EduAgentBench is a new source-grounded benchmark that evaluates tutor agents across pedagogical judgment, situated multi-turn tutoring, and Canvas-style workflow completion, finding frontier models capable of basic judgment but inadequate for professional teaching standards.
Learnlm: Improving gemini for learning.arXiv preprint arXiv:2412.16429
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Behavior latticing synthesizes connections across unstructured user interactions to generate insights into underlying motivations, yielding deeper and more accurate user understanding than task-only models.
Debiasing-DPO reduces bias to spurious social contexts by 84% and improves predictive accuracy by 52% on average for LLMs evaluating U.S. classroom transcripts.
LLMs simulating student think-alouds in multi-step chemistry tutoring produce overly coherent, verbose, and confident reasoning that overestimates learner success compared to 630 human utterances.
Introduces L2-Bench benchmark for AI feedback in language education across six dimensions and identifies explainability pitfalls in AI-generated explanations that appear helpful but are flawed.
citing papers explorer
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Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows
EduAgentBench is a new source-grounded benchmark that evaluates tutor agents across pedagogical judgment, situated multi-turn tutoring, and Canvas-style workflow completion, finding frontier models capable of basic judgment but inadequate for professional teaching standards.
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Behavior Latticing: Inferring User Motivations from Unstructured Interactions
Behavior latticing synthesizes connections across unstructured user interactions to generate insights into underlying motivations, yielding deeper and more accurate user understanding than task-only models.
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Mitigating LLM biases toward spurious social contexts using direct preference optimization
Debiasing-DPO reduces bias to spurious social contexts by 84% and improves predictive accuracy by 52% on average for LLMs evaluating U.S. classroom transcripts.
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Large Language Models as Students Who Think Aloud: Overly Coherent, Verbose, and Confident
LLMs simulating student think-alouds in multi-step chemistry tutoring produce overly coherent, verbose, and confident reasoning that overestimates learner success compared to 630 human utterances.
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Ceci n'est pas une explication: Evaluating Explanation Failures as Explainability Pitfalls in Language Learning Systems
Introduces L2-Bench benchmark for AI feedback in language education across six dimensions and identifies explainability pitfalls in AI-generated explanations that appear helpful but are flawed.