Intelligent Tutoring Systems and the Future of Problem-Solving Education: An Opinionated Synthesis - Dwi Sulisworo; Aysel Arslan (The Indonesian Institute of Science and Technology Research, 2025)
The rapid evolution of Intelligent Tutoring Systems (ITS) has transformed contemporary educational discourse, particularly regarding how automated systems can enhance students’ problem-solving abilities in STEM fields. Although empirical studies consistently show that ITS can outperform traditional instruction, the mechanisms driving these gains remain insufficiently emphasized in policy and pedagogical narratives. This article argues that ITS effectiveness stems from a synergy of seven cognitive mechanisms: adaptive scaffolding, immediate and elaborated feedback, model-tracing and knowledge-tracing, metacognitive prompting, productive practice, error-driven learning, and cognitive load regulation. An opinion-based synthesis supported by extensive literature is presented to advocate for the strategic integration of ITS within national instructional reforms. The conceptual framework includes a visual diagram representing the mechanisms and their interactions. Results and discussion are organized into three subsections The article concludes with recommendations for adopting ITS in large-scale educational systems to improve learners' higher-order problem-solving.