PREreview del AI-Mediated Teaching in K–12 Classrooms

AI tools reduce teacher workload in planning and assessment, but they simultaneously displace critical teaching competencies—specifically diagnostic reasoning (understanding student needs), instructional sequencing (ordering learning activities), and evaluative judgment (assessing student work meaningfully).

Teachers maintain agency (control over their work), but it becomes conditional rather than autonomous. Their professional autonomy is now shaped by three factors: institutional pressures to adopt AI, algorithmic opacity (not understanding how AI makes decisions), and their own confidence levels with the technology.

Transparency issues and questions about professional authority aren't abstract ethical debates—they manifest as everyday cognitive and emotional challenges that teachers navigate in their practice. The study argues AI integration must preserve reflective practice and professional judgment to maintain sustainable teacher agency.

Major issues

• Lack of Teacher-Centered Empirical Research - There's insufficient empirical study of teachers' cognitive and professional experiences working with AI across multiple platforms and contexts. Research has focused on student outcomes rather than how teachers actually experience and work with these tools.

• Misapplication of Cognitive Load Theory (CLT) - CLT has been extensively applied to student learning and instructional design, but rarely used to analyze how AI redistributes cognitive responsibilities within teaching itself—a critical gap given that teachers are the primary users of these systems.

• Policy-Practice Disconnect on Ethics - Ethical concerns (algorithmic opacity, data surveillance, professional autonomy) are predominantly discussed at the policy level, with insufficient attention to how these manifest as concrete cognitive and emotional pressures in teachers' daily work. The research treats ethics as external policy issues rather than integral to the cognitive conditions of professional practice.

  Minor issues

• Methodological Gap in Interview Design - While the study uses semi-structured interviews to capture unexpected cognitive and emotional dimensions, there's an implicit acknowledgment that standard research protocols weren't initially designed to capture these "unanticipated issues" around cognitive effort, emotional engagement, and professional identity.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.