Document Type

Article

Publication Date

2025

DOI

10.1111/bjet.13588

Publication Title

British Journal of Educational Technology

Pages

28 pp.

Abstract

Generative artificial intelligence brings opportunities and unique challenges to nontraditional higher education students, stemming, in part, from the experience of the digital divide. Providing access and practice is critical to bridge this divide and equip students with needed digital competencies. This mixed-methods study investigated how nontraditional higher education students interact with ChatGPT in multiple courses and examined relationships between ChatGPT interactions, engagement, self-efficacy and performance. Data were collected from 73 undergraduate and graduate students through chat logs, course reflections and artefacts, surveys and interviews. ChatGPT interactions were analysed using four metrics: prompt number, depth of knowledge (DoK), prompt relevance and originality. Results showed that ChatGPT prompt numbers (β = 0.256, p < 0.03) and engagement (β = 0.267, p < 0.05) significantly predicted performance, while self-efficacy did not. Students' DoK (r = 0.40, p < 0.01) and prompt relevance (r = 0.42, p < 0.01) were positively correlated with performance. Text mining analysis identified distinct interaction patterns, with ‘strategic inquirers’ demonstrating significantly higher performance than ‘exploratory inquirers’ through more sophisticated follow-up questioning. Qualitative findings revealed that while most students were first-time ChatGPT users who initially showed resistance, they developed growing acceptance. Still, students tended to use ChatGPT sparingly and, even then, as only a starting point for assignments. The study highlights the need for targeted guidance in prompt engineering and AI literacy training to help nontraditional higher education students leverage ChatGPT more effectively for higher-order thinking tasks.

Rights

© 2025 The Authors.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Data Availability

Article states: "The datasets of the current study are available from the corresponding author upon reasonable request."

Original Publication Citation

Yang, M., Jiang, S., Li, B., Herman, K., Luo, T., Moots, S. C., & Lovett, N. (2025). Analysing nontraditional students' ChatGPT interaction, engagement, self‐efficacy and performance: A mixed‐methods approach. British Journal of Educational Technology. Advance online publication. https://doi.org/10.1111/bjet.13588

ORCID

0000-0002-8138-3722 (Luo), 0000-0001-6540-7985 (Moots)

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