AI-Human Collaboration in Teacher Evaluation: A Research Agenda and Future Directions

Teacher evaluation plays a critical role in ensuring educational quality. However, the traditional approaches, such as classroom observations and student surveys, remain limited by inherent subjectivity, high resource consumption, and delayed feedback. While artificial intelligence (AI) offers transformative potential through automated, real-time analysis of instructional data, purely algorithmic methods introduce new challenges related to contextual interpretation, ethical risks, and practical adoption. This paper proposes a novel framework for AI-human collaborative teacher evaluation, designed to synergize the computational efficiency of AI with the nuanced expertise and ethical judgment of human evaluators. The framework establishes dynamic task boundaries, implements explainable workflows across three modes of interaction (Embedding, Copilot, and Agent), and incorporates continuous bias-auditing mechanisms to enhance fairness and adaptability. Planned validation via a mixed-methods approach is expected to demonstrate improvements in evaluation accuracy, efficiency, and teacher receptiveness. By integrating technical innovation with human-centered design and ethical rigor, this study offers a comprehensive foundation for building scalable, culturally adaptive, and pedagogically meaningful evaluation systems.