AI Assistants Overassist
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 probl
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 56%How Preply combines AI and human tutors to personalize learning →
- PossiblePossibly related (embedding) · 55%Beyond grep: The case for a context-rich AI coding harness →
- PossiblePossibly related (embedding) · 54%Student AI Fellows: Piloting solutions to complex campus challenges with large language models - Inside UNC Charlotte →
- FuzzyOverlapping authors or contributors · 62%firecrawl/firecrawl →
“Shared author/contributor keys: jain”
- LinkedLinked via arxiv author · 85%Verona Teo →
“AI Assistants Overassist”
- LinkedLinked via arxiv author · 85%Raghav Jain →
“AI Assistants Overassist”
- LinkedLinked via arxiv author · 85%Tobias Gerstenberg →
“AI Assistants Overassist”
- LinkedLinked via arxiv author · 85%Max Kleiman-Weiner →
“AI Assistants Overassist”