Integrate structured opportunities for critical engagement with AI into teacher education programs¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 qualitative), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article recommends that teacher education programs deliberately build in structured opportunities for critical engagement with generative AI, so that pre-service teachers develop digital pedagogical literacy and reflective instructional decision-making. The study found that unstructured, self-directed use tended toward single-turn prompting and surface-level evaluation of outputs, so critical engagement cannot be assumed to emerge on its own. As the authors state, the findings highlight "the importance of integrating structured opportunities for critical engagement with AI into teacher education programs".

Design Implications¶

Context¶

Requirements¶

• Programs must provide scaffolds supporting critical self-awareness, ethical discernment, and pedagogical agency for meaningful digital engagement

Constraints¶

• The recommendation arises from a small, information-rich sample (N = 11) in one Turkish teacher education context, prioritising depth over generalisable patterns

Target Learners¶

• pre-service EFL teachers in initial teacher education practicums

Target Learning Goals¶

• digital pedagogical literacy
• reflective instructional decision-making
• critical evaluation of AI-generated content

Claims¶

• Participants' prompting was often limited, with poorly formulated prompts leading to vague or unhelpful outputs [+W]

Related Strategies¶

• Embed AI training for pre-service science teachers within science pedagogy and inquiry-based teaching rather than general tool operation
• Structure AI-focused professional development as a supported trajectory of planning, reflection, design autonomy, and embedded critical AI literacy
• Train teachers in prompt engineering, evaluating AI-generated responses, ethical and privacy considerations, and integrating AI outputs into pedagogical decision-making
• Build teacher AI readiness through structured institutional support: professional development, mentoring, technical assistance, and AI literacy in teacher education
• Integrate LLM-based CRP support tools within professional development to encourage teacher reflection beyond template-based outputs
• Design AI-related teacher professional development as practice-oriented, case-based and connected to teachers' real pedagogical decisions

Examples¶

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Key Sources¶

• Saadet Korucu-Kış, Arif Bakla. (2026). Exploring pre-service EFL teachers’ perceptions, use cases and questioning practices of ChatGPT during practicum: a critical digital pedagogy perspective. Instructional Science. https://doi.org/10.1007/s11251-026-09783-6