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Professional Development

created 2026-08-16 · updated 2026-10-03

Professional development — 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 knowledge base's substantial coverage of how AI reshapes the preparation, knowledge, beliefs, and practice of both prospective and practicing teachers.

Questions to Consider

  • Teacher education here spans pre-service training (future teachers in certification programs) and in-service professional development (practicing teachers). Which of the two do you think faces the bigger challenge preparing for AI classrooms, and why?
  • The page suggests teacher education must prepare teachers not just to use AI tools but to understand, evaluate, and ethically integrate them — a shift that 'redefines what it means to be a teacher.' How do you think that redefinition should change what a credentialing program teaches?
  • Frameworks like AI-TPACK extend the classic TPACK model by adding an AI and ethics dimension. What do you think an 'AI dimension' of technological-pedagogical-content knowledge should actually contain beyond how to operate a chatbot?
  • A scoping review of 55 studies cited on the page finds AI enhances pre-service teachers' instructional design, reflection, and critical thinking. But if AI can do some of this for them, when does its use in teacher training build skill versus substitute for the very judgment they'll need?
  • If you were redesigning a teacher-education program for the AI era, what would you require every future teacher to experience — and what would you deliberately keep them from outsourcing to AI?

Introduction

Teacher education sits at the intersection of several knowledge base strands: it is a discipline/domain (like Medical and Health Professions Education and Humanities and Social Science Education), but it also draws on the general concepts of Teaching, Technological Pedagogical Content Knowledge (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 — a shift that redefines what it means to be a teacher. The learning sciences supply the knowledge this formation has to carry: where that field studies how people learn and how learning environments should be designed, teacher education is the professional formation in which such evidence has to land as a teacher's own capacity to design, teach and assess.

Cross-level evidence sharpens the design priority: a multilevel study of 46 teachers and 2,832 secondary students found that pedagogical AI knowledge (TPAIK) — not technical AI knowledge — drove students' perceptions of AI for social good and their intention to learn AI. Teacher education should therefore foreground how to teach with and about AI over tool proficiency, consistent with the "pedagogy first, technology second" guideline.

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 Technological Pedagogical Content Knowledge (TPACK) framework with an AI/ethics dimension.(Conceptualizing pre-service teachers' readiness for AI integration into teaching practices: An intelligent-TPACK approach)(Assessing AI-TPACK readiness in mathematics teacher education: The role of self-efficacy and teaching beliefs)

  • Acceptance profiles, and an ease-of-use/intention paradox. Chen et al. (2026) profiled 128 pre-service teachers into four ChatGPT-acceptance groups: Resistant Skeptics (14.06%) reported high perceived ease of use but very low behavioral intention, so operational skills training alone does not convert them.

  • 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 artificial intelligence for preservice teachers' development: A scoping review of applications, benefits, and challenges)

  • Unscaffolded genAI access scored below no genAI. In an 11-week quasi-experiment with 52 pre-service teachers, the unscaffolded genAI class produced lower rubric-rated lesson plans than the no-genAI class (adjusted 86.54 vs 81.87, F = 8.348, p = 0.006, η² = 0.146), with no group difference in Self-Regulated Learning or critical thinking (Zhang et al. (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.(Coding, robots, computational concepts, and machine learning using the microbit card and the Maqueen and Nezha kits. A study in initial teacher training)

  • 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.(AI-mediated authentic assessment and metacognitive reflection: A mixed-methods study of the AAIWA model)

  • AI-supported inquiry in science education. A quasi-experiment with 48 pre-service science teachers in Türkiye (Aydın) integrated problem- and design-based learning into an 8-week AI-supported guided inquiry program on photosynthesis and respiration. It produced significant gains in conceptual understanding of the two biological processes, but no significant effect on AI literacy or self-perceived computational thinking — a reminder that AI-IBL can deepen subject-matter understanding in teacher candidates without automatically building their AI/CT competencies, which require explicit, targeted design.

