AI Assistants Overassist

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Verona Teo, Raghav Jain, Tobias Gerstenberg, Max Kleiman-Weiner
• Venue: arXiv: cs.LG
• Published: 2026-07-23
• Collected: 2026-07-24
• URL: http://arxiv.org/abs/2607.21306v1
• PDF: https://arxiv.org/pdf/2607.21306v1
• DOI: UNKNOWN

Plain-Language Brief

Researchers introduce Int-Bench, a tool to evaluate how AI language models intervene during problem-solving tasks across code debugging, mathematics, and brain teasers. The study finds that AI models provide more frequent and earlier interventions than humans, often offering complete solutions instead of targeted hints, suggesting they prioritize short-term success over fostering reasoning for deeper learning.

What the Document Says

• Int-Bench is a simulation-based benchmark for evaluating AI interventions during problem-solving.
• The study covers three domains: code debugging, mathematics, and brain teasers.
• AI language models (LLMs) are found to intervene more frequently and earlier than humans.
• In contrast to humans, LLMs tend to provide complete solutions rather than targeted hints.
• The findings imply that current AI assistants focus on short-term success instead of supporting deeper learning.

Key Concepts Mentioned

• Large language models
• Int-Bench
• simulation-based benchmark
• problem-solving
• interventions
• code debugging
• mathematics
• brain teasers
• human comparison

Claims Made by the Document

• AI language models intervene more frequently and earlier than humans.
• LLMs tend to provide complete solutions rather than targeted hints.

Evidence Used

• The study compares LLMs to humans, finding that LLMs intervene more frequently and earlier.
• In contrast to humans, LLMs offer complete solutions instead of targeted hints

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.