AI as a Personal Tutor: Enabling Students to Access Smart Tools

Authors

DOI:

https://doi.org/10.70232/jrep.v3i3.213

Keywords:

Generative AI in Education, AI Tutors, Enabling Education, Student Engagement, Ethical AI, Stigma

Abstract

Generative Artificial Intelligence (GenAI) is increasingly used by university students, yet enabling/preparatory cohorts may require explicit guidance to realise learning benefits while maintaining academic integrity. This study explored GenAI tools as “personal tutors” within a leading Australian university preparatory context, focusing on how students actually used these tools, how institutional transparency requirements shaped practice, and what barriers remained. A blended descriptive methodology combined autoethnography of teaching practice with student-generated artefacts (Statements of AI Engagement and assessment extracts). Dataset 1 comprised the educator’s reflective journal notes, analysed using Reflexive Thematic Analysis. Dataset 2 comprised de-identified student artefacts, analysed using Inductive Thematic Analysis. To contextualise the qualitative findings, Statements of AI Engagement were also coded according to the institution’s four AI-engagement levels and to the tools and usage reported, and then summarised using descriptive frequencies/percentages. Participants were students (n=74) enrolled in two university preparatory courses (English Literacy and Research Design) during Semester 2, 2024. 72 Statements of AI Engagement were collected from 98 submitted assessments. Findings indicate broad acceptance of GenAI as supportive learning aids, most commonly for idea generation, rephrasing, summarisation, and basic editing/proofreading. ChatGPT was the most frequently acknowledged large language model, while AI-enabled search and writing-support tools were used more selectively. Although students generally positioned themselves as retaining authorial control, the study also identified persistent concerns regarding over-trust in automated outputs, cognitive load associated with evaluating and integrating AI suggestions, and an internalised stigma that reduced willingness to access both human and AI writing support. The findings suggest that structured, guidelines-based integration, including explicit verification instructions and reflective Statements of AI Engagement, can promote responsible, transparent GenAI use in enabling education.

References

Abdelouahed, M., Yateem, D., Amzil, C., Aribi, I., Abdelwahed, E. H., & Fredericks, S. (2025). Integrating artificial intelligence into public health education and healthcare: Insights from the COVID-19 and monkeypox crises for future pandemic readiness. Frontiers in Education, 10, Article 1518909. https://doi.org/10.3389/feduc.2025.1518909

Abrar, M. (2024). Generating video narratives to support learning. [Doctoral dissertation, University of Leeds]. White Rose eTheses Online. Retrieved from https://etheses.whiterose.ac.uk/35205/

Adarkwah, M. A. (2021). The power of assessment feedback in teaching and learning: A narrative review and synthesis of the literature. SN Social Sciences, 1(75). https://doi.org/10.1007/s43545-021-00086-w

Alam, A. (2023). Harnessing the power of AI to create intelligent tutoring systems for enhanced classroom experience and improved learning outcomes. In G. Rajakumar, K. L. Du, & A. Rocha (Eds.). Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 171. Springer, Singapore. https://doi.org/10.1007/978-981-99-1767-9_42

Biagini, G. (2025). Towards an AI-literate future: A systematic literature review exploring education, ethics, and applications. International Journal of Artificial Intelligence in Education 35, 2616–2666. https://doi.org/10.1007/s40593-025-00466-w

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa

Braun, V., Clarke, V., Hayfield, N., Davey, L., & Jenkinson, E. (2022). Doing reflexive thematic analysis. In S. Bager-Charleson & A. McBeath (Eds.), Supporting research in counselling and psychotherapy. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-13942-0_2

Brachten, F., Brünker, F., Frick, N. R., Ross, B., & Stieglitz, S. (2020). On the ability of virtual agents to decrease cognitive load: an experimental study. Information Systems and e-Business Management, 18, 187–207. https://doi.org/10.1007/s10257-020-00471-7

