Strategies of Intelligent Tutoring Systems to Engage Students in Online Learning Before LLM Approaches

• Aluisio José Pereira UFPE
• Leandro Marques Queiros UFPE
• Alex Sandro Gomes UFPE
• Tiago Thompsen Primo UFPel

DOI: https://doi.org/10.5753/sbsi.2025.246479

Resumo

Context: Several studies have investigated the integration of Intelligent Tutoring Systems (ITS) in education. However, there is still a gap in understanding approaches that promote student engagement in online learning. Problem: The literature lacks specific analyses that clarify the role of ITS in supporting student engagement in online learning environments, particularly before the advent of Large Language Models (LLM). Solution: This study analyzes the (inter)national literature to identify indicators of ITS contributions to student engagement, focusing on solutions implemented prior to the adoption of LLM. ITS Theory: The research is grounded in ITS theory by addressing ITS as mediating tools in online learning interactions, emphasizing their potential for enhancement through novel technological approaches. Method: Relevant articles from the literature were selected and reviewed to map themes addressed by ITS, identify the main types of solutions, and evaluate their implications for future ITS designs. Summary of Results: The results highlight the themes explored by ITS, the primary solutions developed, and their implications. Among the 15 studies analyzed in the Brazilian context, they also emphasize the potential of combining earlier and current solutions while maintaining the crucial role of human tutors in the teaching-learning process. Contributions and Impact on ITS: This study advances the ITS field by offering theoretical and practical insights for designing ITS that integrate traditional and modern approaches. By focusing on the relationship between ITS and student engagement, the research contributes to the development of tools that enhance online learning effectiveness and foster better interactions between students, systems, and human tutors.

Palavras-chave: Intelligent Tutoring Systems, engagement, student e-learning, Large Language Models

Referências

Wang, W.; Siau, K. 2019. Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda. Journal of Database Management (JDM), 30(1), 61-79.

Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., and Wanko, C. E. 2020. Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. DOI: 10.1108/BPMJ-10-2019-0411

Abdallah, M., Talib, M. A., Feroz, S., Nasir, Q., Abdalla, H., and Mahfood, B. 2020. Artificial intelligence applications in solid waste management: A systematic research review. Waste Management, 109(1), 231-246. DOI: 10.1016/j.wasman.2020.04.057

Sadiq, R. B., Safie, N., Abd Rahman, A. H., and Goudarzi, S. 2021. Artificial intelligence maturity model: a systematic literature review. PeerJ Computer Science, v. 7, e661. DOI: 10.7717/peerj-cs.661

Mithas, S., Chen, Z. L., Saldanha, T. J., and De Oliveira Silveira, A.. 2022. How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?. Production and Operations Management, 31(12), 4475-4487. DOI: 10.1111/poms.13864

Chiu, T. K., Xia, Q., Zhou, X., Chai, C. S., and Cheng, M. 2022. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 100-118. DOI: 10.1016/j.caeai.2022.10011

Kar, A. K.; Choudhary, S. K.; Singh, V. K. 2022. How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production, 134120. DOI: 10.1016/j.jclepro.2022.134120

Alshaikh, F.; Hewahi, N. 2021. Ai and machine learning techniques in the development of Intelligent Tutoring System: A review. In: 2021 International Conference on innovation and Intelligence for informatics, computing, and technologies (3ICT). IEEE. 403-410. DOI: 10.1109/3ICT53449.2021.9582029

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... and Amodei, D. 2020. Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems. 33(1). DOI: 10.48550/arXiv.2005.14165

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... and Lowe, R. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35(1), 27730-27744. DOI: 10.48550/arXiv.2203.02155

Team, G., Anil, R., Borgeaud, S., Alayrac, J. B., Yu, J., Soricut, R., ... and Blanco, L. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805. DOI: 10.48550/arXiv.2312.11805

Phillips, A. 2020. Implementação de um sistema de tutoria inteligente adaptativa como um suplemento instrucional. Pesquisa e Desenvolvimento em Tecnologia Educacional, 68(3), 1409-1437. DOI: 10.1007/s11423-020-09745-w

Chen, X., Zou, D., Xie, H., Cheng, G., and Liu, C. 2022. Two Decades of Artificial Intelligence in Education. Educational Technology & Society, 25(1), 28-47. [link]

Latham, A. 2022. Conversational Intelligent Tutoring Systems: The State of the Art. Women in Computational Intelligence: Key Advances and Perspectives on Emerging Topics, 77-101. DOI: 10.1007/978-3-030-79092-9_4

