The potential of Large Language Models for social robots in special education

Abstract Large language models (LLMs) have created remarkable possibilities for analyzing and generating language data and have been integrated into several fields aiming to transform them, including education. While most research efforts focus on LLMs in typical education or social robots, limited applications of LLMs have been reported in special education. Moreover, there is a profound lack of combined research in LLM-based social robots in special education. In this work, we argue that although LLMs and social robots have demonstrated their potential to advance special education separately, their combination is not yet fully exploited, and further research is required to enable such use. The first objective of this work is to review relevant literature to assess the feasibility of developing LLMs on social robot platforms for use in special education. The second objective of this work is to reveal related challenges, limitations, opportunities, and ethical considerations to provide insights, aiming to subsequently formulate guidelines for the efficient integration of LLM-based social robots into special education practices. To this end, the third objective of this work is to propose a thoughtful framework, aiming to formulate a safe and inclusive learning environment for students in special education, suggesting actionable steps that could be followed by educators, developers and stakeholders, towards address the unique needs and challenges of students with diverse learning requirements.