5 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-…
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…
Using Large Language Models to Detect Socially Shared Regulation of Collaborative Learning
Jiayi Zhang, Conrad Borchers, Clayton Cohn +7
The field of learning analytics has made notable strides in automating the detection of complex learning processes in multimodal data. However, most advancements have focused on in…
Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
Huichi Zhou, Yihang Chen, Siyuan Guo +8
In this paper, we introduce a novel learning paradigm for Adaptive Large Language Model (LLM) agents that eliminates the need for fine-tuning the underlying LLMs. Existing approach…