The impact of dynamic feedback on AI tutor performance

Introduction. Behavioural adaptive AI systems demonstrate significant potential in providing timely learning support. However, a key aspect of their operation, dynamically switching behavioural patterns based on real-time analysis of streaming data from learners, remains an area requiring further research. Aim. The aim of this study is to assess the impact of a transparent switching policy, based on sentiment and response latency, on student engagement, trust, and academic outcomes, as well as to examine its effect on response latency and expressed sentiment. Methodology and research methods. The authors conducted a randomised, real-world study involving 80 students during a 45-minute session. The experiment compared a dynamic-persona tutor with a fixed-persona baseline tutor. To evaluate the results, the following measures were used: a five-item engagement scale, a five-item trust scale, a curriculum-aligned ten-item pre- and post-knowledge test, log-level tutor-to-learner response latency, and message-level sentiment analysis mapped by a transformer classifier onto a polarity scale ranging from -1 to +1. The role-change algorithm operated as follows: if the rolling mean of sentiment was at or below -0.30, the tutor adopted the role of Empathic Coach; if response latency exceeded ten seconds, the tutor assumed the role of Rational Guide; in all other cases, the tutor remained a Neutral Instructor. Following a role change, there was a one-turn “cooldown” period, and a return to the neutral role occurred after two consecutive stable interactions. Results and scientific novelty. The authors developed a testable role-switching algorithm that selects the optimal interaction strategy in real-time by analysing the learner’s emotional state and response latency. The efficacy of this approach was confirmed in a real-world educational setting. Practical significance. This approach provides a ready-made solution for implementing adaptive learning in real-world educational settings. Its advantages include simple rules, low computational costs, and a transparent auditing system.

1. Lacárcel A.M. Artificial intelligence in education as a means to personalize learning. In: Handbook of Research on Artificial Intelligence in Government Practices and Processes. IGI Global Scientific Publishing; 2022:285–308. doi:10.4018/978-1-7998-9609-8.ch016

2. Khazanchi R., Khazanchi P. Artificial intelligence in education: a closer look into intelligent tutoring systems. In: Singh A., Yeh C.J., Blanchard S., Anunciação L., eds. Handbook of Research on Criti cal Issues in Special Education for School Rehabilitation Practices. Information Science Reference/IGI Global; 2021:256–277. doi:10.4018/978-1-7998-7630-4.CH014

3. Kim W., Kim J.-H. Individualized AI tutor based on developmental learning networks. IEEE Access. 2020;8:35862–35873. doi:10.1109/ACCESS.2020.2972167

4. Kokku R., Sundararajan S., Dey P., Sindhgatta R., Nitta S.V., Sengupta B. Augmenting classrooms with AI for personalized education. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Calgary, AB, Canada; 2018:6976–6980. doi:10.1109/ICASSP.2018.8461812

5. Karahasanovic A., Følstad A., Schittekat P. Putting a face on algorithms: personas for modeling artificial intelligence. In: Degen H., Ntoa S., eds. Artificial Intelligence in HCI. HCII 2021. Lecture Notes in Computer Science, vol. 12797. Cham: Springer; 2021:229–240. doi:10.1007/978-3-030-77772-2_15

6. Drobnjak A., Boticki I., Seow P., Kahn K. Learning with conversational AI and personas: a systematic literature review. In: International Conference on Computers in Education. 2023. doi:10.58459/icce.2023.1390

7. Vieriu A.M., Petrea G. The impact of artificial intelligence (AI) on students’ academic development. Education Sciences. 2025;15:343. doi:10.3390/educsci15030343

8. Bassner P., Frankford E., Krusche S. Iris: an AI-driven virtual tutor for computer science education. In: ITiCSE 2024: Innovation and Technology in Computer Science Education. 2024;1:394–400. doi:10.1145/3649217.3653543

9. Sangkala I., Sulaymanova Mardonovna N. Artificial intelligence as a personalized tutor in language learning: a systematic review. Klasikal: Journal OF Education, Language Teaching AND Science. 2024;6(2):565–576. doi:10.52208/klasikal.v6i2.1193

10. Zulpykhar Z., Kariyeva K., Sadvakassova A., Zhilmagambetova R., Nariman S. Assessing the effectiveness of personalized adaptive learning in teaching mathematics at the college level. International Journal of Engineering Pedagogy (iJEP). 2025;15(4):4–22. doi:10.3991/ijep.v15i4.52797

