Teacher–AI Collaboration and the Professionalization of Teachers in the Age of Automation: A Systematic Review

Teacher–AI collaboration is increasingly present in educational settings, yet little is known about how it is conceptualized in empirical research and what this implies for teacher preparation. This review synthesizes 39 empirical studies on teacher professionalization published between 2015 and 2025 to examine how responsibilities for detecting, diagnosing, and acting on educational data are distributed between teachers and AI systems. Results indicate a predominant focus on learning analytics and natural language processing tools, largely operating at intermediate to high levels of automation. In these configurations, teachers are primarily positioned as interpreters or monitors of AI outputs. In addition, our analysis identifies a consistent pattern: as AI systems assume greater pedagogical autonomy, teacher training described in the literature remains brief, procedural, and largely limited to technical familiarization. These findings suggest that different automation configurations entail distinct competence demands, and that teacher preparation must move beyond technical training to conceptualize teacher–AI collaboration as ongoing professional sensemaking within hybrid intelligent systems grounded in educational values.