This conceptual study explores the dual impacts of artificial intelligence on educational evaluation systems. Drawing on recent literature, it highlights benefits like improved monitoring and personalised feedback, while also addressing concerns such as algorithmic bias and privacy issues. The study aims to re-evaluate traditional educational paradigms and explore future education models, with the overarching goal of constructing an educational ecosystem attuned to the digital age. It seeks to bridge the gap between technological advancement and educational reform by examining the synergistic intersection of AI and educational evaluation. Adopting a literature research methodology, it synthesises multidisciplinary frameworks from educational philosophy, data ethics, and assessment theory without empirical validation. The research content points out that while artificial intelligence empowers educational evaluation with values such as data-enhanced outcome evaluation, intelligent process analysis, longitudinal value-added assessment, and multidimensional comprehensive evaluation, it currently brings risks, including ethical dilemmas, personal privacy violations, human-machine relationship imbalances, and algorithmic unfairness. To effectively mitigate these risks, this study proposes strategies including strengthening institutional supply to regulate educational evaluation practices, adhering to the logic of educational evaluation to promote the coupling of value rationality and instrumental rationality, and formulating ethical norms for data governance to establish an optimal educational evaluation paradigm. This research contributes to both theoretical advancement in intelligent education assessment and practical guidelines for policymakers navigating AI integration in educational systems.
Author(s) Details
Yuqing Zhang
College of Teacher Education,
Jiangsu University, Zhenjiang 212000, Jiangsu, China.
Please
see the link:- https://doi.org/10.9734/bpi/mono/978-81-990398-9-6/CH9
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