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8

Chawla, J. (2024). Generative AI for personalized education: Learning experiences. eNest.com. Retrieved from https://enestit.com/generative-ai-for-personalized-education-learning-experiences/

Chen, D., Wu, Y., Qian, L., & Zhou, Y. (2025). The impact of self-stigma on college students’ attitudes toward professional psychological help-seeking: serial-mediated effects of discrimination perceptions and core self-evaluations. Frontiers in Psychology, 16,1630323. https://doi.org/10.3389/fpsyg.2025.1630323

Cheng, H. L., Kwan, K. L. K., & Sevig, T. (2013). Racial and ethnic minority college students’ stigma associated with seeking psychological help: Examining psychocultural correlates. Journal of Counselling Psychology, 60(1), 98-111. https://doi.org/10.1037/a0031169

Chugh, P., & Jain, V. (2026). From adoption to augmentation: A TCCM analysis of generative AI in higher education. Quality and Quantity, 2026. https://doi.org/10.1007/s11135-026-02718-w

Clark, V. L. P., & Creswell, J. W. (2008). The mixed methods reader. Sage.

Davis, C. (2023). Enabling education in Australia: Emerging themes and shared understandings. In F. F. Padró, J. H. Green & D. Bull (Eds.), Widening participation in higher education. University development and administration. Springer Singapore. https://doi.org/10.1007/978-981-19-9553-8_27-2

de Man Y., Wieland-Jorna Y., Torensma B., de Wit K., Francke A. L., Oosterveld-Vlug M. G., & Verheij, R. A. (2023). Opt-In and opt-out consent procedures for the reuse of routinely recorded health data in scientific research and their consequences for consent rate and consent bias: Systematic review. Journal of Medical Internet Research, 25, e42131. https://doi.org/10.2196/42131

Dickinson, M. J., & Dickinson, D. A. (2015). Practically perfect in every way: Can reframing perfectionism for high-achieving undergraduates impact academic resilience?. Studies in Higher Education, 40(10), 1889-1903. https://doi.org/10.1080/03075079.2014.912625

Firat, M. (2023). What ChatGPT means for universities: Perceptions of scholars and students. Journal of Applied Learning and Teaching, 6(1), 57-63. https://doi.org/10.37074/jalt.2023.6.1.22

Garzón, J., Patiño, E., & Marulanda, C. (2025). Systematic review of artificial intelligence in education: trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), 84. https://doi.org/10.3390/mti9080084

Gebregziabher Hagos, H., Lesjak, D., & Flogie, A. (2025). Artificial intelligence in knowledge management for higher education: transformative impact, challenges, and future directions post-COVID-19. Journal of Information Systems Engineering & Management, 10(3s), 344-359. https://doi.org/10.52783/jisem.v10i3s.414

Gilbert, N. L. (2013). The challenges of starting and leading a charter school: Examining the risks, the resistance, and the role of adaptive leadership—An autoethnography. (1399573665) [Doctoral dissertation, University of Pennsylvania]. ProQuest LLC. https://www.proquest.com/openview/c24e13f99dbdd02abe2fec811f814c04/1?pq-origsite=gscholar&cbl=18750

Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational evaluation and policy analysis, 11(3), 255-274. https://doi.org/10.3102/01623737011003255

Guetterman, T. (2015). Descriptions of sampling practices within five approaches to qualitative research in education and the health sciences. Department of Educational Psychology: Faculty Publications 16(2), Article 25. https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1275&context=edpsychpapers

Hatcher, R. & Campbell, R. (2024). Literature review: The impact of AI-personalized tutors on academic ability, self-esteem, self-efficacy, socialization, and academic integrity. ISAR Journal of Arts, Humanities and Social Sciences 2(2), 72-78. https://isarpublisher.com/backend/public/assets/articles/1708487421-ISARJAHSS--1292024-Gallery-Script.pdf

