Prompting change: Exploring undergraduate perceptions after AI literacy training
##plugins.themes.bootstrap3.article.main##
Abstract
As generative artificial intelligence (GenAI) tools become increasingly integrated into daily life, society has been steadily adapting to changes in both work and education, often where guidelines and social contracts have yet to be established. Questions surrounding ethical usage are still being debated, and increased literacy in GenAI is necessary to enable voices, especially from younger generations, in these discussions. This paper presents a case study on the effects of advancing AI literacy among a small group of university undergraduate students (N = 6) through a personalised training intervention. Drawing on pre- and post-test survey data and transcripts from a training on Adobe Firefly, Google Notebook LM, and Google Gemini, the study examines changes in students’ knowledge and ethical considerations. While participants initially expressed cautious or critical views (e.g., fearing overreliance on GenAI or academic dishonesty), quantitative results after training indicate greater familiarity in using GenAI as a learning tool rather than a shortcut, with one student in the post-training survey stating how they now believe their learning “will be enhanced far beyond what [they] were doing before”. Additionally, qualitative analysis reveals a persistent discrepancy between the perceived and actual understanding of GenAI and academic policies, further supporting the need for these discussions. Therefore, this work positions AI literacy as a critical component of higher education onboarding.
References
Adobe (2024) Generative AI in Higher Education: Insights from Campus Thought Leaders | Adobe Blog. Available at: https://blog.adobe.com/en/publish/2024/06/28/generative-ai-in-higher-education-insights-from-campus-thought-leaders (Accessed: June 28, 2025).
Alexander, K., Savvidou, C. and Alexander, C. (2023) “WHO WROTE THIS ESSAY? DETECTING AI-GENERATED WRITING IN SECOND LANGUAGE EDUCATION IN HIGHER EDUCATION,” Teaching English With Technology, 2023(2). Available at: https://doi.org/10.56297/BUKA4060/XHLD5365.
Apata, O. et al. (2025) “The Role of Artificial Intelligence in Enhancing Classroom Learning: Ethical, Practical, and Pedagogical Considerations,” in 2025 ASEE -GSW Annual Conference Proceedings. 2025 ASEE -GSW Annual Conference, Arlington, TX, Texas: ASEE Conferences, p. 55084. Available at: https://doi.org/10.18260/1-2--55084.
Bai, Y. and Wang, S. (2025) “Impact of generative AI interaction and output quality on university students’ learning outcomes: a technology-mediated and motivation-driven approach,” Scientific Reports, 15(1), p. 24054. Available at: https://doi.org/10.1038/s41598-025-08697-6.
Baidoo-Anu, D. et al. (2024) “Exploring student perspectives on generative artificial intelligence in higher education learning,” Discover Education, 3(1), p. 98. Available at: https://doi.org/10.1007/s44217-024-00173-z.
Baytas, C. and Ruediger, D. (2025) Making AI Generative for Higher Education: Adoption and Challenges Among Instructors and Researchers. Ithaka S+R. Available at: https://doi.org/10.18665/sr.322677.
Blahopoulou, J. and Ortiz-Bonnin, S. (2025) “Student perceptions of ChatGPT: benefits, costs, and attitudinal differences between users and non-users toward AI integration in higher education,” Education and Information Technologies [Preprint]. Available at: https://doi.org/10.1007/s10639-025-13575-9.
Braun, V. and Clarke, V. (2006) “Using thematic analysis in psychology,” Qualitative Research in Psychology, 3(2), pp. 77–101. Available at: https://doi.org/10.1191/1478088706qp063oa.
Chan, C.K.Y. (2023) “Is AI Changing the Rules of Academic Misconduct? An In-depth Look at Students’ Perceptions of ‘AI-giarism.’” arXiv. Available at: https://doi.org/10.48550/ARXIV.2306.03358.
Chan, C.K.Y. and Hu, W. (2023) “Students’ voices on generative AI: perceptions, benefits, and challenges in higher education,” International Journal of Educational Technology in Higher Education, 20(1), p. 43. Available at: https://doi.org/10.1186/s41239-023-00411-8.
Chan, C.K.Y. and Zhou, W. (2023) “Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument.” arXiv. Available at: https://doi.org/10.48550/ARXIV.2305.01186.
