Structure AI-focused professional development as a supported trajectory of planning, reflection, design autonomy, and embedded critical AI literacy¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that professional development move beyond tool demonstrations and technology-acceptance outcomes toward a supported developmental trajectory. Programs should "build confidence and informed willingness to experiment, scaffold cycles of planning and reflection, provide autonomy to design and revise modules, and examine evidence from learner work." Critical AI literacy should be embedded in planning, differentiation, inquiry, and assessment rather than confined to a separate ethics session, and assessment should include artifacts and rationales rather than confidence surveys alone.

Design Implications¶

Context¶

Requirements¶

• Programs that scaffold cycles of planning and reflection and give teachers autonomy to design and revise modules

Constraints¶

• The typology used as a formative design heuristic is not a validated measurement scale; claims rest on teacher reports without student outcome measures

Target Learners¶

• in-service teachers in AI-focused professional development; teacher educators designing such programs

Target Learning Goals¶

• teacher professional learning for AI-enhanced heutagogical learning design and critical AI mediation

Affordances¶

• Dual Layer Teacher Regulation Learner Design Ai

Related Strategies¶

• Operationalize the faculty standards through structured professional learning pathways with modules, microcredentials, and peer review
• Invest in sustained, role-specific, discipline-specific professional learning and structured student AI literacy pathways across PK16
• Design AI-related teacher professional development as practice-oriented, case-based and connected to teachers' real pedagogical decisions
• Embed AI training for pre-service science teachers within science pedagogy and inquiry-based teaching rather than general tool operation
• Teacher AI professional development should pair readiness-building with structured engagement with attribution and pedagogy
• Integrate LLM-based CRP support tools within professional development to encourage teacher reflection beyond template-based outputs
• Shift teacher professional development from information-based training to experience-based AI design activities in sustainability contexts
• Build teacher AI readiness through structured institutional support: professional development, mentoring, technical assistance, and AI literacy in teacher education
• Professional development for AI ethics should create spaces for contextualised critique and resistance
• Focus staff development on the judgement work of AI integration rather than tool training
• Institutional AI initiatives should incorporate discussion, reflection, and distributed discipline-specific support networks rather than centralized information provision
• Direct further research toward new teacher roles, learning pathways and graduate attributes focused on imagination, creativity and innovation
• Integrate structured opportunities for critical engagement with AI into teacher education programs

Examples¶

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Key Sources¶

• Hochberg N and Waitzman R (2026). From information providers to learning designers: heutagogical teaching practices in AI-enhanced education. Front. Educ. 11:1905833. https://doi.org/10.3389/feduc.2026.1905833