Teacher AI professional development should pair readiness-building with structured engagement with attribution and pedagogy¶

Strategy · All strategies
Evidence · 3 claims (2 for, 1 mixed) · 1 study (1 associational), q3 · 0 of 1 report an effect size · 3 claims rest on one study

Description¶

Because the study finds positive beliefs and concerns form along separate tracks, the authors recommend PD that goes beyond tool demonstrations and technical confidence. A second strand should help teachers "examine why they are concerned" and whether they attribute plagiarism to stable features of the technology or to addressable factors in assessment design. PD should also model creativity-preserving practices, such as using GenAI as a brainstorming scaffold rather than a drafting replacement, and integrity-supportive practices, such as oral and process-based assessments.

Design Implications¶

Context¶

Requirements¶

• PD must address both the motivational track and the concern track; acknowledgment of legitimate institutional concerns is needed in high-integrity-enforcement contexts

Constraints¶

• The authors note attribution retraining of this kind has not yet been systematically applied to AI professional development

Target Learners¶

• K-12 in-service teachers

Target Learning Goals¶

• Responsible, informed integration of generative AI into instruction

Affordances¶

• Dual Lens Sdt Attribution Genai Beliefs Framework

Claims¶

• AI readiness is the strongest predictor of K-12 teachers' positive beliefs about GenAI's instructional impact, with the model explaining 50% of variance [+W]
• Higher AI readiness is associated with greater, not lower, concern about GenAI reducing student creativity [~W]
• Predictors of positive GenAI beliefs explain only 6–7% of variance in plagiarism and creativity concerns, indicating a two-track belief structure [+W]

Related Strategies¶

• Structure AI-focused professional development as a supported trajectory of planning, reflection, design autonomy, and embedded critical AI literacy
• Invest in dedicated instructional technology leadership roles and role-specific professional learning for AI literacy
• Prioritize evidence-based technology programs and implement them with professional learning and integrated practice
• Engage student teachers in reflection on their educational beliefs before immersing them in IBL

Examples¶

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Key Sources¶

• Xiu RL, Aguilar SJ, Macías AJ, Shikanova A, Al-Sulaiti R, Junjunia M and Jebril ST (2026) K-12 in-service teachers’ beliefs about generative AI in classrooms: insights from the United States, India, Qatar, Colombia, and the Philippines. Front. Educ. 11:1929017. https://doi.org/10.3389/feduc.2026.1929017