Cohen, J. (2013) Statistical Power Analysis for the Behavioral Sciences. Burlington: Elsevier Science.
Cotton, D.R.E., Cotton, P.A. and Shipway, J.R. (2023) “Chatting and cheating: Ensuring academic integrity in the era of ChatGPT,” Innovations in Education and Teaching International, 61(2), pp. 228–239. Available at: https://doi.org/10.1080/14703297.2023.2190148.
Gabriel, S. (2024) “Generative AI and Educational (In)Equity,” International Conference on AI Research, 4(1), pp. 133–142. Available at: https://doi.org/10.34190/icair.4.1.3153.
Gasaymeh, A.-M.M., Beirat, M.A. and Abu Qbeita, A.A. (2024) “University Students’ Insights of Generative Artificial Intelligence (AI) Writing Tools,” Education Sciences, 14(10), p. 1062. Available at: https://doi.org/10.3390/educsci14101062.
Google (no date) Gemini AI video generator powered by Veo 3, Break the silence with Veo 3. Available at: https://gemini.google/overview/video-generation/?hl=en (Accessed: June 30, 2025).
IBM Corp. (2019) “IBM SPSS Statistics for Windows.” Armonk, NY: IBM Corp. Available at: https://www.ibm.com/products/spss-statistics.
Knoth, N. et al. (2024) “AI literacy and its implications for prompt engineering strategies,” Computers and Education: Artificial Intelligence, 6, p. 100225. Available at: https://doi.org/10.1016/j.caeai.2024.100225.
Leslie, D. and Perini, A.M. (2024) “Future Shock: Generative AI and the International AI Policy and Governance Crisis,” Harvard Data Science Review [Preprint], (Special Issue 5). Available at: https://doi.org/10.1162/99608f92.88b4cc98.
Long, D. and Magerko, B. (2020) “What is AI Literacy? Competencies and Design Considerations,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu HI USA: ACM, pp. 1–16. Available at: https://doi.org/10.1145/3313831.3376727.
Lumivero (2023) “NVivo.” Available at: https://www.lumivero.com/.
Lund, B.D. et al. (2025) “AI and Academic Integrity: Exploring Student Perceptions and Implications for Higher Education,” Journal of Academic Ethics [Preprint]. Available at: https://doi.org/10.1007/s10805-025-09613-3.
Microsoft and LinkedIn (2024) AI at Work Is Here. Now Comes the Hard Part. Available at: https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part (Accessed: June 28, 2025).
Rapanta, C. et al. (2025) “Critical GenAI Literacy: Postdigital Configurations,” Postdigital Science and Education [Preprint]. Available at: https://doi.org/10.1007/s42438-025-00573-w.
Ravšelj, D. et al. (2025) “Higher education students’ perceptions of ChatGPT: A global study of early reactions,” PLOS ONE. Edited by C. Wang, 20(2), p. e0315011. Available at: https://doi.org/10.1371/journal.pone.0315011.
Rebelo, M. (2025) An inside look at Google’s AI-powered NotebookLM. Available at: https://zapier.com/blog/google-ai-notebook-notebooklm/ (Accessed: June 28, 2025).
Schiff, K.J., Schiff, D.S. and Bueno, N.S. (2025) “The Liar’s Dividend: Can Politicians Claim Misinformation to Evade Accountability?,” American Political Science Review, 119(1), pp. 71–90. Available at: https://doi.org/10.1017/S0003055423001454.
Yang, Y. et al. (2025) “Navigating the landscape of AI literacy education: insights from a decade of research (2014–2024),” Humanities and Social Sciences Communications, 12(1), p. 374. Available at: https://doi.org/10.1057/s41599-025-04583-8.
Yarlagadda, K.C. (2025) “AI in Education: Personalized Learning and Intelligent Tutoring Systems,” European Journal of Computer Science and Information Technology, 13(32), pp. 15–27. Available at: https://doi.org/10.37745/ejcsit.2013/vol13n321527.
Downloads
Article Metrics Graph
##plugins.themes.bootstrap3.article.details##
Data Availability Statement
The transcription data contains a lot of references that could aid in identifying the participants and have not been shared for that reason
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.