Balancing Artificial Intelligence and Human Guidance in Teacher Education: A Quasi-Experimental Study

This study investigates how artificial intelligence (AI)–mediated support and instructor guidance shape preservice mathematics teachers’ relational and procedural understanding. Using a quasi-experimental design with 76 participants across two universities, the study compared learning outcomes in AI-supported and instructor-led settings. Participants generated relational and procedural examples and explained their reasoning; responses were evaluated for both accuracy and originality. Findings revealed a trade-off: AI-supported participants produced more accurate examples but frequently reproduced content verbatim, thereby reducing originality, whereas instructor-supported participants exhibited more misconceptions yet demonstrated independent reasoning and initiative. These results suggest that AI can enhance efficiency and precision while limiting opportunities for critical engagement and intellectual autonomy. The study contributes to broader debates on the role of educational technology in higher education, highlighting the potential of hybrid instructional models that combine AI scaffolds with human facilitation. Such models can emphasize explanation, variation, and accountability while preserving creativity. Policy implications include the need for assessment designs that reward generative reasoning, transparent disclosure of AI use, and guidance for teacher-education programs to integrate AI responsibly. Overall, the research underscores the importance of balancing technological support and human guidance to achieve both accuracy and originality in teacher preparation.