Pink, S., Gomes, A., Zilse, R., Lucena, R., Pinto, J., Porto, A., ... and De Oliveira, M. D. 2018. Automated and connected? Smartphones and automobility through the global south. Applied Mobilities. DOI: 10.1080/23800127.2018.1505263

Chen, F.; Terken, J. 2022. Design Process. In: Automotive Interaction Design: From Theory to Practice. Singapore: Springer Nature Singapore. 165-179. DOI: 10.1007/978-981-19-3448-3_10

Salas‐Pilco, S. Z.; Yang, Y.; Zhang, Z. 2022. Student engagement in online learning in Latin American higher education during the COVID‐19 pandemic: A systematic review. British Journal of Educational Technology, 53(3), 593-619. DOI: 10.1111/bjet.13190

Kitchenham, B. 2004. Procedures for performing systematic reviews. Keele, UK, Keele University, 33(4), 1-26.

Abrami, P. C., Bernard, R. M., Bures, E. M., Borokhovski, E., and Tamim, R. M. 2011. Interaction in distance education and online learning: Using evidence and theory to improve practice. Journal of computing in higher education, 23(2-3), 82-103. DOI: 10.1007/s12528-011-9043-x

Martins, R. M.; Wangenheim, C. G. V. 2022. Findings on Teaching Machine Learning in High School: A Ten-Year Systematic Literature Review. Informatics in Education. DOI: 10.15388/infedu.2023.18

Lee, A. V. Y. 2021. Determining quality and distribution of ideas in online classroom talk using learning analytics and machine learning. Educational Technology & Society, 24(1), 236-249. [link]

Song, P; Wang, X. 2020. A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), 473-486. DOI: 10.1007/s12564-020-09640-2

Cuéllar-Rojas, O. A., Hincapié, M., Contero, M., and Güemes-Castorena, D. 2021. Intelligent Tutoring System: A Bibliometric Analysis and Systematic Literature Review. DOI: 10.21203/rs.3.rs-673038/v1

Egghe, L. 2006. Theory and practise of the g-index. Scientometrics, 69(1), 131-152.

Shute, V. J.; Psotka, J. 1994. Intelligent Tutoring Systems: Past, Present, and Future. ARMSTRONG LAB BROOKS AFB TX HUMAN RESOURCES DIRECTORATE.

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., and Lim, W. M. 2021. How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133(1), 285-296. DOI: 10.1016/j.jbusres.2021.04.070

Chen, H.; Park, H. W.; Breazeal, C. 2020. Teaching and learning with children: Impact of reciprocal peer learning with a social robot on children’s learning and emotive engagement. Computers & Education, 150(1), 103836. DOI: 10.1016/j.compedu.2020.103836

Ashwin, T. S.; GUDDETI, R. M. R. 2020. Affective database for e-learning and classroom environments using Indian students’ faces, hand gestures and body postures. Future Generation Computer Systems, 108(1), 334-348. DOI: 10.1016/j.future.2020.02.075

Chrysafiadi, K., Virvou, M., Tsihrintzis, G. A., and Hatzilygeroudis, I. 2022. Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy-based intelligent tutoring system for computer programming for academic students in Greece. Education and Information Technologies, 1-31. DOI: 10.1007/s10639-022-11444-3

D'mello, S. K.; Graesser, A. 2012. Language and discourse are powerful signals of student emotions during tutoring. IEEE Transactions on Learning Technologies, 5(4), 304-317. DOI: 10.1109/TLT.2012.10

Ruiz, N., Yu, H., Allessio, D. A., Jalal, M., Joshi, A., Murray, T., ... and Betke, M. 2022. ATL-BP: a student engagement dataset and model for affect transfer learning for behavior prediction. IEEE Transactions on Biometrics, Behavior, and Identity Science. DOI: 10.1109/TBIOM.2022.3210479

Liu, H. L., Wang, T. H., Lin, H. C. K., Lai, C. F., and Huang, Y. M. 2022. The Influence of Affective Feedback Adaptive Learning System on Learning Engagement and Self-Directed Learning. Frontiers in Psychology, 13(1). DOI: 10.3389/fpsyg.2022.858411

Baothman, F. A. 2021. An intelligent big data management system using haar algorithm-based Nao agent multisensory communication. Wireless Communications and Mobile Computing, v. 2021. DOI: 10.1155/2021/9977751

Tahir, F.; Mitrovic, A.; Sotardi, V. 2022. Investigating the causal relationships between badges and learning outcomes in SQL-Tutor. Research and Practice in Technology Enhanced Learning, 17(1), 1-23. DOI: 10.1186/s41039-022-00180-4

