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Practitioner Voices Summit: How Teachers Evaluate AI Tools Through Deliberative Sensemaking

Date
Publisher
arXiv
Teachers face growing pressure to integrate AI tools into their classrooms, yet are rarely positioned as agentic decision-makers in this process. Understanding the criteria teachers use to evaluate AI tools, and the conditions that support such reasoning, is essential for responsible AI integration. We address this gap through a two-day national summit in which 61 U.S. K-12 mathematics educators developed personal rubrics for evaluating AI classroom tools. The summit was designed to support deliberative sensemaking, a process we conceptualize by integrating Technological Pedagogical Content Knowledge (TPACK) with deliberative agency. Teachers generated over 200 criteria - initial articulations spanning four higher-order themes (Practical, Equitable, Flexible, and Rigorous) - that addressed both AI outputs and the process of using AI. Criteria contained productive tensions (e.g., personalization versus fairness, adaptability versus efficiency), and the vast majority framed AI as an assistant rather than a coaching tool for professional learning. Analysis of surveys, interviews, and summit discussions revealed five mechanisms supporting deliberative sensemaking: time and space for deliberation, artifact-centered sensemaking, collaborative reflection through diverse viewpoints, knowledge-building, and psychological safety. Across these mechanisms, TPACK and agency operated in a mutually reinforcing cycle - knowledge-building enabled more grounded evaluative judgment, while the act of constructing criteria deepened teachers' understanding of tools. We discuss implications for edtech developers seeking practitioner input, school leaders making adoption decisions, educators and professional learning designers, and researchers working to elicit teachers' evaluative reasoning about rapidly evolving technologies.
Study at a glance
  • Question: What criteria do K–12 mathematics educators use to evaluate classroom AI tools, and how can structured convenings support their reasoning?
  • Setting and data: Two-day national summit in June 2025; 61 U.S. K–12 mathematics educators across 22 states; surveys, recorded discussions, interviews, and 55 teacher-level evaluation rubrics.
  • Method: Mixed methods analysis combining qualitative coding of discussion transcripts, surveys, and interviews with quantitative and multinomial logistic regression analysis of teacher-created rubrics.
Why it matters

Teachers face growing pressure to adopt AI tools but are rarely positioned as evaluators in technology decisions. Understanding how teachers evaluate AI tools is essential for responsible integration.

Key findings
  • Teachers articulated over 200 evaluation criteria spanning practicality, equity, flexibility, and rigor, with practical criteria appearing most frequently (122 instances).
  • Nearly all criteria framed AI as an assistant to execute teacher-defined tasks, with only 3% positioning AI as a coach to improve teaching practice.
  • Five mechanisms supported deliberative sensemaking: time for deliberation, artifact-centered tasks, collaborative reflection across diverse views, knowledge-building, and psychological safety.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

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