Purpose This study aims to construct a quality indicator system for artificial intelligence-generated content (AIGC), enhance users’ ability to identify high-quality AIGC and provide a reference for service providers to optimize their content. Design/methodology/approach This study collected primary data through literature review and in-depth user interviews, and applied grounded theory to conduct three-level coding to identify and extract indicators for evaluating AIGC quality. Based on these indicators, a questionnaire survey and the analytic hierarchy process were used to determine indicator weights and to construct a comprehensive AIGC quality evaluation system. Findings This study identifies four first-level indicators and 21 second-level indicators for evaluating AIGC quality, along with their respective weights. To further highlight the uniqueness of the constructed indicator system, this study also reveals the commonalities and differences in quality assessment dimensions among AIGC, professionally generated content, and user-generated content through comparative analysis. Originality/value This research aims to enrich the academic discussion on AIGC quality indicator system, help users identify high-quality AI-generated content and provide guidance for relevant stakeholders to formulate policies.
Guo et al. (Mon,) studied this question.