  • Generative AI for constructivist instructional design. The SAHAB model is a quasi-experimental professional-development intervention in which generative AI empowers teachers to design Constructivism instruction, with large gains (d = 1.18). It shows GenAI can scaffold instructional design for teacher candidates and practicing teachers alike — not just deliver content, but support the design of student-centered learning activities.

  • Smart-classroom training for graduate mathematics M.Ed. students. Zhu, Liang, Mao, and Wang (2026) show how a mathematics M.Ed. course can be enhanced with intelligent educational technologies (automated scoring, personalized recommendations, multi-AI feedback) across pre-, in-, and post-class stages, yielding statistically significant gains in instructional-objective design proficiency. The transferable D-T-E Model (Disciplinary Demand–Technological Empowerment–Evaluation Loop) offers teacher educators a discipline-specific framework for integrating smart education into graduate teacher preparation.

  • Adoption under a permissive policy is thinner than surveys suggest, and assessment literacy does not transfer. Zou et al. (2026) surveyed 85 student teachers across three Hong Kong teacher-education courses where generative AI was explicitly permitted in assessment: 62.4% (53) chose not to use it at all, and adopters' use was shallow and corrective (proofreading 43.8%, clarity checks 34.4%, text generation 18.8%, brainstorming 12.5%). Fear of being wrongly accused of plagiarism (41.5% of non-adopters) and a preference for solo work (77.4%) outweighed missing skills (13.2%), and nine of eleven interviewees read the permissive policy as a possible "trap" — evidence that pre-service teachers bring integrity anxiety, not just tool readiness, into AI-permitted coursework.

  • Classroom AI policy writing as a lens on preservice thinking. Nash & Burriss (2026) had 27 preservice secondary English teachers in the capstone year of a secondary English preparation program write the AI policies they would use in their own 6–12 classrooms, in a state with no state-level AI guidance. 26 of the 27 permitted some AI use, almost always restricted to teacher-specified tasks, times, and places, and only one banned it outright on ethical grounds tied to labor practices and copyright; 22 permitted AI for generating ideas while 22 disallowed or left unclear the use of AI to compose sentences, paragraphs, or papers, and the accompanying reflections read as negotiations between pedagogical commitments and a felt obligation to integrate AI. Asking preservice teachers to author a policy — rather than debate AI in the abstract — surfaced their philosophies, beliefs, and curricular assumptions, making policy writing a high-value activity for methods coursework.

  • Policy does not govern practice. Four semesters of survey data from a US teacher-education program show GenAI use for assessment preparation rising from 57% to 83% and for studying from 44% to 76%, even as institutions considered AI-detection software, with ethical uncertainty rising across the waves (Parker et al. (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 effective AI professional development: A framework grounded in intelligent-TPACK)

  • GenAI-specific technological pedagogical knowledge (TPK). Teacher educators themselves need GenAI-TPK — pedagogical reasoning, ethical awareness, and AI-augmented instructional design — to prepare teachers.(Teaching the teachers: A systematic review of genAI-specific technological pedagogical knowledge (TPK) in teacher education)

  • 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 for teacher educators: Designing professional learning for critical AI literacy)

  • Capacity built by doing, not by studying. A TPACK-based structural model of 122 in-service teachers found that actual use of AI on science and green-energy tasks and involvement in developing ESD-aligned materials predicted AI-integration capability, while abstract AI knowledge and attitudes toward AI did not (Riandi et al. (2026)).

  • Collaborative evaluation of AI-generated content as PD. Gat, Usher, and Barak (2026) report on a workshop in which 60 middle-school science teachers rated ChatGPT-generated assessment questions through their disciplinary, pedagogical, and curricular judgment. Collaborative evaluation itself functioned as professional development, helping teachers apply conceptual-precision criteria to AI output and surface the risk that AI content reinforces Misconceptions about AI — positioning teachers as critical evaluators of GenAI material rather than passive consumers.

