Teacher AI competency encompasses the knowledge, skills, and dispositions required for effective AI integration in educational contexts. Emerging frameworks identify three competency dimensions:
1. Technical Proficiency
Prompt Engineering: Crafting effective prompts for educational objectives (Xiao et al. 2026: 4-hour training → 2.3x integration increase)Tool Evaluation: Assessing AI tools for pedagogical fit and safetyTroubleshooting: Addressing AI-system failures in real-time classroom contexts2. Pedagogical Integration
Curriculum Alignment: Mapping AI use to learning objectivesScaffolding Design: Using AI to support student metacognition and self-regulationAssessment Literacy: Evaluating AI-generated content and student AI outputs3. Ethical & Critical Use
Bias Recognition: Identifying cultural and representational gaps in AI outputs (Wang et al. 2025)Data Privacy: Protecting student data in AI-enhanced learning environmentsEquity Advocacy: Ensuring AI tools serve diverse learnersThe Competency Gap
Zhang et al. (2026) documented a 40% gap between self-reported and performance-based AI competency. Teachers confident in AI skills often lack foundational prompting and evaluation abilities—highlighting the need for performance-based assessment.
References
Zhang, S., Xiao, R., et al. (2026). How to Assess AI Literacy: Misalignment Between Self-Reported and Performance. arXiv:2601.06101.
Xiao, R., Ye, R., et al. (2026). Transforming GenAI Policy to Prompting Instruction: An RCT. arXiv:2602.16033.
Wang, J., Xiao, R., et al. (2025). LLMs to Support K-12 Teachers in Culturally Relevant Pedagogy. arXiv:2505.08083.
Source
https://arxiv.org/abs/2601.06101https://arxiv.org/abs/2602.16033Connected Concepts
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