Build educator AI literacy and pedagogical competence alongside student critical engagement with AI-generated content¶

Strategy · All strategies
Evidence · 2 claims (2 for) · 2 studies (1 associational, 1 qualitative), q2 · 0 of 2 report an effect size · 2 claims rest on one study

Description¶

Among its educational implications, the article highlights the need for teacher capability: "Teachers must be equipped not only to use AI tools but also to critically evaluate their outputs, understand their limitations, and integrate them meaningfully into instructional practice." It links this to supporting students' critical engagement with generative AI content, given concerns about inaccuracies and over-reliance on automated feedback.

Design Implications¶

Context¶

Requirements¶

• Professional development that develops both AI literacy and pedagogical competence among educators

Constraints¶

• The article cautions that without careful implementation, AI may risk reinforcing existing educational inequalities rather than reducing them

Target Learners¶

• teachers and educators in STEM education

Target Learning Goals¶

• effective and critical integration of AI technologies into instruction

Claims¶

• Faculty concerns centered on academic integrity (80%) and student over-reliance on AI (70%), and their most requested development topic was AI ethics and academic integrity (70%) [+W]
• Pedagogical AI competence is positively associated with teachers' AI use [+W]

Related Strategies¶

• Ai As Pedagogical Partner Not Replacement
• Reaffirm learning-by-making through STEAM tinkering and critical AI literacy to cultivate epistemic resilience
• Institutions should respond to generative AI in education with guided critical engagement rather than prohibition
• Ground AI integration in higher education on core principles of human agency, academic freedom, transparency, ethics, inclusivity, and critical thinking
• Transition from restrictive AI governance to frameworks that formally integrate operational and epistemic workflow competencies with subject-sensitive guidance
• Faculty development for GenAI-era teaching innovation should address professional identity, AI literacy, and teaching self-efficacy alongside knowledge
• Institutional governance should establish practical conditions supporting AI literacy, learner agency, and critical evaluation rather than relying on prohibition and detection
• Keep governance of teacher-AI design processes with teachers

Examples¶

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Key Sources¶

• Lb0