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Frontiers in Education ·

Volume 11 - 2026 | Issue 1 | https://doi.org/10.3389/feduc.2026.1390332

Artificial Intelligence Reshapes Academic Inequality in Higher Education Through Unequal Access to Learning Enhancement Technologies

  1. 1Universidad Nacional Hermilio Valdizán
  2. 2Universidad Nacional Intercultural de la Selva Central Juan Santos Atahualpa
  3. 3Universidad Católica Santo Toribio de Mogrovejo
  4. 4Universidad Nacional de Trujillo
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Abstract

Introduction: Artificial intelligence (AI) is expanding opportunities for personalized learning, academic writing assistance, automated feedback, and access to educational resources in higher education. However, students may differ in their ability to benefit from these technologies because of socioeconomic circumstances, unequal technological access, differences in AI literacy, and variations in institutional support. This study aims to examine the relationships between unequal access to AI-enhanced learning technologies and academic inequality among university students.

Methodology: The study proposes a quantitative, non-experimental, cross-sectional design with a correlational and explanatory orientation. Data will be collected through a structured questionnaire administered to university students. The analytical framework includes socioeconomic conditions, access to AI-enhanced learning technologies, AI literacy, institutional support, learning engagement, and self-reported academic outcomes. The planned analyses include descriptive statistics, correlation analysis, and multivariable regression. Structural equation modeling may be considered if the sample size, measurement properties, and model specification justify its use.

Results: Empirical results are not yet available because data collection and statistical analysis have not been completed. The final manuscript will report the participant characteristics, measurement quality, estimated associations, confidence intervals, effect sizes, and hypothesis-testing results based on the observed data.

Conclusion: The proposed framework seeks to clarify how access to AI technologies, students' AI-related competencies, socioeconomic conditions, and institutional support may be associated with differences in academic experiences. The study may inform institutional strategies for promoting equitable access to AI-enhanced learning technologies. Any conclusions about educational effects will be based on the empirical findings and interpreted in light of the limitations of a cross-sectional design.

Keywords

1. Introduction

Artificial intelligence (AI), particularly generative AI, is changing the ways in which university students access information, receive feedback, organize academic tasks, and engage with learning resources. AI-powered applications can provide explanations, support writing processes, facilitate information retrieval, and help students explore complex concepts. Nevertheless, the availability of these technologies does not necessarily ensure that all students benefit from them equally.

Digital inequality research has demonstrated that disparities extend beyond access to devices and internet connectivity. Students may also differ in their digital competencies, patterns of technology use, and ability to obtain meaningful educational benefits from technological resources (Hargittai, 2002; van Deursen & van Dijk, 2014; Scheerder et al., 2017). The emergence of generative AI introduces additional questions concerning access to advanced tools, the ability to evaluate AI-generated information, and the institutional conditions under which these technologies are used.

In higher education, students' socioeconomic circumstances may influence their access to suitable devices, reliable connectivity, paid applications, and opportunities to develop AI-related competencies. At the same time, institutional policies, staff guidance, and training opportunities may shape students' understanding of appropriate and responsible AI use. These factors suggest that AI-related educational inequality should be examined as a multidimensional issue involving resources, capabilities, institutional support, and students' academic experiences.

Although AI can expand access to educational assistance, it can also introduce challenges related to reliability, bias, academic integrity, and unequal opportunities to develop relevant skills (Kasneci et al., 2023; Miao & Holmes, 2023). The consequences of AI integration should therefore be investigated empirically rather than assumed to be uniformly beneficial or harmful.

This study proposes a framework for examining the relationships between socioeconomic conditions, access to AI-enhanced learning technologies, AI literacy, institutional support, learning engagement, and self-reported academic outcomes among university students. By distinguishing technological availability from the capacity to use AI critically and effectively, the study seeks to contribute to discussions about educational equity and responsible AI integration in higher education.

2. Hypotheses and objectives

General objective

To examine the relationships between socioeconomic conditions, access to AI-enhanced learning technologies, AI literacy, institutional support, learning engagement, and self-reported academic outcomes among university students.

Specific objectives

1. To examine the association between students' socioeconomic conditions and their access to AI-enhanced learning technologies. 2. To assess the relationship between access to AI-enhanced learning technologies and AI literacy. 3. To examine the association between AI literacy and self-reported academic outcomes. 4. To investigate the relationship between institutional support and AI literacy. 5. To assess the association between institutional support and self-reported academic outcomes. 6. To examine the relationship between socioeconomic conditions and self-reported academic outcomes.

Research hypotheses

H1: More favorable socioeconomic conditions are positively associated with access to AI-enhanced learning technologies.

H2: Greater access to AI-enhanced learning technologies is positively associated with AI literacy.

H3: Higher levels of AI literacy are positively associated with self-reported academic outcomes.

H4: Greater institutional support is positively associated with AI literacy.

H5: Greater institutional support is positively associated with self-reported academic outcomes.

H6: More favorable socioeconomic conditions are positively associated with self-reported academic outcomes.

All hypotheses will be evaluated using empirical data. Associations will not be interpreted as causal effects because the proposed research design is non-experimental and cross-sectional.

3. Materials and methods

Research design

The study will use a quantitative, non-experimental, cross-sectional design with a correlational and explanatory orientation. This design is intended to examine associations among socioeconomic conditions, access to AI-enhanced learning technologies, AI literacy, institutional support, and self-reported academic outcomes at a single point in time.

