Large language models for autism: evaluating theory of mind tasks in a gamified environment

Autism Spectrum Disorder often significantly affects reciprocal social communication, leading to difficulties in interpreting social cues, recognizing emotions, and maintaining verbal interactions. These challenges can make everyday conversations especially demanding. To support autistic people in developing their social competence and communication abilities, we propose an interactive game specifically designed to enhance social understanding. By incorporating gamification elements and a user-centered design approach, the application aims to balance clinical relevance with high usability, ensuring it remains accessible, engaging, and beneficial for anyone seeking to improve their social skills. Large Language Models have recently been assessed for their ability to detect sarcasm and irony within Theory of Mind tasks, showing performance comparable to that of trained psychologists. However, a significant limitation remains: their dependence on traditional "black box" AI architectures, which often lack explainability, interpretability, and transparency. This limitation is particularly concerning when people with and without Autism Spectrum Disorder use these models to learn and practice social skills in safe, virtual environments. This study investigates and compares the performance of Large Language Models and human experts in evaluating Theory of Mind tasks, providing a detailed comparative analysis. A total of 21 participants engaged with our game, and their responses were assessed by four human experts alongside GPT-4o. The results indicate that GPT-4o matches human experts in both adherence to instructional criteria and evaluation accuracy, with no statistically significant differences observed. These findings underscore the potential of LLMs to support scalable, always-available social training systems that are accessible from anywhere.