Knowledge-Based Design Requirements for Generative Social Robots in Higher Education
This work addresses the need for structured knowledge-based design to align generative social robots with pedagogical and ethical expectations in higher education, though it is incremental as it builds on existing frameworks.
The paper tackled the problem of generative social robots lacking knowledge prerequisites for responsible and effective tutoring in higher education, resulting in the identification of twelve design requirements across self-knowledge, user-knowledge, and context-knowledge based on interviews with students and lecturers.
Generative social robots (GSRs) powered by large language models enable adaptive, conversational tutoring but also introduce risks such as hallucinations, 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 systems 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 semi-structured interviews with university students and lecturers, we identify 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, course-related information, and physical learning environment). By identifying these knowledge requirements, this work provides a structured foundation for the design of tutoring GSRs and future evaluations, aligning generative system capabilities with pedagogical and ethical expectations.