Hodges, B., Bedford, T., Hartley, J., Klinger, C., Murray, N., O’Rourke, J., & Schofield, N. (2013). Enabling retention: Processes and strategies for improving student retention in university-based Enabling programs: Final report 2013. Australian Government Office for Learning and Teaching. Retrieved from https://enablingeducators.org/wp-content/uploads/2019/11/CG10_1697_Hodges_Report_2013.pdf

Hsu, P-F., Nguyen, T.(K)., Wang, C-Y., Huang, P-J. (2023). Chatbot commerce—How contextual factors affect chatbot effectiveness. Electron Markets 33, 14. https://doi.org/10.1007/s12525-023-00629-4

Hughes, S., & Noblit, G. (2017). Meta-ethnography of autoethnographies: A worked example of the method using educational studies. Ethnography and Education 12(2), 211-227. https://doi.org/10.1080/17457823.2016.1216322

Huang, W., Hew, K. F., & Fryer, L. K. (2022). Chatbots for language learning—Are they really useful? A systematic review of chatbot‐supported language learning. Journal of Computer Assisted Learning, 38(1), 237-257. https://doi.org/10.1111/jcal.12610

Iqbal, J., Hashmi, Z. F., Asghar, M. Z., & Abid, M. N. (2025). Generative AI tool use enhances academic achievement in sustainable education through shared metacognition and cognitive offloading among preservice teachers. Scientific Reports, 15, 16610. https://doi.org/10.1038/s41598-025-01676-x

Johnston, K. (2021). “I am an enabling success story”: An autoethnographic narrative of an unskilled mother’s foray into academia. International Studies in Wider Education, 8(1), 22-31. https://novaojs.newcastle.edu.au/ceehe/index.php/iswp/article/view/151

Jones, M., & Worrall, L. (2025). Using artefacts as a research method to explore effective mentoring in teacher education. Practice, 7(2), 98–112. https://doi.org/10.1080/25783858.2024.2424207

Jose, B., Cherian, J., Verghis, A. M., Varghise, S. M., & Joseph, S. (2025). The cognitive paradox of AI in education: between enhancement and erosion. Frontiers in Psychology, 16, Article 1550621. https://doi.org/10.3389/fpsyg.2025.1550621

Junghans, C., Feder, G., Hemingway, H., Timmis, A., & Jones, M. (2005). Recruiting patients to medical research: double blind randomised trial of “opt-in” versus “opt-out” strategies. BMJ, 331(7522), 940. https://doi.org/10.1136/bmj.38583.625613.AE

Karaman, P. (2021). The effect of formative assessment practices on student learning: A meta-analysis study. International Journal of Assessment Tools in Education, 8(4), 801-817. https://doi.org/10.21449/ijate.870300

Kaswan, K. S., Dhatterwal, J.S. & Ojha, R. P. (2024). AI in personalized learning. In A. Garg, B. V. Babu & V. E. Balas (Eds.), Advances in technological innovations in higher education. CRC Press.

Keleş, U. (2022a). Autoethnography as a recent methodology in applied linguistics: A methodological review. The Qualitative Report, 27(2), 448-474. https://doi.org/10.46743/2160-3715/2022.5131

Keleş, U. (2022b). In an effort to write a “good” autoethnography in qualitative educational research: A modest proposal. The Qualitative Report, 27(9), 2026-2045. https://doi.org/10.46743/2160-3715/2022.5662

Kerr, K. F., Marsh, T. L., & Janes, H. (2019). The importance of uncertainty and opt-in v. opt-out: Best practices for decision curve analysis. Medical Decision Making, 39(5), 491-492. https://doi.org/10.1177/0272989X19849436

Kim, J. & Lee, S-S. (2023). Are two heads better than one?: The effect of student‑AI collaboration on students’ learning task performance. TechTrends, 67(2), 365–375. https://doi.org/10.1007/s11528-022-00788-9

