面向高等教育的生成社交机器人知识基础设计需求
Knowledge-Based Design Requirements for Generative Social Robots in Higher Education
AI总结：
本文基于十二次半结构化访谈，识别出生成社交机器人在高等教育中需满足的十二项设计要求，涵盖自我知识、用户知识和情境知识，为设计负责任且有效的教学GSR提供结构化基础。
摘要：
生成社交机器人（GSR）通过大语言模型实现自适应对话教学，但也带来误导、过度依赖和隐私侵犯等风险。现有教育技术与负责任AI框架主要定义期望行为，但很少规定使生成代理可靠表达这些行为所需的知识前提。本文采用知识导向设计视角，探讨面向教学的GSR在高等教育中需具备哪些信息以负责任和有效运作。基于十二次半结构化访谈，我们识别出涵盖自我知识（自信、自律和友好的个性，可定制角色）、用户知识（个性化学生学习目标、学习进度、动机类型、情绪状态和背景）和情境知识（学习材料、教育策略、课程相关信息和物理学习环境）的十二项设计要求。本文为设计教学GSR提供结构化基础，将生成AI能力与教学和伦理期望对齐。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
