Strategies of Intelligent Tutoring Systems to Engage Students in Online Learning Before LLM Approaches
Aluísio José Pereira, Leandro Marques Queiros, Alex Sandro Gomes, Tiago Thompsen Primo · 2025
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.