LLM student personas with ADHD show stable self-reported traits at high intensity but behavioral drift in unscripted interactions that scripted prompts eliminate.
Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents
2 Pith papers cite this work. Polarity classification is still indexing.
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CogEvolution combines ICAP cognitive taxonomy, IRT memory retrieval, and evolutionary algorithms into a generative agent that simulates dynamic student cognitive evolution and outperforms baselines in fidelity and learning curves.
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LLM-Based Educational Simulation: Evaluating Temporal Student Persona Stability Across ADHD Profiles
LLM student personas with ADHD show stable self-reported traits at high intensity but behavioral drift in unscripted interactions that scripted prompts eliminate.
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CogEvolution: A Human-like Generative Educational Agent to Simulate Student's Cognitive Evolution
CogEvolution combines ICAP cognitive taxonomy, IRT memory retrieval, and evolutionary algorithms into a generative agent that simulates dynamic student cognitive evolution and outperforms baselines in fidelity and learning curves.