为高校教学机器人设计知识框架，确保其可靠且负责任地辅导学生。
Knowledge-Based Design Requirements for Generative Social Robots in Higher Education
- 从师生访谈中提炼出三类关键知识：自我认知、用户信息与学习环境。
- 明确机器人需掌握学生目标、情绪、进度等个性化信息才能有效辅导。
- 适合教育AI开发者与智能教学系统设计者参考。
由大语言模型驱动的生成式社交机器人（GSRs）可实现自适应对话式辅导，但也存在误导信息、过度依赖和隐私泄露等风险。现有教育技术与负责任AI框架多定义理想行为，却很少规定实现这些行为所需的知识前提。为此，本文采用基于知识的设计视角，通过12场半结构化访谈（对象为大学生与教师），识别出三类核心知识要求：自我知识（可定制的角色性格，如自信、负责、友好）、用户知识（学生的学习目标、进度、动机类型、情绪状态及背景信息）以及情境知识（课程材料、教学策略、课程相关信息及物理学习环境）。研究结果为教学型GSR的设计提供了结构化基础，使生成式AI能力与教学与伦理期望相匹配。
原文摘要 · Abstract (English)
Generative social robots (GSRs) powered by large language models enable adaptive, conversational tutoring but also introduce risks such as misinformation, overreliance, and privacy violations. Existing frameworks for educational technologies and responsible AI primarily define desired behaviors, yet they rarely specify the knowledge prerequisites that enable generative agents to express these behaviors reliably. To address this gap, we adopt a knowledge-based design perspective and investigate what information tutoring-oriented GSRs require to function responsibly and effectively in higher education. Based on twelve semistructured interviews with university students and lecturers, we identified twelve design requirements across three knowledge types: self-knowledge (assertive, conscientious, and friendly personality with customizable role), user-knowledge (personalized information about student learning goals, learning progress, motivation type, emotional state, and background), and context-knowledge (learning materials, educational strategies, courserelated information, and physical learning environment). Drawing from these results, this work provides a structured foundation for the design of tutoring GSRs, aligning generative AI capabilities with pedagogical and ethical expectations.
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