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Cited by 49Artificial Intelligence in EducationDigital literacy in educationArtificial Intelligence in Healthcare and Education

The GenAI divide among university students: A call for action

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Beckman, Karley, Apps, Tiffani, Howard, Sarah Katherine, Rogerson, Claire, Rogerson, Ann, Tondeur, Jo·2025·The Internet and Higher Education
DOI 10.1016/j.iheduc.2025.101036
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Abstract

The rapid pace of technological change with generative artificial intelligence is accelerating much faster than our capacity to understand and regulate it. Higher education institutions have been firmly focused on the impacts of this innovation on academic integrity while grappling with unknown longer-term impacts on students' academic study and future work. This mixed method study aims to capture student perspectives on their self-reported understanding of GenAI and intentions to use GenAI for their academic study during the critical diffusion stage and policy vacuum. Through a survey with 194 university students, the study explored student's understanding, knowledge, experience and intended use of GenAI tools to support their academic study. The paper presents three distinct student profiles established through cluster analysis of measures of digital and AI literacy, which are then explored in-depth through presentation of qualitative items. Notably, the cluster profiles demonstrate variation across the profiles of novice, cautious and enthusiastic users and patterns related to their knowledge of ChatGPT and intended uses. The paper draws on digital divide empirical literature and explores the potential to repeat digital divides among groups of students based on their access, capabilities, and capacity to leverage these for educational advantage. We propose that building upon a vast existing body of educational research about digital literacy inequalities offers rich insights into the current problems facing education institutions, specifically, what role do universities play in supporting students to understand and harness GenAI, now and in their futures.

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Crossref · 10.1016/j.iheduc.2025.101036

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  • Conventional digital divide models fail to sufficiently address the psychological impacts of unequal access, including low self-efficacy, increased anxiety, or identity threat.

    “Although these models can be applied in identifying structural differences, they fail to sufficiently address the psychological impacts of unequal access, including low self-efficacy, increased anxiety, or identity threat, which this review has found to be important mediating processes (Pratama, 2025; Beckman et al., 2025).”

    From Mapping the impact of generative AI in higher education: A scoping review of psychological and equity dimensions. (2026) · cites this paper as Beckman et al., 2025

    The Discussion (p. 7) reports that students with lower self-reported digital and AI literacy were sometimes uncertain or fearful about GenAI, and that risk discourse may compound disadvantage for cautious and novice students. The paper does not conclude that conventional digital-divide models inadequately address psychological impacts, nor does it establish increased anxiety or identity threat as mediating processes. (LLM, conf=0.82)

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Authors
Beckman, K., Apps, T., Howard, S. K., Rogerson, C., Rogerson, A., & Tondeur, J.
Affiliations
  • University of Wollongong (AU)
  • University of Leeds (GB)
  • Vrije Universiteit Brussel (BE)
Corresponding author
Beckman, Karley
ORCID
Venue
The Internet and Higher Education
Publisher
Elsevier BV
Published
2025-10
Volume
67
Pages
101036
ISSN
1096-7516 (ISSN-L)
DOI
10.1016/j.iheduc.2025.101036
OpenAlex
W4412430540
Cited by
49
References
27
Citations by year
2026: 46 · 2025: 3
FWCI
50.95
Type
journal article
Language
en
Open access
Licence
Locations
  • The Internet and Higher Education (publishedVersion, cc-by-nc)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
  • VUBIR (Vrije Universiteit Brussel) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Information Systems › Digital literacy in education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
generative AI, university students, digital divide, digital literacy, AI literacy, student perceptions of AI, ChatGPT, academic integrity, student profile, higher education policy

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