Students’ Perceptions of AI Digital Assistants (AIDAs): Should Institutions Invest in Their Own AIDAs?
Source abstract
Abstract
This mixed methods study explores 315 distance learning students’ perceptions of artificial intelligence digital assistants (AIDAs) over an 11-month period in three distinct studies. The research investigates student perspectives on both a publicly available AI digital assistant (p-AIDA), such as ChatGPT, and a potential institutionally developed AI digital assistant (i-AIDA). Findings indicate that students highly valued 24/7 immediate academic feedback and the personalisation of the i-AIDA, and these perspectives remained largely stable across the three studies. However, concerns about academic integrity, data privacy, and ethical implications persisted across the studies. A cluster analysis identified three distinct student groups of highly critical, supportive, and keenly supportive learners, with key differences based on prior GenAI experience, educational background, and age. This study underscores both the potential and challenges of developing institutional AI solutions to enhance student learning while addressing privacy and ethical concerns.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · AI in Service Interactions · E-Learning and Knowledge Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Educational Technology, The Open University, Milton Keynes MK7 6AA, UK
Knowledge Media Institute, The Open University, Milton Keynes MK7 6AA, UK
References (25)
Show source-provided references
1. ChatGPT for good? On opportunities and challenges of large language models for education10.1016/j.lindif.2023.102274 ↗
2. Impact of AI assistance on student agency10.1016/j.compedu.2023.104967 ↗
3. Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., Järvelä, S., Mavrikis, M., and Rienties, B. (2024). The promise and challenges of generative AI in education. Behav. Inf. Technol., 1–27.10.1080/0144929X.2024.2394886 ↗
4. Jahić, I., Ebner, M., Schön, S., and Edelsbrunner, S. (2024). Exploring the Use of Generative AI in Education: Broadening the Scope, Springer.10.1007/978-3-031-61691-4_19 ↗
5. Jin, Y., Yang, K., Yan, L., Echeverria, V., Zhao, L., Alfredo, R., Milesi, M., Fan, J.X., Li, X., and Gasevic, D. (2025, January 3–7). Chatting with a Learning Analytics Dashboard: The Role of Generative AI Literacy on Learner Interaction with Conventional and Scaffolding Chatbots. Proceedings of the 15th International Learning Analytics and Knowledge Conference, Dublin, Ireland.10.1145/3706468.3706545 ↗
6. AI for data generation in education: Towards learning and teaching support at scale10.1111/bjet.13580 ↗
7. Sedrakyan, G., Borsci, S., van den Berg, S.M., van Hillegersberg, J., and Veldkamp, B.P. (2024). Design Implications for Next Generation Chatbots with Education 5.0, Springer.10.1007/978-981-97-3883-0_1 ↗
8. Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N.A., Iahad, N., Abdulla, A.A., Akopyan, A., Aldana Segura, M.W., and AlHumaid, J. (2025). Higher education students’ perceptions of ChatGPT: A global study of early reactions. PLoS ONE, 20.10.1371/journal.pone.0315011 ↗
9. Freeman, J. (2025). Student Generative AI Survey 2025, Higher Education Policy Institute.
10. Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies10.1016/j.compedu.2024.105224 ↗
11. Shibani, A., Knight, S., Kitto, K., Karunanayake, A., and Buckingham Shum, S. (2024, January 11–16). Untangling Critical Interaction with AI in Students’ Written Assessment. Proceedings of the Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.10.1145/3613905.3651083 ↗
12. Chung, J., Henderson, M., Pepperell, N., Slade, C., and Liang, Y. (2024). Student Perspectives on AI in Higher Education: Student Survey, Monash University.
13. Youth Expectations and Perceptions of Generative Artificial Intelligence in Higher Education
14. Challenging the status quo and exploring the new boundaries in the age of algorithms: Reimagining the role of generative AI in distance education and online learning
15. Rienties, B., Domingue, J., Duttaroy, S., Herodotou, C., Tessarolo, F., and Whitelock, D. (2024). What distance learning students want from an AI Digital Assistant. Distance Educ., 1–17.10.1080/01587919.2024.2338717 ↗
16. The engagement of university teachers with predictive learning analytics10.1016/j.compedu.2021.104285 ↗
17. A tour of Open Universities through literature
18. From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations10.1109/TLT.2018.2823317 ↗
19. How group structure, members’ interactions and teacher facilitation explain the emergence of roles in collaborative learning10.1016/j.lindif.2024.102463 ↗
20. Mohd Talib, N.I., Abd Majid, N.A., and Sahran, S. (2023). Identification of Student Behavioral Patterns in Higher Education Using K-Means Clustering and Support Vector Machine. Appl. Sci., 13.10.3390/app13053267 ↗
21. An institutional AI digital assistant: What do distance learners expect and value?10.5334/uproc.129 ↗
22. Rienties, B., Tessarolo, F., Coughlan, T., Herodotou, C., Domingue, J., and Whitelock, D. (2025). A Design-Based Research Approach to what distance learners expect and value from an Institutional AI Digital Assistant. Eur. J. Open Distance E-Learn.
23. Freeman, J. (2024). Provide or Punish? Students’ Views on Generative AI in Higher Education, Higher Education Policy Institute.
24. Exploring the Use of Artificial Intelligence for Qualitative Data Analysis: The Case of ChatGPT10.1177/16094069231211248 ↗
25. Hamilton, A., Wiliam, D., and Hattie, J. (2023). The Future of AI in Education: 13 Things We Can Do to Minimize the Damage. 2023. EdArXiv Preprints.10.35542/osf.io/372vr ↗