教学生如何批判性使用生成式AI设计系统，避免盲目依赖。
Embedding Generative AI into Systems Analysis and Design Curriculum: Framework, Case Study, and Cross-Campus Empirical Evidence
- 设计SAGE框架，引导学生判断何时采纳、修改或拒绝AI建议。
- 84%学生能主动评估AI输出，但无人主动发现人机共同遗漏的问题。
- 强调文档化决策理由与嵌入可访问性提示，提升教学实效。
系统分析课程中学生越来越多地使用生成式AI，但现有教学缺乏系统性方法来培养负责任的AI协作能力，导致学生可能盲目接受AI建议而忽视用户需求或上下文适切性。本文提出SAGE（结构化AI引导教育）框架，将生成式AI融入课程设计，训练学生在何时接受、修改或拒绝AI贡献方面做出判断。在四所澳大利亚高校的18个学生小组中实施后发现，84%的学生超越了被动接受，展现出选择性判断能力，但无一主动识别出人类与AI分析均忽略的盲点，表明存在能力上限。善于解释决策的学生在整合信息源方面表现更佳，具备深厚领域知识的学生始终关注可访问性问题。尽管85%的小组在编写需求时明确考虑老年人和文化需求，仍有55%难以识别AI对系统边界误判（内外部界定错误），45%遗漏数据管理错误（信息存储与更新方式），55%忽略异常处理缺失。对教育者的三条启示：(i) 要求学生记录每条AI建议的采纳/修改/拒绝理由，使推理显性化；(ii) 在每个开发阶段嵌入可访问性提示，因意识会因缺乏持续支持而消退；(iii) 让学生先自行制定规范，再与AI版本对比，并以研究或标准为锚点识别差距。
原文摘要 · Abstract (English)
Systems analysis students increasingly use Generative AI, yet current pedagogy lacks systematic approaches for teaching responsible AI orchestration that fosters critical thinking whilst meeting educational outcomes. Students risk accepting AI suggestions blindly or uncritically without assessing alignment with user needs or contextual appropriateness. SAGE (Structured AI-Guided Education) addresses this gap by embedding GenAI into curriculum design, training students when to accept, modify, or reject AI contributions. Implementation with 18 student groups across four Australian universities revealed how orchestration skills develop. Most groups (84\%) moved beyond passive acceptance, showing selective judgment, yet none proactively identified gaps overlooked by both human and AI analysis, indicating a competency ceiling. Students strong at explaining decisions also performed well at integrating sources, and those with deep domain understanding consistently considered accessibility considerations. Accessibility awareness proved fragile. When writing requirements, 85\% of groups explicitly considered elderly users and cultural needs. Notably, 55\% of groups struggled identifying when AI misclassified system boundaries (what belongs inside versus outside the system), 45\% missed data management errors (how information is stored and updated), and 55\% overlooked missing exception handling. Three implications emerge for educators: (i) require students to document why they accepted, modified, or rejected each AI suggestion, making reasoning explicit; (ii) embed accessibility prompts at each development stage because awareness collapses without continuous scaffolding; and (iii) have students create their own specifications before using AI, then compare versions, and anchor to research or standards to identify gaps.
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