The quality evaluation index system for AI-generated digital educational resources

Abstract

With the rapid development of Generative AI (GAI) technology, AI-generated content (AIGC) has become the main mode of content creation, replacing User-Generated Content (UGC) and Professional Generated Content (PGC). This transformation has led to the adoption of a brand-new form for AI-generated digital educational resources (AIGDER). However, concerns have arisen regarding the quality of generated content. To address this issue, this paper proposes a comprehensive evaluation index system for AIGDER by integrating the Delphi method and the entropy weight method. Firstly, through a systematic review of recent literature, potential quality indicators covering content, expression, user aspects, and technical aspects were identified. Subsequently, these indicators were refined through two rounds of expert consultation using the Delphi method, and finally, a structured quality indicator system was established, including four dimensions and twenty specific indicators. After that, the weight coefficients of the quality indicators were determined using the entropy weight method, and the importance of each indicator was analyzed. The proposed system provides a framework for relevant stakeholders to select high-quality AIGDER (AI educational resources) and make AI tools conform to educational standards.

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