Study Finds AI Tutors Intervene More Frequently and Earlier Than Humans
A new arXiv preprint introduces Int-Bench, a benchmark designed to evaluate how large language models (LLMs) act as tutors during problem-solving. The study finds that LLMs, when compared to human tutors, tend to intervene both more frequently and earlier, often providing full solutions instead of incremental hints. This behavior suggests that current AI assistants may prioritize immediate task completion over fostering deeper reasoning or learning.
Why it matters: As AI tutors become more widely used, understanding their intervention patterns is important to ensure they support, rather than undermine, genuine learning.
Full story at: arXiv Computers and Society ↗