Student Perceptions of Personalised GenAI Agents on Student Learning, Success, Engagement, and Wellbeing: A Pilot Study of Cogniti AI
Abstract
In the competitive higher education landscape, Generative AI (GenAI) tools are rapidly emerging as a potential tool for enhancing student learning, engagement, success, and wellbeing. This pilot study investigated undergraduate students’ perceptions of personalised GenAI academic agents as on-demand academic support during assessment preparation across four university courses (education, psychology, social work, and sport). In total, 139 students (18–55 years) completed surveys before and after the deployment of an academic agent. The survey assessed students’ perceptions of the influence of GenAI agents on their learning, engagement, success, and wellbeing with regard to a singular assessment, with quantitative data analysed using Mann-Whitney U tests and qualitative responses analysed using a content analysis method. Results indicated significant improvements in students’ perceptions of their learning and success ( p = 0.04 for both), with no significant changes observed for engagement or wellbeing. Qualitative insights suggest that students perceive value and benefit in having access to 24/7 academic support, increased confidence in assignment preparation, and reduced stress, although some students also emphasised the importance of maintaining human interactions between academics and students. The findings indicate that Cogniti AI academic agents have the potential to enhance targeted aspects of students’ experiences, particularly with respect to student learning and success, when carefully integrated into higher education settings. Conceptual, pedagogical, and institutional implications are discussed to ensure these GenAI agents complement, rather than replace, human-centred learning.
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Students emphasize that GenAI tools should supplement rather than replace human teaching and interaction.
“However, students emphasize that these tools should supplement rather than replace human teaching and interaction [6].”
From Engineering student perceptions of generative AI use in learning trust judgment and information seeking (2026) · cites this paper as [6]
Generative AI is often discussed as a resource that may support learning, improve efficiency, and expand access to educational assistance.
“Consequently, GenAI is often discussed as a resource that may support learning, improve efficiency, and expand access to educational assistance [5,6].”
From Engineering student perceptions of generative AI use in learning trust judgment and information seeking (2026) · cites this paper as [6]
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Cites
School Engagement: Potential of the Concept, State of the Evidence
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004) · Review of Educational Research · doi:10.3102/00346543074001059 · Cited by 12,581 · see citing works
Students’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301 · see citing works
BAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 1,826 · see citing works
The Influence of ChatGPT on Student Engagement: A Systematic Review and Future Research Agenda
Lo, C. K., Hew, K. F., & Jong, M. S.-Y. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105100 · Cited by 392 · see citing works
McDonald, N., Johri, A., Ali, A., & Collier, A. H. (2025) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2025.100121 · Cited by 295 · see citing works
Singh, H., Tayarani-Najaran, M.-H., & Yaqoob, M. (2023) · Education Sciences · doi:10.3390/educsci13090924 · Cited by 150 · see citing works
Rawas, S. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00118-3 · Cited by 119 · see citing works
Flagged first, then by citations
Research works printed but not linked: 21
- Ahmed, M. A. (2023). ChatGPT and the EFL classroom: Supplement or substitute in Saudi Arabia's Eastern region. Information Sciences Letters, 12(7), 2727-2734.
- Arum, R., & Roksa, J. (2011). Academically adrift: Limited learning on college campuses. University of Chicago Press.
- Bloom, B. S. (1984). The 2 Sigma problem: The search for methods of group instruction as effective as one- to-one tutoring. Educational Researcher, 13(6), 4-16.
- Du Boulay, B. (2019). Escape from the Skinner box: The case for contemporary intelligent learning environ- ments. British Journal of Educational Technology, 50(6), 2902-2919.
- Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), 1109.
- Hasanein, A. M., & Sobaih, A. E. E. (2023). Drivers and consequences of ChatGPT use in higher education: Key stakeholder perspectives. European Journal of Investigation in Health Psychology and Education, 13(11), 2599-2614. h t t p s : / / p m . i . n l m . n i h . / /
- Ilieva, G., Yankova, T., Klisarova-Belcheva, S., Dimitrov, A., Bratkov, M., & Angelov, D. (2023). Effects of generative chatbots in higher education. Information, 14(9), 492.
- Jain, K. K., & Raghuram, J. (2024). Gen-AI integration in higher education: Predicting intentions using SEM-ANN approach. Education and Information Technologies, 29(13), 17169-17209.
- Johnson, S. N., & Gallagher, E. D. (2021). An early exploration of undergraduate student definitions of learn- ing, memorizing, studying, and Understanding. Advances in Physiology Education, 45(2), 342-352. https://doi.org/10.1152/advan.00082.2020. https://journals.physiology.org/doi/pdf/ · DOI
- Jones, E., Priestley, M., Brewster, L., Wilbraham, S. J., Hughes, G., & Spanner, L. (2021). Student wellbeing and assessment in higher education: The balancing act. Assessment & Evaluation in Higher Education, 46(3), 438-450.
- Kubullek, A. K., Kumaç, N., & Dogangün, A. (2024). Understanding the adoption of ChatGPT in higher education: A comparative study with insights from STEM and bausiness students. In Proceedings of mensch und computer 2024 (pp. 684-689).
- Laari, L. (2025). Inductive-Deductive qualitative data analysis logic in health sciences research: A frame- work for analysing qualitative data. International Journal of Qualitative Methods, 24. h t t p s : / / d o i . o r g / 1 0 . 1 1 7 7 / 1 6 0 9 4 0 6 9 2 5 1 3 8 1 7 0 6 · DOI
- Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410.
- Mavrikis, M., Cukurova, M., Di Mitri, D., Schneider, J., & Drachsler, H. (2021). A short history, emerging challenges and co-operation structures for artificial intelligence in education. Bildung Und Erziehung (Stuttgart Germany), 74(3), 249-263. https://doi.org/10.13109/buer.2021.74.3.249 · DOI
- McLean, H. (2018). This is the way to teach: Insights from academics and students about assessment that supports learning. Assessment and Evaluation in Higher Education, 43(8), 1228-1240.
- Mor, Y., & Craft, B. (2012). Learning design: Reflections upon the current landscape. Research in Learning Technology, 20(sup1), 85-94. https://doi.org/10.3402/rlt.v20i0.19196 · DOI
- Prowse, A., Ruiz Vargas, V., & Powell, S. (2021). Design considerations for personalised supported learning: Implications for higher education. Journal of Further and Higher Education, 45(4), 497-510. h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 0 3 0 9 8 7 7 X . 2 0 2 0 . 1 7 8 9 9 1 · DOI
- Stone, C. (2022). From the margins to the mainstream: The online learning rethink and its implications for enhancing student equity. Australasian Journal of Educational Technology, 38(6), 139-149.
- Wang, N., Wang, X., & Su, Y. S. (2024). Critical analysis of the technological affordances, challenges and future directions of generative AI in education: A systematic review. Asia Pacific Journal of Education, 44(1), 139-155.
- White, M. A., & McCallum, F. (2021). Wellbeing and resilience education: COVID-19 and its impact on education. Routledge.
- Yusuf, A., Pervin, N., Román-González, M., & Noor, N. M. (2024). Generative AI in education and research: A systematic mapping review. Review of Education, 12(2), e3489.
Cited by
Sorted by citations
Mohammed, Y., Qandil, H., & Lund, B. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02086-2
- Authors
- Elliott, S., Richter, M., Alozie, C., & Raidis, D.
- ORCID
- Elliott, Sam
- Venue
- Technology, Knowledge and Learning
- Published
- 2026-02-10
- ISSN
- 2211-1662
- 2211-1670
- Cited by
- 2
- References
- 7
- Language
- en
- Funders
- Flinders University
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