Augmenting Physics Instruction and Assessment through Multimodal AI: Insights from Project Ethel
TL;DR
The paper summarizes early deployments of retrieval-augmented course chatbots, accessibility support for annotated materials, formative feedback on handwritten and computational work, and AI-assisted workflows for summative grading and operational constraints and design choices intended to support equitable access and course alignment.
Abstract
Multimodal large language models are quickly expanding what AI can support in STEM instruction and assessment, but they also undermine answer-focused, unsupervised assessments. We describe Project Ethel, an institutional platform for course-specific, instructor-controlled AI assistance that emphasizes controlled context injection and human oversight. The paper summarizes early deployments of retrieval-augmented course chatbots, accessibility support for annotated materials, formative feedback on handwritten and computational work, and AI-assisted workflows for summative grading. For handwritten exams, we outline rubric-guided scoring and confidence-based triage (including item-response approaches) to route uncertain cases to human graders. We discuss operational constraints and design choices intended to support equitable access and course alignment while avoiding lock-in to provider-specific frontends.