Опубликована: Апрель 18, 2025
Язык: Английский
Опубликована: Апрель 18, 2025
Язык: Английский
Systems, Год журнала: 2025, Номер 13(3), С. 174 - 174
Опубликована: Март 3, 2025
With the proliferation of artificial intelligence in education, AI-generated digital educational resources are increasingly being employed as supplements for university teaching and learning. However, this raises concerns about quality content produced. To conduct a comprehensive assessment, paper presents an evaluation index system by combining Delphi method Analytic Hierarchy Process. The initial indicators across dimensions content, expression, user technical aspects identified through systematic literature review recent research. Then, is utilized to modify according experts’ opinions two rounds questionnaire surveys. Subsequently, weight coefficients calculated using Finally, indicator evaluating developed, which comprises four twenty indicators. findings reveal that characteristics critical importance assessing resources, followed expression second most significant factor, with also recognized. Among second-level indicators, “authenticity”, “accuracy”, “legitimacy”, “relevance” accorded greater relative other proposed equips relevant stakeholders framework selecting high-quality AIGDERs steering AI tools line standards. some implications provided support selection guidance on aligning these
Язык: Английский
Процитировано
0Humanities and Social Sciences Communications, Год журнала: 2025, Номер 12(1)
Опубликована: Март 8, 2025
Язык: Английский
Процитировано
0Education and Information Technologies, Год журнала: 2025, Номер unknown
Опубликована: Март 13, 2025
Язык: Английский
Процитировано
0Applied Sciences, Год журнала: 2025, Номер 15(7), С. 3886 - 3886
Опубликована: Апрель 2, 2025
The advent of generative artificial intelligence (GAI) technologies has significantly influenced the educational landscape. However, public perceptions and underlying emotions toward intelligence-generated content (AIGC) applications in education remain complex issues. To address this issue, study employs LDA network opinion topic mining SnowNLP sentiment analysis to comprehensively analyze over 40,000 comments collected from multiple social media platforms China. Through a detailed data, examines distribution positive negative identifies six topics. further utilizes visual tools such as word clouds heatmaps present research findings. results indicate that emotional polarity across all topics is characterized by predominance ones. Moreover, an keywords reveals each its own emphasis, yet there are overlaps between them. Therefore, study, through quantitative methods, also reflects interconnections among elements within ecosystem. Additionally, integrates identified with Technology–Organization–Environment (TOE) framework explore broad impact AIGC on perspectives technology, organization, environment. This provides novel perspective attitudes key concerns Chinese regarding use education.
Язык: Английский
Процитировано
0Опубликована: Апрель 18, 2025
Язык: Английский
Процитировано
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