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当前位置：中国现代教育学报 > 2026年7-8月-2卷4期 > 文章详情

Open Access Article

生成式人工智能时代高校教育评价的证据危机、过程确证与制度重构

Evidence Crisis, Process Confirmation, and Institutional Reconstruction of Higher Education Assessment in the Era of Generative Artificial Intelligence

作者：黎银霞

单位：广州商学院

卷期：2026年7-8月-2卷4期

稿件号：202607148328

DOI：10.70693/202607148328

页码：97-101

发布时间：2026-08-04

总浏览量：198

摘要

生成式人工智能正在深度嵌入高校课程学习、论文写作、科研训练与知识生产过程，使传统以终结性成果为主要依据的教育评价面临证据效度危机。在人机共同生产条件下，论文、代码、报告和项目方案往往由学生能力、模型能力、提示策略、平台资源和外部资料共同生成，作品质量与学生真实能力之间的推断关系不再稳定。文章在高校数字化转型、人工智能教育应用、形成性评价和教育治理研究基础上，按照研究前沿、问题提出、问题分析和解决方案的逻辑，提出高校教育评价应从结果中心转向过程确证。过程确证并非以过程评价替代结果评价，而是围绕明确的能力主张，综合AI使用披露、生成过程记录、关键决策说明、现场表现和迁移性任务等多源证据，对学生真实学习、实质贡献和责任承担进行交叉验证。高校应在任务分级、AI使用披露、过程证据链、能力表现校验与责任数据治理之间建立制度闭环，在创新、诚信、公平与育人价值之间形成新的评价平衡。

关键词

生成式人工智能；高校教育评价；评价效度；过程证据；能力确证；人机协同

Abstract

Generative artificial intelligence is becoming deeply embedded in university course learning, thesis writing, research training, and knowledge production, creating a crisis of evidential validity for traditional educational assessment that relies mainly on summative outcomes. Under conditions of human-AI co-production, papers, codes, reports, and project proposals are often jointly generated by students’ abilities, model capabilities, prompting strategies, platform resources, and external materials. As a result, the inferential relationship between the quality of a submitted work and students’ actual abilities is no longer stable. Drawing on research on university digital transformation, AI applications in education, formative assessment, and educational governance, this paper follows the logic of research frontiers, problem formulation, problem analysis, and solution design, and argues that higher education assessment should shift from outcome-centered evaluation to process confirmation. Process confirmation does not replace outcome assessment with process assessment. Rather, around clearly defined competence claims, it integrates multiple sources of evidence, including AI-use disclosure, records of the generation process, explanations of key decisions, on-site performance, and transfer tasks, in order to cross-validate students’ authentic learning, substantive contribution, and responsibility-taking. Universities should establish an institutional closed loop among task classification, AI-use disclosure, process evidence chains, competence performance verification, and responsibility-data governance, thereby forming a new assessment balance among innovation, integrity, fairness, and educational value.

Keywords

generative artificial intelligence; higher education assessment; assessment validity; process evidence; competence confirmation; human-AI collaboration

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引用本文

黎银霞. 生成式人工智能时代高校教育评价的证据危机、过程确证与制度重构[J]. 中国现代教育学报. 2026, 2 (4): 97-101. DOI: 10.70693/202607148328.

APA引用

黎银霞. (2026). 生成式人工智能时代高校教育评价的证据危机、过程确证与制度重构. 中国现代教育学报, 2 (4), 97-101. https://doi.org/10.70693/202607148328

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