From prompt to context: Multi-theoretical ChatGPT design for teacher feedback in K-12 engineering terminology instruction

This article presents a theory-informed framework for using ChatGPT to enhance instructional feedback to K–12 teachers for teaching engineering terminology. Engineering concepts often present abstract challenges for young learners, creating instructional difficulties for teachers without specialized training. While generative AI tools like ChatGPT offer promising support, their educational value depends on principled and context-sensitive design. Grounded in Control Theory, Sociocultural Feedback Theory, and Feedback Intervention Theory, our framework enables ChatGPT to deliver goal-oriented and developmentally appropriate feedback through structured prompt design. We demonstrate theory-based prompt examples and analyze ChatGPT’s capabilities alongside its limitations, particularly regarding contextual and pedagogical awareness. We recommend that ChatGPT be used as a complementary tool to support teachers’ reflective practice, rather than as a replacement for traditional professional development. We conclude by identifying future research opportunities for integrating AI-mediated feedback into sustainable teacher learning systems that enhance engineering education outcomes.