Abstract
Achieving deeper understanding in online learning requires adaptive support that evolves with learner needs. This paper traces the iterative design of Ivy, a generative AI coach that combines structured knowledge representations with large language models to deliver pedagogically aligned responses. Across three versions: pre-Ivy, Ivy 1.0, and Ivy 2.0, we analyzed learner interactions and preferences. Expert users consistently favored Ivy’s structured, example-driven responses, while novices often preferred the more conversational tone of a ChatGPT-powered assistant. These insights shaped Ivy’s refinement and suggest its potential to support novices in developing conceptual understanding and progressing toward expertise.
