Attributional patterns toward students with and without learning disabilities: Artificial intelligence models vs. trainee teachers

Abstract

This study explored differences in the attributional patterns of four advanced artificial intelligence (AI) Large Language Models (LLMs): ChatGPT3.5, ChatGPT4, Claude, and Gemini) by focusing on feedback, frustration, sympathy, and expectations of future failure among students with and without learning disabilities (LD). These findings were compared with responses from a sample of Australian and Chinese trainee teachers, comprising individuals nearing qualification with varied demographic and educational backgrounds. Eight vignettes depicting students with varying abilities and efforts were evaluated by the LLMs ten times each, resulting in 320 evaluations, with trainee teachers providing comparable ratings. For LD students, the LLMs exhibited lower frustration and higher sympathy than trainee teachers, while for non-LD students, LLMs similarly showed lower frustration, with ChatGPT3.5 aligning closely with Chinese teachers and ChatGPT4 demonstrating more sympathy than both teacher groups. Notably, LLMs expressed lower expectations of future academic failure for both LD and non-LD students compared to trainee teachers. Regarding feedback, the findings reflect ratings of the qualitative nature of feedback LLMs and teachers would provide, rather than actual feedback text. The LLMs, particularly ChatGPT3.5 and Gemini, were rated as providing more negative feedback than trainee teachers, while ChatGPT4 provided more positive ratings for both LD and non-LD students, aligning with Chinese teachers in some cases. These findings suggest that LLMs may promote a positive and inclusive outlook for LD students by exhibiting lower judgmental tendencies and higher optimism. However, their tendency to rate feedback more negatively than trainee teachers highlights the need to recalibrate AI tools to better align with cultural and emotional nuances.