Teachers need AI literacy and professional judgment before classrooms can scale AI
A new review of teacher education research finds that AI can support more flexible and responsive training, but only when tied to pedagogy, ethics and professional judgment. The study, published in Education Sciences, analyzed literature from 2019 to 2025 and identified eight recurring dimensions of sustainable AI integration in teacher preparation.
The shift is significant. AI is now embedded in curriculum design, practicum work, mentoring and digital literacy - changing what teachers and teacher educators do. They are no longer expected only to adopt new systems, but to evaluate, adapt and question them.
How AI changes teacher preparation
For pre-service teachers, AI can create safer environments to test instructional strategies before entering full classroom responsibility. Simulations, adaptive learning modules and AI-supported feedback help novice teachers rehearse classroom decisions, reflect on mistakes and refine their professional identity.
For in-service teachers, AI is more often linked to professional development, classroom management, mentoring and data-informed improvement.
The review emphasizes that AI should scaffold professional judgment, not replace it. Teachers still provide empathy, moral responsibility, cultural awareness and context-sensitive decisions that AI systems cannot supply alone.
The strongest AI-supported teacher education keeps teachers as active designers. Rather than accepting AI-generated content as ready-made instruction, teachers need to align it with learning goals, student context, curriculum requirements and ethical responsibilities.
Readiness gaps and bias remain barriers
Teacher adoption of AI is uneven. Some educators use generative AI tools such as ChatGPT for planning and feedback, but many remain unsure how to integrate them responsibly. Others lack formal AI training or misunderstand what AI systems can do.
AI literacy, according to the review, is more than technical skill. It includes understanding how AI systems work, recognizing their limits, identifying bias, protecting data and knowing when human judgment should override automation.
Data privacy is a major concern. AI-enabled education systems often rely on student and teacher data. If governance is weak, sensitive information may be exposed or misused. The review also highlights algorithmic bias, especially when AI tools are trained on datasets that reflect unequal social, linguistic or cultural assumptions.
The review raises concerns about Western-centric knowledge in AI-generated educational content. If teacher education programs rely heavily on commercial systems trained on narrow knowledge bases, they may reproduce cultural bias and marginalize local knowledge - a particular risk in multicultural systems such as the UAE, where schools operate across diverse curricula and languages.
The digital divide threatens sustainable adoption. Access to AI tools, high-speed internet, devices and training varies across regions and institutions. Without equity planning, AI adoption may deepen existing inequalities.
Teacher agency matters
Some research suggests AI can expand teacher leadership by freeing time for mentoring and planning. Other studies warn that AI can reduce autonomy if teachers are pressured to follow automated recommendations without room for professional interpretation.
Sustainable AI in teacher education depends on keeping teachers in control of pedagogy. AI should support teacher expertise, not narrow it.
Eight dimensions of sustainable integration
The review identifies eight recurring dimensions needed for responsible AI use in teacher education:
- AI-driven personalization using performance data to tailor teacher training
- Pedagogical enhancement supporting inquiry-based and project-based learning
- Continuous professional development through mentors, feedback systems and simulations
- Ethical and inclusive AI use addressing data privacy and bias reduction
- Collaborative learning tools supporting peer discussion and shared reflection
- Scalable infrastructure ensuring access across different settings
- AI literacy connecting technical knowledge with professional values
- Policy and governance frameworks providing rules, safeguards and institutional readiness
What sustainable implementation requires
A teacher may have access to an advanced tool, but without training, ethical guidance and institutional support, that access may not lead to better teaching. Policymakers need to set clear rules on data privacy, algorithmic fairness and access.
AI literacy should be embedded in teacher preparation, not treated as a short add-on workshop. Pre-service teachers need opportunities to test AI tools, evaluate outputs, design AI-supported lessons and discuss ethical dilemmas.
In-service teachers need continuous professional development rather than one-time training. AI tools change quickly, and teachers need recurring support to adapt. Professional learning should include technical practice, pedagogical design, ethical analysis and opportunities to share examples from real classrooms.
The review supports hybrid teaching models that combine human instruction with AI support. This approach avoids two weak extremes: rejecting AI entirely or handing instructional authority to automated systems. In a sustainable model, teachers use AI to expand planning and feedback while preserving the human relationships and judgment at the center of education.
Research gaps remain
The review is based on selected literature rather than new classroom data. Much of the available research still lacks long-term evidence on how AI affects teacher quality, student outcomes and professional identity over time.
More longitudinal research is needed to understand whether AI-supported teacher preparation produces lasting improvements or merely short-term efficiencies. Comparative studies are also needed across different cultural, institutional and policy contexts.
For educators looking to develop skills in this area, resources like the AI Learning Path for Teachers and AI for Education can provide structured guidance on responsible AI integration in classrooms.
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