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Designing effective AI professional development: A framework grounded in intelligent-TPACK

● high · 2026-08-16 · teacher-education, professional-development, tpack, intelligent-tpack, ai-literacy, ethics

Synthesis: Dogan (2026) employs a Conceptual Framework Development (CFD) model, conducted in four phases, to construct the Intelligent-Technological Pedagogical Content Knowledge-based Teacher Education (i-TPACK-based PD) Framework. This research-informed, integrated model aligns the five knowledge domains of i-TPACK (i-TK, i-TCK, i-TPK, i-TPACK, and AI Ethics) with four evidence-based AI PD pathways — Active Learning, use of models and examples, coaching and expert support, and Feedback and reflection — drawn from the Integrated Pathways for Teacher Growth model. Grounded in an evidence base that spans a PRISMA systematic review of AI PD, two additional systematic reviews, and searches of Scopus, ERIC, and Web of Science (May 2024–May 2025), the framework interweaves these domains and pathways to foster teachers' AI-specific pedagogical reasoning, technological fluency, content-based applications, and ethical decision-making, offering a detailed domain-to-pathway mapping, a sample scenario, and design principles for PD developers while critically addressing limitations related to program duration, delivery format, outcome evaluation, and ethical integration.

Key Findings

i-TPACK-based PD framework. The study integrates the five i-TPACK knowledge domains with four evidence-based PD pathways to create a cohesive structure for teacher AI professional development, building on the premise that effective teacher learning emerges not from isolated elements but from their intentional, dynamic interaction.

Five knowledge domains. i-TK (intelligent technological knowledge), i-TCK (technological content), i-TPK (technological pedagogical), i-TPACK (integrated implementation knowledge), and AI Ethics are all addressed, with ethics woven throughout — a notable contrast to many existing AI PD programs in which AI ethics is "superficially addressed or entirely absent."

Four PD pathways. The framework maps to active learning, use of models and examples, coaching and expert support, and feedback and reflection — the evidence-based mechanisms for teacher learning. These operate as connected, mutually reinforcing learning experiences rather than isolated interventions, governed by five guiding principles: Collective Synergy Over Silos, Cross-Element Responsiveness, Recursive Learning Loops, Design for Convergence, and Flexibility within Structure.

Methodological grounding. The framework draws on Dogan et al.'s PRISMA systematic review of AI PD studies, supplemented by two later PRISMA-based reviews (Tan et al., Li et al.) and an updated search of Scopus, ERIC, and Web of Science (May 10, 2024–May 10, 2025) that surfaced 17 new AI PD studies. Empirical examples embedded across pathways include Sun et al.'s classroom visits and lesson planning, Nazaretsky et al.'s group discussions and practice grading with AI tools, Ding et al.'s case-based AI PD, and Brünner et al.'s interactive workshop introducing AI Literacy, prompting strategies, and ethics.

Practical tools. The paper provides a detailed i-TPACK domain-to-AI-PD-pathway mapping, a sample implementation scenario across the phases of teacher learning, and design principles to support PD developers, alongside recommendations for research and practice.

Implication. This is a foundational framework for the wiki's Teacher Education and Faculty Development concepts, showing how AI teacher learning must build technological, pedagogical, content, and ethical fluency together — a framework for AI PD that is pedagogically grounded, ethically responsive, and adaptable across educational contexts.

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Citation

Dogan, S. (2026). Designing effective AI professional development: A framework grounded in intelligent-TPACK. Computers and Education Open, 100337. https://doi.org/10.1016/j.caeo.2026.100337