Personalizing AI Tools for Second Language Speaking: The Role of Gender and Autistic Traits

Yiran Du ^1* Chenghao Wang ² Bin Zou ^2* Yinan Xia ² Yuan Ji ³ Zifei Liu ² Zheni Yan ²

• ¹ Harvard University, Cambridge, United States
• ² Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, China
• ³ University of Liverpool, Liverpool, North West England, United Kingdom

This study investigates the interplay between autistic traits, gender, and the perception of artificial intelligence (AI) tools designed for second language (L2) speaking practice, contributing to a deeper understanding of inclusive educational technology. A sample of 111 university students completed the Broad Autism Phenotype Questionnaire (BAPQ) to measure autistic traits (AU) and their sub-traits Aloof (AF), Rigid (RD), and Pragmatic Language (PL). Perceptions of AI tools were assessed across five dimensions: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude (AT), Behavioral Intention (BI), and Usage Behavior (UB). The study utilized correlation and regression analyses to examine relationships between these variables, while exploring gender-specific moderating effects. Key findings revealed no significant gender differences in autistic traits or overall perceptions of AI tools. Contrary to expectations, autistic traits were negatively correlated with perceptions of AI tools, suggesting that current AI designs may not adequately support individuals with pronounced autistic traits. Additionally, gender moderated some relationships, with males displaying stronger associations between autistic traits and both PEOU and UB. This research bridges critical gaps by linking neurodiversity and gender to technology acceptance, advancing the field's understanding of individual differences in AI-based language learning. It underscores the importance of designing personalized and adaptive educational tools that address diverse learner needs, promoting inclusivity and effectiveness in L2 practice.