Impact of AI-Based Tutoring Systems on Students’ Problem-Solving Skills
Keywords:
Artificial Intelligence Tutoring, Intelligent Tutoring Systems, Problem-Solving Skills, Adaptive Scaffolding, Learning Analytics, Transfer of LearningAbstract
Artificial intelligence-based tutoring systems are increasingly being integrated into education to provide personalised explanations, adaptive hints, and immediate formative feedback. However, improvements in test scores during AI-supported practice do not necessarily demonstrate that learners have developed independent and transferable problem-solving competence. This study addresses this distinction by examining whether adaptive scaffold fading in an AI tutoring environment strengthens students’ ability to solve unfamiliar problems after AI assistance is withdrawn. The research framework separates immediate task performance from near-transfer, far-transfer, reasoning efficiency, and independent strategy selection. A controlled educational experiment is designed in which students receive either adaptive AI tutoring, non-adaptive digital practice, or conventional instructor-supported problem-solving activities. The proposed AI tutor combines learner-state estimation, error-pattern classification, progressive hint generation, and confidence-sensitive scaffold reduction rather than directly supplying complete solutions. The study further investigates whether excessive dependence on AI assistance weakens autonomous reasoning despite improving performance during guided practice.
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Articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing others to distribute, remix, adapt, and build upon the work for non-commercial purposes while crediting the original author.