  • Structured PD for language educators is rare but effective. A systematic review of 23 studies (Li et al. 2026) found only three included studies reported structured professional development — an embedded grammar-course module, a government EMI program, and embedded chatbot inquiry — yet all converged on gains in knowledge, confidence, and identity reframing, shifting educators' views of GenAI from "replacement risk" to assistant/augmenter. The review argues PD should pair technical skill-building with practical wisdom, moving from awareness-raising and ethics through hands-on tool mastery to co-design of AI-enhanced lessons, and recommends a two-phase "back-end then classroom" implementation strategy.

The knowledge base's clearest instance of framework-driven GenAI PD for mathematics is a study of eight in-service teachers from rural and under-resourced districts who completed ten interactive modules built on Rhodes' 4P framework (How generative AI guided-professional development supports teachers’ engagement with mathematical creativity, content knowledge, and pedagogical content knowledge). It supplies two design mechanisms in-service PD design otherwise lacks here: scaffolding that fades deliberately (explicit guidance early, then requests to justify, generalize and design tasks) and simulated student reasoning across Modules 6-10, alongside the finding that MC, CK and PCK moved together — while measuring engagement only, not teacher knowledge or student learning.

  • Post-qualification programs. In-service science educators' AI literacy and usage inform the design of AI-related post-qualification programs.(Science educators' AI literacy and AI usage in teaching: Implications for post-qualification programs)

  • AI support and guidance in teacher design work. Pishtari, Gnadlinger & Ley (2026) had 13 higher-education teachers design activities across no-AI, AI-chatbot, and AI-plus-training conditions in a half-day program. AI access raised higher-order (Bloom) task attainment and cut perceived cognitive effort, while the subsequent interaction-training session plateaued quality but slightly raised effort (germane vs. extraneous load unresolved). It frames PD for AI-era teaching as needing both pedagogy-grounded design frameworks (Bloom, ICAP) and structured chatbot-interaction strategies, while cautioning that quality gains may mask offloading of pedagogical decisions to AI.

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.(Beyond operational skills: Teachers' AI knowledge and interactions with generative AI in lesson planning) The distinction runs even deeper when experience is crossed with AI proficiency: Choi et al. (2026) found that technically fluent novices still accept AI output passively during lesson design, whereas experienced teachers — even those with lower measured AI proficiency — critically re-prompt and adapt AI suggestions to their students and context. The implication is that in-service PD cannot treat "building AI skill" as sufficient; it must be differentiated by the teacher's experience and proficiency profile — response-evaluation and critical-adaptation scaffolds for novices, hands-on AI skill-building for experienced teachers with weaker AI proficiency. Expert judgment of AI-generated lesson plans marks that critical-adaptation stance as the norm, not the exception: Karaismailoglu, Surmeli and Yildirim (2026) found eleven Turkish science-education specialists rated ChatGPT-4 and Teacher's Buddy plans for a sixth-grade unit as usable drafts — 7 of 11 judging them "applicable by correction," only 3 directly "Applicable" — and their preferences diverged from raw scores, favoring affective and contextual qualities over structural fidelity. Teacher education should therefore build the ability to evaluate, adapt, and localize AI-generated instructional materials as a core professional competence, in both pre-service and in-service programs. Psychological factors also matter — Self-Efficacy positively predicts AI-TPACK, while strong traditional teaching beliefs can act as a cognitive barrier.(Assessing AI-TPACK readiness in mathematics teacher education: The role of self-efficacy and teaching beliefs) Trust in AI is shaped by both technical knowledge and ethical perceptions (transparency, fairness, accountability, inclusiveness).(Unpacking ethics-domain of intelligent-TPACK scale in relation to in-service teachers' trust and distrust) Measuring teachers' AI literacy is itself an active front: because most AI-literacy assessments target students or general users, the Teachers' AI Literacy Scale (TAILS) — grounded in the six-dimension ED-AI framework — was developed and validated specifically for preservice language teachers through EFA and CFA, filling the measurement gap within teacher education. A comparably neglected question is whether the mode of preparation changes AI readiness. Mnguni et al. (2026) surveyed 186 final-year science student teachers in South Africa and found self-reported TPACK for AI-integrated teaching higher at a campus-based university (64.0%) than at a distance education university (47.4%), with Pedagogical Knowledge weakest in both, and AI training associated with self-reported TPACK only at the distance institution, where a five-credit short course predicted weaker reported TPACK than no training. Distance preparation therefore needs its own evidence base rather than assuming that training designed for campus programs transfers.

