7 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…
EXG: Self-Evolving Agents with Experience Graphs
Yuxin Jin, Siyuan Zhang, Hanchen Wang +3
Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions, yet most deployed agents rem…
AI-Assisted Competency Assessment from Egocentric Video in Simulation-Based Nursing Education
Hanchen David Wang, Yilin Liu, Madison J. Lee +4
Assessing learner competency in clinical simulation requires expert observation that is time-intensive, difficult to scale, and subject to inter-rater variability. Vision-language…
Towards Verified and Targeted Explanations through Formal Methods
Hanchen David Wang, Diego Manzanas Lopez, Preston K. Robinette +3
As deep neural networks are deployed in safety-critical domains such as autonomous driving and medical diagnosis, stakeholders need explanations that are interpretable but also tru…
Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review
Clayton Cohn, Eduardo Davalos, Caleb Vatral +7
Recent technological advancements in multimodal machine learning--including the rise of large language models (LLMs)--have improved our ability to collect, process, and analyze div…