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Teacher Education β€” the preparation and ongoing professional development of teachers, spanning pre-service teacher training (initial certification programs) and in-service professional development. In AI-in-education research, teacher education has become a central concern because teachers' AI literacy, technological-pedagogical knowledge, ethical fluency, and readiness to integrate AI into instruction determine whether AI adoption in classrooms succeeds. This concept organizes the wiki's substantial coverage of how AI reshapes the preparation, knowledge, beliefs, and practice of both prospective and practicing teachers.

Teacher education sits at the intersection of several wiki strands: it is a discipline/domain (like Medical Education and Humanities Education), but it also draws on the general concepts of Teacher Role, TPACK, Teacher AI Competency, and AI Literacy. In the AI era, teacher education must prepare teachers not only to use AI tools but to understand, evaluate, and ethically integrate them β€” a shift that redefines what it means to be a teacher.

Pre-service teacher education

Pre-service (initial) teacher education prepares future teachers during their certification programs. AI research in this strand includes:

In-service professional development

In-service professional development supports practicing teachers in integrating AI. AI research here includes:

Knowledge, beliefs, and practice

A key finding across teacher-education research is the gap between what teachers articulate and what they enact: teachers often claim operational AI skills but struggle to apply pedagogically meaningful knowledge in practice.^Teachers AI Knowledge GenAI Lesson Planning 2026 Psychological factors also matter β€” Self Efficacy positively predicts AI-TPACK, while strong traditional teaching beliefs can act as a cognitive barrier.^AI TPACK Mathematics Teacher Education 2026 Trust in AI is shaped by both technical knowledge and ethical perceptions (transparency, fairness, accountability, inclusiveness).^Intelligent TPACK Ethics Teachers Trust Distrust 2026

Simulated instructional practice

Beyond content and beliefs, teacher preparation increasingly uses simulated classrooms for hands-on practice that scales. EducaSim uses generative student agents (with personas, course-grounded memories, and an LLM-as-judge speech oracle) to simulate a small-group section for teachers-in-training in a CS1 course supporting ~20,000 students. Deployed as an optional prep tool across 254 sessions (mean ~16 min), it provides low-cost ($0.05–$0.10/session) role-play practice with structured post-session feedback (talk-time statistics, LLM-identified instructional behaviors) and self-reflection prompts. This complements the Simulating Students paradigm: simulated learners serve not just evaluation but experiential, high-frequency teacher preparation, especially for massive online courses where live coaching cannot scale.

Implications for teacher educators

  • Build both AI literacy and critical AI-TPACK. Pre-service and in-service teachers need readiness to integrate AI β€” instruments extend TPACK with an AI/ethics dimension (AI-TPACK, i-TPACK PD); teach ethical reasoning, transparency, and fairness alongside tool use.
  • Close the articulate-vs-enact gap. Teachers often claim operational AI skills but struggle to apply them pedagogically; design PD that moves from knowledge to enacted practice (lesson planning).
  • Use simulated practice to scale preparation. EducaSim-style simulated classrooms give teachers-in-training low-cost, high-frequency practice with feedback β€” a complement to limited live coaching.
  • Address beliefs and trust. Self-efficacy predicts AI-TPACK while strong traditional-teaching beliefs can be a barrier, and Trust is shaped by transparency/fairness β€” attend to these psychological factors, not just skills.

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