AIGC-assisted evaluation of teachers’ tour-guide scripts: Construction and practice of the six-step method

Abstract

Artificial Intelligence Generated Content (AIGC) has garnered significant attention in education due to its strengths in language processing and content creation. It offers a novel technical approach to enhance tools for evaluating tour-guide scripts, which are inherently contextual, audience-oriented, and focused on cultural expression. This paper proposes a six-step method for AIGC-assisted evaluation of teachers' tour-guide scripts within the framework of Technological Pedagogical Content Knowledge (TPACK). The approach leverages the integration of technological knowledge, pedagogical knowledge, and content knowledge to establish a clear human–AI workflow. The six steps include Standard Setting, Standardized Input, AIGC Check, Guided Modification, Comparative Analysis, and Summary and Sharing. The study utilizes vocational students as the research group and employs a combination of teacher–AIGC comparison and mixed methods for evaluation. The focus is on the quantity and types of comments, the focus dimensions, and students’ performance in second-round writing. Results indicate that AIGC is effective in ensuring language accuracy and structural completeness. Teachers demonstrate stronger professional judgment in areas related to context and cultural expression. Their collaboration within the six-step process significantly enhances evaluation efficiency. This method provides a clear, repeatable process model and offers practical support for integrating AIGC into tour-guide script teaching.