# Knowledge-Based Design Requirements for Generative Social Robots in Higher Education

arXiv: https://arxiv.org/abs/2602.12873 · HTML: https://mlsanity.com/paper/2602.12873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 78 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [Understanding Large-Language Model (LLM)-powered Human-Robot Interaction](https://mlsanity.com/paper/2401.03217/) (2024)
- [Framework for Adoption of Generative Artificial Intelligence (GenAI) in Education](https://mlsanity.com/paper/2408.01443/) (2024)
- [Exploring persuasive interactions with generative social robots: An experimental framework](https://mlsanity.com/paper/2509.03231/) (2025)
- [Understanding Persuasive Interactions between Generative Social Agents and Humans: The Knowledge-based Persuasion Model (KPM)](https://mlsanity.com/paper/2602.11483/) (2026)
- [The Role of Robot Competence, Autonomy, and Personality on Trust Formation in Human-Robot Interaction](https://mlsanity.com/paper/2503.04296/) (2025)
- [Integrating emotional intelligence, memory architecture, and gestures to achieve empathetic humanoid robot interaction in an educational setting](https://mlsanity.com/paper/2505.19803/) (2025)

## Built on by (most cited first, 1 of 1)

- [Teachy Mini: Development and Preliminary Evaluation of a Knowledge-Based Generative Social Robot for Higher Education](https://mlsanity.com/paper/2607.22345/) (2026)

## Abstract (arXiv)

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.