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.

翻译：由大语言模型驱动的生成式社交机器人（GSRs）具备自适应对话式辅导能力，但也带来了错误信息、过度依赖及隐私侵犯等风险。现有教育技术与负责任人工智能框架主要定义理想行为，却鲜有阐明使生成式智能体可靠表达这些行为所需的知识前提。为填补这一空白，我们采用基于知识的设计视角，探究面向辅导的GSRs在高等教育中负责任且有效运行所需的信息。通过对12位大学生与教师的半结构化访谈，我们梳理出涵盖三类知识的12项设计需求：自我知识（具有可定制角色的自信、尽责、友好型人格）、用户知识（学生个性化信息——学习目标、学习进度、动机类型、情绪状态及背景）与情境知识（学习材料、教育策略、课程相关信息及物理学习环境）。基于这些成果，本文为辅导型GSRs的设计提供了结构化基础，使生成式AI能力与教学及伦理期望相协调。