Teacher-Authored Prompts for Configuring Student-AI Dialogue: K-12 Classroom Implementation

TL;DR

This study explores how teacher-designed prompts influence student-AI dialogues in K-12 classrooms, highlighting their role in aligning AI interactions with pedagogical goals and identifying design factors that improve cognitive demand.

Contribution

It demonstrates that teacher-authored prompts significantly shape student-AI interactions, revealing design strategies that enhance alignment with instructional intent and cognitive complexity.

Findings

71% of student-AI conversations were on-track with instructional goals

Explicit finish lines in prompts reduced the cognitive demand gap by 0.22 levels

Guardrails preventing direct answers lowered AI final-answer rates by 8.5 percentage points

Abstract

GenAI has rapidly entered instructional and learning settings as a teaching assistant or AI tutor. However, less is known about how pedagogical intent connects to the learning generated within these systems, especially when student-facing AI dialogues are fine-tuned through teacher orchestration in live classrooms. This study examines a classroom deployment of a "Classroom Teaching Aide" (TASD) system, which enables teachers to author both a teacher-to-AI setup prompt (instructional scaffold) and a student-facing conversation starter to launch AI-mediated classroom discussions. We analyze a multi-subject pilot conducted in Spring 2025, involving 20 participating teachers (16 of whom implemented the system), across 39 classrooms and 77 TASD settings, yielding 1,479 student-AI conversations with 878 unique students. Using platform logs, LLM coding with human validation, and post-study teacher interviews (N=10), we characterize teacher authoring choices and link them to enacted student-AI interaction outcomes. In deployment, student-AI conversations were largely aligned with instructional intent: 71% were fully on-track, and fewer than 1% were substantially off-track. However, a persistent design-enactment gap emerged for cognitive demand: 38% of conversations under-reached the teacher-targeted DOK level, approaching 50% when targeting DOK 3. The study also shows that explicit finish lines in the prompt reduced the DOK gap by 0.22 levels (p < .001), and "no direct answers" guardrails reduced AI final-answer rates by 8.5 percentage points. These findings position teacher-authored prompt layers as critical orchestration levers that translate pedagogical intent into structured student-AI dialogue, underscoring both their promise for scalable classroom integration and the need for additional supports to reliably sustain higher-order reasoning during enactment.