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Generative artificial intelligence (AI) is rapidly becoming part of educational practice, prompting teacher educators to reconsider how these tools should be integrated into preservice preparation. Rather than asking whether AI should be permitted or prohibited, this practitioner inquiry explores how instructional design can position AI as a catalyst for professional learning. In an undergraduate elementary STEAM methods course, preservice teachers used generative AI to produce an initial draft of an integrated instructional unit aligned to a Next Generation Science Standards (NGSS) performance expectation. They then engaged in a structured process of critique, revision, justification, and reflection designed to position AI-generated content as an object of professional analysis rather than a finished product. An iterative thematic review of students' written reflections suggested four recurring themes: AI lowered the barrier to beginning, students developed greater discernment in evaluating AI-generated content, revision became the primary site of professional judgment, and confidence emerged through ownership of instructional decisions. The findings suggest that the educational value of generative AI lies less in the technology itself than in the pedagogical structures surrounding its use. The article proposes a human-centered framework for AI integration in teacher education, arguing that thoughtfully designed learning experiences can reposition, rather than replace, the cognitive work of learning to teach.
Keywords: Artificial intelligence in education, Teacher education, Preservice teacher education, AI literacy, Instructional design, STEAM integration, Educational leadership
How to Cite: Pesnell, B. A. (2026) “From Prompt to Professional Judgment: Designing Human-Centered AI Integration in Elementary Teacher Education”, Educational Considerations. 51(3). doi: https://doi.org/10.4148/edconsiderations.20914
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