Aslan, S., Agrawal, A., Alyuz, N., Chierichetti, R., Durham, L. M., Manuvinakurike, R., ... and Nachman, L. 2022. Exploring Kid Space in the wild: a preliminary study of multimodal and immersive collaborative play-based learning experiences. Educational technology research and development, 70(1), 205-230. DOI: 10.1007/s11423-021-10072-x

Panzoli, D., Qureshi, A., Dunwell, I., Petridis, P., De Freitas, S., and Rebolledo-Mendez, G. 2010. Levels of interaction (loi): a model for scaffolding learner engagement in an immersive environment. In: Intelligent Tutoring Systems: 10th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14-18, 2010, Proceedings, Part II 10. Springer Berlin Heidelberg. 393-395. DOI: 10.1007/978-3-642-13437-1_81

Forsyth, C. M.; Graesser, A.; Millis, K. 2020. Predicting learning in a multi-component serious game. Technology, Knowledge and Learning, 25(2), 251-277. DOI: 10.1007/s10758-019-09421-w

Sætra, H. S. 2022. Scaffolding Human Champions: AI as a More Competent Other. Human Arenas, 1-23. DOI: 10.1007/s42087-022-00304-8

De Bruin, L. R. 2022. Collaborative learning experiences in the university jazz/creative music ensemble: Student perspectives on instructional communication. Psychology of Music, 50(4), 1039-1058. DOI: 10.1177/03057356211027651

Vuković, I., Kuk, K., Čisar, P., Banđur, M., Banđur, Đ., Milić, N., and Popović, B. 2021. Multi-agent system observer: Intelligent support for engaged e-learning. Electronics, 10(12), 1370. DOI: 10.3390/electronics10121370

Li, S., Lajoie, S. P., Zheng, J., Wu, H., and Cheng, H. 2021. Automated detection of cognitive engagement to inform the art of staying engaged in problem-solving. Computers & Education, 163(1), 104114. DOI: 10.1016/j.compedu.2020.104114

Ruan, S., He, J., Ying, R., Burkle, J., Hakim, D., Wang, A., ... and Landay, J. A. 2020. Supporting children's math learning with feedback-augmented narrative technology. In: Proceedings of the Interaction Design and Children Conference. 567-580. DOI: 10.1145/3392063.3394400

Xiao, X.; Wang, J. 2016. Context and cognitive state triggered interventions for mobile MOOC learning. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction. 378-385. DOI: 10.1145/2993148.2993177

Aslan, S., Alyuz, N., Okur, E., Mete, S. E., Oktay, E., and Esme, A. A. 2018. Effect of emotion-aware interventions on students’ behavioral and emotional states. Educational Technology Research and Development, 66(6), 1399-1413. DOI: 10.1007/s11423-018-9589-7

Pham, P.; Wang, J. 2018. Predicting learners’ emotions in mobile MOOC learning via a multimodal intelligent tutor. In: International Conference on Intelligent Tutoring Systems. Springer, Cham. 150-159. DOI: 10.1007/978-3-319-91464-0_15

Alyuz, N., Okur, E., Genc, U., Aslan, S., Tanriover, C., and Esme, A. A. 2017. An unobtrusive and multimodal approach for behavioral engagement detection of students. In: Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education. 26-32. DOI: 10.1145/3139513.3139521

Okur, E., Alyuz, N., Aslan, S., Genc, U., Tanriover, C., and Arslan Esme, A. 2017. Behavioral engagement detection of students in the wild. In: International Conference on Artificial Intelligence in Education. Springer, Cham. 250-261. DOI: 10.1007/978-3-319-61425-0_21

Fenza, G.; Loia, V.; Orciuoli, F. 2016. Providing smart objects with intelligent tutoring capabilities by semantic technologies. In: 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS). IEEE. 103-109. DOI: 10.1109/INCoS.2016.110

Ponticorvo, M., Ferrara, F., Di Fuccio, R., Di Ferdinando, A., and Miglino, O. 2017. SNIFF: A game-based assessment and training tool for the sense of smell. In: Methodologies and Intelligent Systems for Technology Enhanced Learning: 7th International Conference. Springer International Publishing. 126-133. DOI: 10.1007/978-3-319-60819-8_15

Muñoz, K., Mc Kevitt, P., Lunney, T., Noguez, J., and Neri, L. 2011. An emotional student model for game-play adaptation. Entertainment Computing, 2(2), 133-141. DOI: 10.1016/j.entcom.2010.12.006

Ahuja, N. J., Dutt, S., Choudhary, S. L., and Kumar, M. 2022. Intelligent Tutoring System in Education for Disabled Learners Using Human–Computer Interaction and Augmented Reality. International Journal of Human–Computer Interaction, 1-13. DOI: 10.1080/10447318.2022.2124359