生成式人工智能赋能中职财经商贸类课程学业评价的内在逻辑、场景风险与优化路径

摘要

职业教育数字化转型与教育评价改革的推进，对中职课程学业评价提出了由结果判定走向过程诊断、由知识考查走向能力评价的新要求。中职财经商贸类课程具有岗位导向鲜明、操作规范严格、数据属性敏感和职业素养要求较高等特点，传统学业评价在评价内容、过程证据、反馈方式和职业情境融入等方面仍存在不足。生成式人工智能为学习过程留痕、个性化反馈、职业任务情境创设和评价证据整合提供了新的技术条件，但也可能引发合规性误判、成果真实性削弱、数据安全隐患、技术依赖以及职业素养判断不足等风险。本文基于文献研究、逻辑分析与场景分析认为，生成式人工智能赋能中职财经商贸类课程学业评价的关键，不在于替代教师评分，而在于辅助教师更准确地识别学生在职业任务中的知识运用、技能表现、规范意识和反思能力。为此，应构建“AI初评—学生修正—教师终评”的评价流程，完善“知识—技能—规范—反思”多维评价标准，形成“原始作品—修改过程—AI反馈—教师意见”的评价证据链，推动中职财经商贸类课程学业评价向过程性、诊断性和素养导向转型。

Abstract

With the advancement of digital transformation in vocational education and the reform of educational evaluation, academic evaluation in secondary vocational schools is increasingly expected to shift from result-oriented judgment to process-oriented diagnosis, and from knowledge-based assessment to competence-based evaluation. Finance and commerce courses in secondary vocational schools are characterized by strong occupational orientation, strict operational norms, sensitive data attributes, and high requirements for professional literacy. However, traditional academic evaluation still faces problems such as an excessive emphasis on knowledge outcomes, insufficient process evidence, generalized feedback, and weak integration of occupational contexts. Generative artificial intelligence provides new technical conditions for recording learning processes, offering personalized feedback, creating occupational task scenarios, and integrating evaluation evidence. Nevertheless, its application may also bring risks such as compliance misjudgment, weakened authenticity of student work, data security concerns, technological dependence, and insufficient judgment of professional literacy. Based on literature review, logical analysis, and scenario analysis, this paper argues that the key role of generative artificial intelligence in empowering academic evaluation of finance and commerce courses in secondary vocational schools is not to replace teachers’ assessment, but to assist teachers in more accurately identifying students’ knowledge application, skill performance, normative awareness, and reflective competence in occupational tasks. Therefore, it is necessary to construct an evaluation process of “AI preliminary assessment—student revision—teacher final assessment,” establish multidimensional evaluation standards covering knowledge, skills, norms, and reflection, and build an evidence chain consisting of original work, revision process, AI feedback, and teacher comments. These measures can promote the transformation of academic evaluation toward process orientation, diagnostic function, and professional competence development.

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