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 Large Language Models (LLMs)-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. A subject-specific instance is Student GPT (Zhuang & Zhang 2025), a custom ChatGPT chatbot that role-plays a middle school student holding common ratio-reasoning misconceptions; preservice secondary mathematics teachers practiced diagnosing and remediating those misconceptions in low-risk, personalized interactions within a methods course, showing how lightweight GenAI role-play can support practice-based teaching.

Implications for teacher educators

  • Build both AI literacy and critical AI-TPACK. Pre-service and in-service teachers need readiness to integrate AI — 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 — 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 — attend to these psychological factors, not just skills.

  • Backtracking from classroom dilemmas. Kohnke et al. (2026) interviewed 17 pre-service teachers at a Hong Kong university and worked backward from anticipated classroom dilemmas to teacher-education design needs. Pre-service teachers raised concerns about academic integrity, privacy, and preserving essential human skills in a GenAI-driven environment, pointing to curriculum implications that weave ethics, Privacy, and AI literacy (with critical-thinking skills) throughout teacher preparation rather than treating responsible use as an add-on.

  • AI as cognitive mediator in teacher reflection (2026): A practice-based Reflective Triangle Model uses AI to mediate between individual teacher reflection and shared professional knowledge in learning communities, addressing the common failure of reflection to transform into collective professional learning (The Reflective Triangle Model: AI as a Cognitive Mediator in Teachers' Professional Learning and Learning-Community Development).

  • Make GenAI assessment design part of professional curriculum work. Student teachers in Zou et al. (2026) showed little intention of redesigning their own future school assessments in response to generative AI, and the kindergarten and secondary participants largely judged the technology irrelevant to their own teaching context — an underdeveloped dimension of assessment literacy rather than a knowledge gap. Teacher-education programs should therefore embed ethical GenAI use in assessment design as part of professional curriculum, pairing program-level policy consistency with concrete exemplars of acceptable and unacceptable use and timely Feedback on students' actual AI use.

  • Separate delegable from non-delegable work. O'Byrne (2026) found 23 undergraduates, most pre-service teachers, produced comparable artifacts whether they sustained boundary work or delegated it early, and the "cheating tension" over authorship was strongest among those doing the most boundary work — so the teaching aim is judging which interpretive work not to hand over.

  • Make classroom policy writing a core teacher-education activity, and treat ambiguity as a policy defect. Nash & Burriss (2026) found that asking preservice teachers to write their own classroom AI policies functions as rhetorical world-building about what writing is for — a window into emergent beliefs that abstract discussion does not open. Because AI separates writing-as-product from writing-as-process, programs should help teachers locate where thinking, learning, and value actually live across brainstorming, drafting, revising, and editing rather than locating thinking only in final text, and should support them in translating beliefs into operational, unambiguous rules paired with AI Literacy instruction: contradictions such as barring AI-generated text while holding students responsible for the AI-generated content they submit leave students unable to comply.

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Metadata

LevelTeacher Education, K 12, Adult Learning
Intended audienceInstructors
Foundations of AI in educationAI Literacy, Teaching, Technological Pedagogical Content Knowledge (TPACK)
SourceHistoryEditChat

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