This paper examines a human–ML framework for large-scale qualitative data coding. Using a nationally representative sample of transcript data, the study evaluates semantic embeddings and ranked recommendations through validation and user testing. Results demonstrate improved efficiency and accuracy while maintaining human expertise, oversight, and responsibility for final coding decisions.

This paper describes a framework for human–AI collaboration in educational measurement that connects four workflow components. The framework integrates educational measurement and responsible AI principles, emphasizing AI tools as support for human expertise. Illustrative applications demonstrate how human–AI collaboration can strengthen assessment processes while maintaining measurement quality and integrity.