Study population and sampling

The target population will consist of university students enrolled in higher education institutions. The geographical scope, participating institutions, eligibility criteria, sampling strategy, and final sample size must be specified before recruitment. The sample size will be determined through an appropriate a priori calculation based on the primary statistical analysis, anticipated effect size, significance level, statistical power, and number of predictors.

Data collection instrument

Data will be collected using a structured questionnaire. The instrument will cover socioeconomic conditions, access to AI-enhanced learning technologies, AI literacy, institutional support, learning engagement, and self-reported academic outcomes. Wherever possible, established instruments with evidence of validity and reliability will be adapted to the study context. Any newly developed or adapted items will undergo expert review and pilot testing before the main data collection.

Data collection procedure

Eligible participants will receive information about the study and provide informed consent before completing the questionnaire. Participation will be voluntary. Procedures will be established to protect confidentiality, minimize the collection of identifying information, and store research data securely.

Statistical analysis

The analysis will begin with data screening and descriptive statistics. The internal consistency and measurement properties of the scales will be examined using procedures appropriate to the instrument and data. Bivariate associations will be explored using suitable correlation coefficients. Multivariable regression models will then be used to examine the hypothesized relationships while accounting for theoretically justified covariates. Structural equation modeling will be considered only if the sample size, measurement model, and assumptions support its use.

The final report will document missing-data handling, model assumptions, effect estimates, confidence intervals, and relevant measures of uncertainty. All findings will be interpreted as associations rather than causal effects.

4. Results

Empirical results are not yet available because data collection and statistical analysis have not been completed. This section will be updated after the study has been conducted. The final results will report participant characteristics, descriptive statistics, measurement reliability and validity, bivariate associations, multivariable regression estimates, confidence intervals, effect sizes, and the evidence concerning each prespecified hypothesis. No numerical findings or hypothesis-testing conclusions will be reported before the analysis of the observed data.

5. Discussion

The proposed framework addresses AI-related academic inequality as a multidimensional issue rather than a problem of technological access alone. Students may have different opportunities to use AI tools because of socioeconomic constraints, differences in AI literacy, and variation in institutional guidance. These dimensions warrant joint examination when evaluating the equity implications of AI integration in higher education.

The distinction between access and effective use is particularly important. Having access to an AI application does not necessarily mean that a student can evaluate the accuracy of its responses, recognize its limitations, or use its outputs appropriately for academic purposes. AI literacy may therefore be an important dimension of students' experiences with AI-enhanced learning technologies.

Institutional support may also be relevant. Clear guidance, accessible training, and transparent academic policies could help students understand appropriate uses of AI and develop the competencies required for responsible engagement. However, the strength and direction of these relationships must be established through empirical analysis.

Because the study has not yet produced empirical results, these considerations should be understood as theoretically informed expectations rather than findings. Once data are available, the discussion will compare the observed results with previous research, evaluate the research hypotheses, and consider alternative explanations.

The proposed cross-sectional design will limit the ability to establish temporal order or causal relationships. Self-reported academic outcomes may also differ from objective measures of achievement. Further limitations may arise from sampling procedures, institutional context, measurement choices, and the generalizability of the findings. These limitations will be assessed in the final manuscript.

6. Conclusions

This study proposes a framework for examining the relationships between socioeconomic conditions, access to AI-enhanced learning technologies, AI literacy, institutional support, and self-reported academic outcomes among university students. Its central premise is that equitable AI integration requires attention not only to technological availability but also to students' competencies and the institutional conditions that shape their opportunities to benefit from these tools.

The proposed research may provide evidence to inform institutional approaches to AI literacy development, responsible use, and equitable access. However, substantive conclusions about the relationships among the study variables must await data collection and analysis. The findings will need to be interpreted in accordance with the study design, measurement quality, and limitations of self-reported data.

Statements

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.

Funding

This research received no external funding.

Data availability

No empirical dataset is currently available because data collection has not yet been completed. Following completion of the study, the availability of anonymized data and supporting research materials will be determined in accordance with participant consent, applicable ethical requirements, and institutional data protection policies.

Ethics approval

Before data collection begins, the research protocol will be submitted to the appropriate institutional ethics committee or review board for evaluation and approval, where required. Informed consent will be obtained from participants before they complete the questionnaire. The final manuscript will report the relevant ethics committee, approval reference, and date if approval is granted and such details are required by the target journal.

Author contributions

Merith Eva Bardales Meneces: Conceptualization and research design. Alexandra Rivas Meza: Literature review and theoretical framework. Raymond Portelli Xueres: Methodology and research instrument development. Karen Roxana Pulido Murillo: Critical review and manuscript editing.

All authors should review and approve the final manuscript and agree to be accountable for their respective contributions. This statement must be revised if the actual contributions differ from those described.

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Citation

Meneces, M. E. B., et al. (2026). Artificial Intelligence Reshapes Academic Inequality in Higher Education Through Unequal Access to Learning Enhancement Technologies. Frontiers in Education, Volume 11. https://doi.org/
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11 - 2026

Edited by

Teresa Maria Dias Paiva

Copyright

© 2026 Merith Eva Bardales Meneces, Alexandra Rivas Meza, Raymond Portelli Xueres and Karen Roxana Pulido Murillo. 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: Merith Eva Bardales Meneces meritheva86@gmail.com

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