Designing and Evaluating a 5E-Structured GenAI Coach for Guided Inquiry: A Pedagogy-to-Prompt Engineering Framework

The challenge of designing generative AI (GenAI) tutors that are both pedagogically sound and effective for guided inquiry remains significant. This paper introduces and evaluates a replicable design framework-termed a Pedagogy-to-Prompt Engineering Framework-that systematically translates established pedagogical models into structured AI interactions. We engineered a 5E-structured GenAI coach by integrating the 5E Learning Cycle as the instructional architecture and the 5S Prompting Principles to govern the AI’s dialogue. The coach was evaluated in a middle school chemistry context (N = 60) focusing on procedural skill acquisition for balancing chemical equations. A quasi-experimental study showed the GenAI group achieved significantly higher learning gains than a control group receiving traditional instruction (t(58) = 2.646, p = 0.011, Cohen’s d = 0.68). Crucially, a Johnson-Neyman analysis revealed that the coach was particularly beneficial for students with lower prior knowledge (pre-test scores < 39.39), effectively narrowing the achievement gap. Furthermore, Lag Sequential Analysis of the interaction logs confirmed that the student-AI dialogue successfully adhered to the intended 5E pedagogical sequence (e.g., Engage → Explore transition, z = 11.157). This study demonstrates that the proposed framework is a viable method for creating effective, scalable AI-driven learning environments. Beyond chemistry, this approach is readily adaptable to other STEM disciplines requiring guided inquiry, such as physics and mathematics. By validating a low-code, pedagogy-first methodology, this work offers a scalable blueprint for instructional designers to bridge the gap between generative AI capabilities and rigorous educational standards.