From Explanation to Diagnosis: Next Generation Interactive Video Coach with Misstep Awareness
For developers of intelligent tutoring systems, this work provides a method to incorporate explicit diagnostic knowledge into AI coaches, enabling more precise and actionable feedback that supports conceptual change.
The paper introduces a misstep-aware coaching capability for the Ivy neurosymbolic AI coach by augmenting its Task-Method-Knowledge model with a Pedagogical Model that encodes instructor diagnostic knowledge. This enables detection and classification of learner errors and generation of diagnosis-grounded scaffolding, moving beyond knowledge retrieval toward diagnostic misstep awareness.
Intelligent tutoring systems excel at generating explanations but rarely provide principled diagnosis of where and why a learner is wrong. We introduce a misstep-aware coaching capability for Ivy, a neurosymbolic AI coach, built on a two-model architecture that augments a Task-Method-Knowledge (TMK) model with a new Pedagogical Model (PM) in the context of an online graduate AI course at Georgia Tech. The PM makes instructor diagnostic knowledge explicit and machine-readable by encoding, for each quiz question and incorrect response, the learner's underlying belief(a brief statement of the incorrect idea or missing knowledge), a TMK locus(the source of the misunderstanding), a misconception type and targeted scaffolding derived from the instructor's Q\&A key. Using quiz questions from the course, we demonstrate a proof-of-concept pipeline that detects and classifies learner errors and generates diagnosis-grounded scaffolding, moving Ivy beyond knowledge retrieval toward diagnostic misstep awareness, and enabling more precise, actionable feedback that supports conceptual change and advances adaptive learning systems in AI in education and the learning sciences.