π·οΈ Concept
Teacher Education
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:
- AI-TPACK and intelligent-TPACK readiness. Instruments and frameworks measure and build pre-service teachers' readiness to integrate AI, extending the TPACK framework with an AI/ethics dimension.^Conceptualizing Preservice Teachers AI Readiness 2026^AI TPACK Mathematics Teacher Education 2026
- Applications and benefits. A scoping review of 55 studies shows AI enhances pre-service teachers' instructional design, subject instruction, practical teaching skills, evaluation, reflective practice, critical thinking, technology integration, and pedagogical innovation.^Harnessing AI Preservice Teachers Scoping 2026
- Educational robotics and ML. Initial teacher training embeds coding, robotics, and machine-learning activities (e.g., micro:bit) to build computational thinking in future teachers.^Microbit Robotics Machine Learning Teacher Training 2026
- Authentic assessment and metacognition. AI-mediated assessment models (e.g., AAIWA) integrate authentic rubric-based assessment, condition-responsive AI feedback, and metacognitive reflection in pre-service programs.^Aaiwa AI Authentic Assessment Metacognition 2026
In-service professional development
In-service professional development supports practicing teachers in integrating AI. AI research here includes:
- Intelligent-TPACK-based PD frameworks. A research-informed framework aligns the five i-TPACK knowledge domains with four evidence-based PD pathways (active learning, models/examples, coaching, feedback/reflection).^Designing AI Professional Development Itpack 2026
- GenAI-specific technological pedagogical knowledge (TPK). Teacher educators themselves need GenAI-TPK β pedagogical reasoning, ethical awareness, and AI-augmented instructional design β to prepare teachers.^Teaching The Teachers GenAI Tpk Review 2026
- Human-centered and critical AI literacy. Design-based research produces professional-learning curricula that operationalize critical AI literacy through human-centered AI activities, including educator-in-the-loop tasks.^Human Centered AI Teacher Educators 2026
- Post-qualification programs. In-service science educators' AI literacy and usage inform the design of AI-related post-qualification programs.^Science Educators AI Literacy Postqualification 2026
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 ($0.05β$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.
Connected Concepts
- Teacher Role
- TPACK
- Teacher AI Competency
- AI Literacy
- Faculty Development
- K 12
- Ethics
- AI Education
- Chemistry Education β Chemistry education and AI: labs, formative assessment, LLM limits, philosophy of experimentation
Connected Articles
- Harnessing AI Preservice Teachers Scoping 2026 β Scoping review of AI in preservice teacher development
- Designing AI Professional Development Itpack 2026 β Intelligent-TPACK-based professional development framework
- Human Centered AI Teacher Educators 2026 β Professional learning for critical AI literacy in teacher educators
- Teaching The Teachers GenAI Tpk Review 2026 β GenAI-specific TPK in teacher education
- Teachers AI Knowledge GenAI Lesson Planning 2026 β Teachers' AI knowledge in GenAI lesson planning
- Conceptualizing Preservice Teachers AI Readiness 2026 β Pre-service intelligent-TPACK readiness
- AI TPACK Mathematics Teacher Education 2026 β AI-TPACK in mathematics teacher education
- Intelligent TPACK Ethics Teachers Trust Distrust 2026 β Ethics domain and in-service teachers' trust
- Microbit Robotics Machine Learning Teacher Training 2026 β Micro:bit robotics in initial teacher training
- Aaiwa AI Authentic Assessment Metacognition 2026 β AI-mediated authentic assessment in pre-service education
- Science Educators AI Literacy Postqualification 2026 β Science educators' AI literacy and post-qualification programs
- Ithaka Sr AI Skills College Graduates 2026 β Instructors report institutional AI-skills consensus and assessment gaps
- Young People Learning Generative AI Rapid Review 2026 β Teachers essential for relational/higher-order work in hybrid arrangements
- AI Science Chemistry Education Systematic Review 2025 β Systematic review of AI in science/chemistry education
- Context Based AI Secondary Chemistry 2026 β Context-based 7E + AI instruction in secondary chemistry
- Instructor AI Roles Chatgpt Formative Assessment 2026 β Instructor and AI roles in ChatGPT-enhanced formative assessment
- Educasim Cs1 Instructional Practice β EducaSim: interactive simulacra for CS1 instructional practice
- Chen Preservice Teachers Chatgpt Lpa 2026 β Pre-service teacher ChatGPT acceptance profiles