AI Assistants Overassist

2026-07-23

AI Summary

This study addresses the tendency of large language models (LLMs) to hinder users’ deep thinking and long-term learning through premature and excessive intervention in educational settings. To systematically evaluate AI teaching strategies, the authors propose Int-Bench, the first simulation-based benchmark encompassing diverse tasks such as code debugging, mathematics, and riddles. By fine-grained comparison of intervention timing, frequency, and style between LLMs and human tutors, the research reveals that current models predominantly deliver full solutions rather than scaffolding hints, prioritizing immediate task completion over fostering deeper cognitive engagement. This behavior significantly impairs learning generalization. The work provides the first quantitative evidence of behavioral biases in AI teaching agents and their detrimental impact on educational outcomes.

Abstract

Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems. While guidance from AI assistants can scaffold thinking and foster learning, such benefits depend on how they help--for instance, intervening too early or too frequently may hinder true learning and cognitive engagement. Yet how AI systems navigate intervention decisions during problem-solving remains poorly understood. Here, we introduce Int-Bench, a simulation-based benchmark for evaluating LLM interventions during learning. Int-Bench simulates a "student" solving a problem while a "teacher" monitors the student's reasoning and decides whether, when, and how to intervene. Across three domains--code debugging, mathematics, and brain teasers--we evaluate LLM teachers on the frequency and timing of interventions, as well as their impact on both immediate task success and generalization to new problems. We also compare LLMs to humans, finding that LLMs intervene more frequently and earlier than humans. Moreover, in contrast to humans, they tend to provide complete solutions rather than targeted hints. These findings suggest that current LLM assistants often optimize for short-term success rather than supporting the reasoning processes needed for deeper learning and long-term success.

Problem

• overassistance
• intervention timing
• cognitive engagement
• learning scaffolding
• AI tutoring

Innovation

• Int-Bench
• AI intervention
• scaffolding learning
• large language models
• human-AI comparison