10 papers
Evidence-Decision-Feedback: Theory-Driven Adaptive Scaffolding for LLM Agents
Clayton Cohn, Siyuan Guo, Surya Rayala +11
LLMs offer tremendous opportunities for pedagogical agents to help students construct knowledge and develop problem-solving skills, yet many of these agents operate on a "one-size-…
CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback
Clayton Cohn, Ashwin T S, Naveeduddin Mohammed +1
Large language models (LLMs) have created new opportunities to assist teachers and support student learning. While researchers have explored various prompt engineering approaches i…
A Theory-Guided LLM Pedagogical Agent for STEM+C Scaffolding Without Over-Reliance
Clayton Cohn, Surya Rayala, Siyuan Guo +13
LLM pedagogical agents are proliferating, yet recent findings have raised questions about their adherence to established theories of learning and, by extension, their educational v…
BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation
Hanchen David Wang, Clayton Cohn, Zifan Xu +3
Simulating student learning behaviors in open-ended problem-solving environments holds potential for education research, from training adaptive tutoring systems to stress-testing p…
Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)
Clayton Cohn, Surya Rayala, Caitlin Snyder +10
Collaborative dialogue offers rich insights into students' learning and critical thinking, which is essential for personalizing pedagogical agent interactions in STEM+C settings. W…
A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents
Clayton Cohn, Surya Rayala, Namrata Srivastava +6
Large language models (LLMs) present new opportunities for creating pedagogical agents that engage in meaningful dialogue to support student learning. However, current LLM systems…