Autistic traits, gender and age interact to influence attitudes towards Artificial Intelligence
We investigated the influence of neurodiversity on attitudes towards AI and how the effect is moderated by gender and age. Attitudes towards AI, which strongly predict how people engage with the technology, vary widely among individuals. While existing research has primarily focused on the general population, there is an increasing need to explore how specific groups that may benefit in particular from AI, such as autistic people, perceive and interact with it. We administered the Attitudes Towards Artificial Intelligence Scale in a web-based format to 1057 German-native speakers, comprising people with a self-reported autism diagnosis (n = 29), people who self-identified as autistic without a formal diagnosis (n = 86), and non-autistic participants (n = 942). Group means showed that self-identified autistic participants reported significantly less positive attitudes towards AI than diagnosed and non-autistic participants. In hierarchical regressions, diagnosis emerged as a positive predictor of general AI attitudes, when controlling for gender, age, and autistic traits. Trait analyses revealed that greater social difficulties and sensory sensitivities predicted less favourable attitudes towards AI, while other trait dimensions were not significantly associated. Both gender and age moderated these effects: attitudes declined with increasing traits in both groups, but more slowly in females, and the relationship between autistic traits and AI attitudes was more positive among younger compared to older participants. While some effect sizes were small, these results suggest that neurodiversity, gender, and age play a role in shaping AI perceptions. • Self-identified people show more negative AI views than diagnosed and non-autistic. • Social and sensory difficulties predict less favourable AI attitudes. • Gender moderates attitudes: trait–attitude link is weaker among females. • Age moderates attitudes: younger autistic people show more AI positivity.
Authors
- Nathan Caruana (ORCID: https://orcid.org/0000-0002-9676-814X)
- Outi Tuomainen (ORCID: https://orcid.org/0000-0002-8654-2446)
- Friederike Charlotte Hechler (ORCID: https://orcid.org/0009-0000-4577-0496)
Institutions
- Flinders University (AU)
- University of Potsdam (DE)
- Macquarie University (AU)
Publication Details
- Journal
- Computers in Human Behavior Reports
- Published
- 2025-11-08
- DOI
- https://doi.org/10.1016/j.chbr.2025.100847
- Primary Topic
- Autism Spectrum Disorder Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00