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

Synthesis: Dogan (2026) employs a Conceptual Framework Development (CFD) model, conducted in four phases, to construct the Intelligent-Technological Pedagogical Content Knowledge-based Professional Development (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 knowledge base's Professional Development and Educational 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.

What this means for practice

• Faculty developers. Design AI professional development to develop all five i-TPACK domains together — i-TK, i-TCK, i-TPK, i-TPACK, and AI ethics — and make ethics explicit, since existing programs address it superficially or not at all.
• Faculty developers. Layer the four pathways (active learning, models and examples, coaching and expert support, feedback and reflection) inside single activities instead of running them as separate workshops, following the principle of collective synergy over silos.
• Instructors. Locate your own development needs on the domain-to-pathway mapping and start from the domain it exposes rather than from whichever AI tool is available.
• Administrators. Fund sustained programs over one-off workshops: the framework warns that without structured, ethically informed, and context-sensitive PD, AI integration remains superficial or harmful.

Limitations

• This is conceptual framework development: four phases of literature synthesis produce a mapping, a sample scenario, and design principles, but no program was implemented or evaluated with teachers.
• The evidence base is small — the initial PRISMA review covered 14 AI PD studies — and the updated search (May 2024 to May 2025) yielded 17 new studies of which only 2 survived eligibility screening.
• Inclusion criteria restricted the search to K-12, peer-reviewed empirical studies, so the framework's claimed transferability to higher education and adult learning goes beyond its evidence.
• The framework depends on i-TPACK, a still-emerging extension of TPACK, and the paper's own stated limitations concern program duration, delivery format, outcome evaluation, and ethical integration — none of which a conceptual design can resolve.

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