This paper introduces EE-Eval, an automated framework to evaluate the interaction quality of AI-generated educational content called 'explorable explanations' by comparing their underlying interaction logic to ideal pedagogical designs.

In plain terms

AI can now quickly create interactive learning materials, but it's difficult to properly assess how good their interactivity truly is—meaning how effectively they let learners explore and receive useful feedback. Current evaluation methods mostly focus on things like code working or visual appeal, not the actual quality of the learning experience. The researchers developed EE-Eval, an automated framework that formalizes the interaction logic of these AI-generated materials as a 'Finite State Machine' (FSM), which is like a map showing all possible learner actions and system responses. They extract these FSMs from AI-generated content and compare them to an 'ideal FSM' that represents the intended teaching strategy. EE-Eval consistently differentiated interaction quality beyond simple checks, and its assessments aligned much better with human judgments of how interactive and effective the learning materials were.