Designing Teacher-Centered Interfaces to Elicit Pedagogical Context for LLM-Based Content Generation

Abstract

Abstract Large Language Models (LLMs) can help teachers generate and adapt educational materials, but most interactions still rely on free-form prompting, making instructional intent difficult to express and reuse. In this paper, we study how teacher-facing interfaces can elicit pedagogically meaningful contextual information and reduce ad hoc prompt engineering. We first conducted a requirement analysis with $$N=100$$ N = 100 teachers across 17 contextual fields. We measured importance and introduced fill deficit to identify “friction points” (i.e., information teachers consider important but are less likely to provide). Based on this analysis, we implemented a context-rich interface with targeted supports for important and friction-point fields. In a separate user study with a different sample of $$N=100$$ N = 100 teachers, the interface achieved high usability (SUS mean $$=80.33$$ = 80.33 ), low perceived workload, and increased willingness to provide contextual information, particularly for friction-point fields. For further validation, we conducted a comparative pilot with $$N=10$$ N = 10 experienced teachers; participants reported a greater tendency to use content generated with our context-rich interface (72.2%) than content generated with a baseline GPT interface (37.0%).