. These measures represent potential directions for promoting more equitable AI-supported learning environments, although their effectiveness requires empirical evaluation.

The principal contribution of this article is a conceptual structure that can guide future investigations into AI adoption and academic inequality. Further research should examine the proposed relationships using appropriate data, validated measures, and diverse institutional contexts. Such research will be necessary to determine how AI technologies can be integrated into higher education in ways that support educational quality while promoting equitable learning opportunities.

Statements

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

No new datasets were generated or analyzed for this conceptual article. Data availability is therefore not applicable.

Ethics approval

Ethical review and approval were not required for this conceptual article because it did not involve human participants, animal subjects, or the collection of personal data.

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Citation

Lavarello, J. C. Q., et al. (2026). AI-Supported Learning and Academic Performance: The Mediating Role of Student Engagement. Frontiers in Education, Volume 11. https://doi.org/
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11 - 2026

Edited by

Rany Sam

Copyright

© 2026 Quijandria Lavarello, Palomino Silva, Valladares Jara and Sosa Bitulas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).

License: CC BY 4.0

*Correspondence: Juan Carlos Quijandria Lavarello

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