11. Fernández-Herrero J. Evaluating recent advances in affective intelligent tutoring systems: A scoping review of educational impacts and future prospects. Education Sciences. 2024;14(8):839. doi:10.3390/educsci14080839

12. Yuvaraj R. Affective computing for learning in education. Education Sciences. 2025;15(1):65. doi:10.3390/educsci15010065

13. Ilić J., Ivanović M., Klašnja-Milićević A. The impact of intelligent tutoring systems and artificial intelligence on students’ motivation and achievement in STEM education: a systematic review. Journal of Educational Studies in Mathematics and Computer Science. 2024;1(2):5–18. doi:10.5937/JESMAC2402005I

14. Fredricks J.A., Blumenfeld P.C., Paris A.H. School engagement: potential of the concept, state of the evidence. Review of Educational Research. 2004:74(1):59–109. doi:10.3102/00346543074001059

15. Lee J.D., See K.A. Trust in automation: designing for appropriate reliance. Human Factors. 2004:46(1):50–80. doi:10.1518/hfes.46.1.50_30392

16. Gomes D. A comprehensive study of advancements in intelligent tutoring systems through artificial intelligent education platforms. In: Moreira F., Teles R., eds. Improving Student Assessment With Emerging AI Tools. IGI Global Scientific Publishing; 2025:213–244. doi:10.4018/979-8-3693-6170-2.ch008

17. Gan W., Sun Y., Ye S., Fan Y., Sun Y. AI-tutor: generating tailored remedial questions and answers based on cognitive diagnostic assessment. In: 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC). Beijing, China; 2019:1–6. doi:10.1109/BESC48373.2019.8963236

18. Makharia R., Kim Y.C., Su B., Kim M.A., Jain A., Agarwal P., et al. AI tutor enhanced with prompt engineering and deep knowledge tracing. In: 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). Gwalior, India; 2024:1–6. doi:10.1109/IATMSI60426.2024.10503187

19. Chen X., Xie H., Hwang G.-J. A multi-perspective study on artificial intelligence in education: grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence. 2020;1:100005. doi:10.1016/j.caeai.2020.100005

20. Wolf T., Debut L., Sanh V., Chaumond J., Delangue C., Moi A., et al. Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics; 2020:38– 45. doi:10.18653/v1/2020.emnlp-demos.6

21. Belkina M., Daniel S., Nikolic S., Haque R., Lyden S., Neal P., et al. Implementing generative AI (GenAI) in higher education: a systematic review of case studies. Computers and Education: Artificial Intelligence. 2025;8:100407. doi:10.1016/j.caeai.2025.100407

22. Lata P. Beyond algorithms: humanizing artificial intelligence for personalized and adaptive learning. International Journal of Innovative Research in Engineering and Management. 2024;11(5):40–47. doi:10.55524/ijirem.2024.11.5.6

23. Bibauw S., François T., Desmet P. Dialogue systems for language learning: chatbots and beyond. In: Ziegler N., González-Lloret M., eds. The Routledge Handbook of Second Language Acquisition and Technology. Routledge; 2023:121–134. doi:10.4324/9781351117586-12

24. Baumgart A., Mamlouk A.M. A Knowledge-model for AI-driven tutoring systems. In: Tropmann-Frick M., Thalheim B., Jaakkola H., Kiyoki Y., Yoshida N., eds. Information Modelling and Knowledge Bases XXXIII (Frontiers in Artificial Intelligence and Applications, Vol. 343). IOS Press; 2022:1–18. doi:10.3233/FAIA210474

25. Sparks J.R., Lehman B., Zapata-Rivera D. Caring assessments: challenges and opportunities. Frontiers in Education. 2024;9:1216481. doi:10.3389/feduc.2024.1216481

26. Kim B., Research A.I., Suh H., Heo J., Choi Y. AI-driven interface design for intelligent tutoring system improves student engagement. arXiv preprint arXiv:2009.08976. 2020. doi:10.48550/arXiv.2009.08976

27. Braun V., Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006;3(2):77–101. doi:10.1191/1478088706qp063oa

28. El Gounidi R., Chafiq N., Talbi M., Zahar O. Evaluating the impact of metaverse integration on academic performance and engagement in primary education: a case study of Medersat.com Bouskoura. In: Ireland International Conference on Education (IICE-2024). Dublin, Ireland; 2024. doi:10.20533/iice.2024.10.0017