| 许可，方波，罗涛.人工智能大模型技术赋能增值评价的现实困境、理论框架与技术路向[J].陕西学前师范学院学报,2024,40(11):80-89 |
| 人工智能大模型技术赋能增值评价的现实困境、理论框架与技术路向 |
| Realistic Difficulties, Theoretical Framework and Technical Development Direction of Value-added Evaluation of Large Model Technology of Artificial Intelligence |
| 投稿时间：2024-08-13 修订日期：2024-08-23 |
| DOI：10. 11995/j. issn. 2095-770X. 2024. 11. 010 |
| 中文关键词: 人工智能 大模型 增值评价 理论框架 技术路向 |
| 英文关键词: Artificial Intelligence Large model Value-added Evaluation Theoretical Framework Technical Direction |
| 基金项目:四川高等职业教育研究中心2024年度科研项目（GZY24B32）；四川省教育信息化与大数据中心2024年度研究课题（DSJZXKT326）；四川省教师教育研究中心2024年度课题（TER2024-028） |
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| 中文摘要: |
| 教育数字化转型背景下，人工智能大模型赋能增值评价已成为评价改革的重要内容。通过梳理国内增值评价研究与应用的四个演变阶段，针对现阶段增值评价中存在的评价目标取向错位、评价内容循证不足、评价方式算法殊异、评价结果反馈延迟的现实困境，提出涵盖评价目标、评价内容、评价方式、评价结果的人工智能大模型技术赋能增值评价4M理论框架。基于该理论框架，从人工智能大模型的理念、数据、算法、算力四个层面的技术逻辑出发，提出技术路径，支撑人工智能大模型赋能增值评价的实现。 |
| 英文摘要: |
| Under the background of digital transformation of education, value-added evaluation empowered by artificial intelligence large model has become an important content of evaluation reform. By combing through the four evolution stages of domestic value-added evaluation research and application, and aiming at the realistic difficulties existing in the current stage of value-added evaluation, such as misplaced evaluation goal orientation, insufficient evidence-based evaluation content, redundant evaluation methods and algorithms, and delayed feedback of evaluation results, A 4M theoretical framework of value-added evaluation empowered by AI large model technology is proposed. It covers evaluation objectives, evaluation contents, evaluation methods and evaluation results. Based on the theoretical framework, from the technical logic of the four aspects of the concept, data, algorithm and computing power of the AI big model, the technical path is proposed to support the realization of the value-added evaluation of the AI large model. |
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