Kim, N., Kim, I. & Taylor, D. D. (2025). Stigma Among College Students: Associations with Help-Seeking and Social Support. International Journal for the Advancement of Counselling, 47, 852–867. https://doi.org/10.1007/s10447-025-09614-2

Koopman, W. J., Watling, C. J., & LaDonna, K. A. (2020). Autoethnography as a strategy for engaging in reflexivity. Global Qualitative Nursing Research, 7, 2333393620970508. https://doi.org/10.1177/2333393620970508

Kozlov, V., Levina, E. & Tregubova, T. (2022). Transformation of universities during the COVID-19 pandemic: Digitalization, new formats, “Re-education of Educators”. In S. Gonçalves & S. Majhanovich (Eds.). Pandemic, disruption and adjustment in higher education (pp. 31-45). Brill. https://doi.org/10.1163/9789004512672_003

Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers and Education: Artificial Intelligence, 5, Article 100178. https://doi.org/10.1016/j.caeai.2023.100178

Lee, D. (2024). AI-powered writing support: Writing feedback. Paper presented at Festival of Learning and Teaching. 14th June 2024, University of Adelaide, Adelaide, South Australia. https://www.researchgate.net/publication/381473241_AI-Powered_Writing_Support_Writing_Feedback

Liu, G., & Ma, C. (2024). Measuring EFL learners’ use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching, 18(2), 125-138. https://doi.org/10.1080/17501229.2023.2240316

Luckin, R., Holmes, W., Griffiths, M. & Forcier, L. B. (2016). Intelligence unleashed. An argument for AI in education. Pearson. https://discovery.ucl.ac.uk/id/eprint/1475756/

Mao, J., Romero-Hall, E. & Reeves, T. C. (2023). Autoethnography as a research method for educational technology: A reflective discourse. Educational Technology Research and Development, 2023. https://doi.org/10.1007/s11423-023-10281-6

Maringe, F., & Jenkins, J. (2015). Stigma, tensions, and apprehension: The academic writing experience of international students. International Journal of Educational Management, 29(5), 609-626. https://doi.org/10.1108/IJEM-04-2014-0049

Matthews, M. L. (2020). My life in the arts: Challenges of a non-conformist professional path. [Doctoral Dissertation, University of South Florida] (28156542). https://www.proquest.com/docview/2474806391

Morris, R., Perry, T., & Wardle, L. (2021). Formative assessment and feedback for learning in higher education: A systematic review. Review of Education, 9(3), e3292. https://doi.org/10.1002/rev3.3292

Motwani, S., & Gupta, A. (2022). Makers’ Studio: Enabling education and skill development through ICT. IT Professional, 24(1), 18–26. https://doi.org/10.1109/MITP.2021.3089429

Ngo, T. T. A. (2023). The perception by university students of the use of ChatGPT in education. International Journal of Emerging Technologies in Learning (Online), 18(17), 4-9. https://doi.org/10.3991/ijet.v18i17.39019

Ntumi, S., Upoalkpajor, J-L. N. & Nimo, D. G. (2025). Culturally responsive assessment of help-seeking behavior among university students: a mediation-moderation analysis of cultural norms, mental health stigma, and digital engagement across cross-cultural contexts. BMC Psychology, 13, 922. https://doi.org/10.1186/s40359-025-03256-0

Pasupuleti, R. V. (2013). Cultural factors, stigma, stress, and help-seeking attitudes among college students. [Doctoral dissertation, University of Rhode Island] Open Access Dissertations. https://digitalcommons.uri.edu/oa_diss/114

Pithouse-Morgan, K., Pillay, D., & Naicker, I. (2021). Autoethnography as/in higher education. In T. E. Adams, S. H. Jones, & C. Ellis (Eds.), Handbook of autoethnography (pp. 215-227). Routledge.

Proudfoot, K. (2023). Inductive/deductive hybrid thematic analysis in mixed methods research. Journal of Mixed Methods Research, 17(3), 308-326. https://doi.org/10.1177/15586898221126816

Rizvi, M. (2023). Investigating AI-powered tutoring systems that adapt to individual student needs, providing personalized guidance and assessments. The Eurasia Proceedings of Educational and Social Sciences, 31, 67-73. https://doi.org/10.55549/epess.1381518

Robin, B. R. (2016). The power of digital storytelling to support teaching and learning. Digital Education Review, 30, 17-29. https://files.eric.ed.gov/fulltext/EJ1125504.pdf

Rostami, M., & Mehdi Abadi, P. (2023). The impact of doing assignments with chatbots on the students’ working memory. Health Nexus, 1(1), 64-70. https://journals.kmanpub.com/index.php/Health-Nexus/article/view/1083/1304

Ruihua, L., Che Hassan, N. & Saharuddin, N. (2025). Understanding academic help-seeking among first-generation college students: a phenomenological approach. Humanities and Social Sciences Communications, 12, 56. https://doi.org/10.1057/s41599-024-04165-0

Sakshaug, J. W., Schmucker, A., Kreuter, F., Couper, M. P., & Singer, E. (2016). Evaluating active (opt-in) and passive (opt-out) consent bias in the transfer of federal contact data to a third-party survey agency. Journal of Survey Statistics and Methodology, 4(3), 382–416. https://doi.org/10.1093/jssam/smw020

Schei, O. M., Møgelvang, A., Ludvigsen, K. (2024). Perceptions and use of AI chatbots among students in higher education: A scoping review of empirical studies. Education Sciences, 14(8), Article 922. https://doi.org/10.3390/educsci14080922

Smeda, N., Dakich, E., & Sharda, N. (2014). The effectiveness of digital storytelling in the classrooms: A comprehensive study. Smart Learning Environments, 1, Article 6. https://doi.org/10.1186/s40561-014-0006-3

Strugnell, C., Orellana, L., Hayward, J., Millar, L., Swinburn, B., & Allender, S. (2018). Active (opt-in) consent underestimates mean BMI-z and the prevalence of overweight and obesity compared to passive (opt-out) consent. Evidence from the healthy together Victoria and childhood obesity study. International Journal of Environmental Research and Public Health, 15(4), Article 747. https://doi.org/10.3390/ijerph15040747

Steiner, S. (2018). How using autoethnography improved my teaching. The Scholarly Teacher. (Jan 26). https://www.scholarlyteacher.com/post/autoethnography-to-improve-teaching

Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2023.2209881

Syzdykbayeva, A., Baikulova, A., & Kerimbayeva, R. (2021). Introduction of artificial intelligence as the basis of modern online education on the example of higher education. In 2021 IEEE International Conference on Smart Information Systems and Technologies (pp. 1-8). IEEE. https://doi.org/10.1109/SIST50301.2021.9465974

Villamin, P., Lopez, V., Thapa, D. K., & Cleary, M. (2024). A worked example of qualitative descriptive design: A step‑by‑step guide for novice and early career researchers. Journal of Advanced Nursing, 81(8), 5181-5195. https://onlinelibrary.wiley.com/doi/10.1111/jan.16481

Zeng, F., John, W. C. M., Qiao, D., & Sun, X. (2023). Association between psychological distress and mental help-seeking intentions in international students of National University of Singapore: A mediation analysis of mental health literacy. BMC Public Health, 23, 2358. https://doi.org/10.1186/s12889-023-17346-4

Zhang, M., & Yang, X. (2024). Google or ChatGPT: Who is the better helper for university students. Education and Information Technologies, 30, 5177–5198. https://doi.org/10.1007/s10639-024-13002-5

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Published

2026-08-03

How to Cite

Lee, D., & Robinson, A. (2026). AI as a Personal Tutor: Enabling Students to Access Smart Tools. Journal of Research in Education and Pedagogy, 3(3), 578–595. https://doi.org/10.70232/jrep.v3i3.213

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