AI Literacy Linked to Positive Attitudes While AI Anxiety Is Associated with Negative Views in Speech-Language Therapy Students

A cross-sectional survey of 313 fourth-year undergraduate speech-language therapy students from 24 departments across Türkiye has found that self-reported AI literacy and AI anxiety relate to attitudes toward artificial intelligence in distinctly different ways. The study, published in BMC Medical Education, examined how these two factors independently predict both positive and negative attitudes toward AI among students preparing for clinical careers.

The researchers used the General Attitudes towards Artificial Intelligence Scale, analysing its two dimensions separately. Multiple regression models adjusted for age, gender, frequency of AI use, self-directed AI learning, and clinical-purpose AI use. Standard errors were clustered by department, and wild cluster bootstrap inference was used to strengthen the statistical analysis.

The results showed a clear pattern. The observed self-reported AI-literacy composite was associated with more positive attitudes toward AI, with a coefficient of B = 0.433 (β = 0.373, p < .001). AI anxiety, however, showed no such association with positive attitudes. Conversely, AI anxiety was strongly associated with more negative attitudes toward AI (B = −0.624, β = −0.674, p < .001), while the literacy composite was not associated with negative attitudes.

One secondary finding emerged from the analysis: clinical-purpose AI use showed a smaller association with less negative attitudes (B = 0.148, β = 0.121, bootstrap p = .003). By contrast, the estimate for self-directed AI learning was inconclusive, meaning the data could not clearly determine whether self-directed learning influenced attitudes in either direction.

The two focal associations remained consistent in direction and statistical interpretation across sensitivity analyses, and their magnitudes were broadly similar throughout.

The authors are careful to frame these results appropriately. The findings describe concurrent adjusted associations rather than temporal or causal pathways. In other words, the study cannot determine whether higher self-reported literacy leads to more positive attitudes, whether positive attitudes encourage students to rate their own literacy higher, or whether other factors influence both. The cross-sectional design captures a single point in time and cannot establish causation.

An important limitation concerns the measurement instrument itself. The study’s conclusions should be interpreted in light of the weak measurement structure of the AI-literacy instrument in this sample. Because the literacy measure relied on students’ self-assessments rather than objective tests of AI knowledge, and because its psychometric properties were weak in this population, the literacy findings warrant particular caution.

The study addresses a gap in the evidence base. As the authors note in the background, self-reported AI literacy and AI anxiety may be associated with how future speech-language therapists evaluate AI, but evidence specific to this population had been limited.

For the field of medical education, the study offers a measured contribution: among these Turkish speech-language therapy students, positive and negative attitudes toward AI appear to track different psychological variables. The authors emphasize that these are associations observed at one point in time, adjusted for several student characteristics, and should not be read as evidence that interventions targeting literacy or anxiety would change attitudes over time.

Subject of Research: Cross-sectional associations of self-reported AI literacy and AI anxiety with AI attitudes among speech-language therapy students in Türkiye

Article Title: Self-reported AI literacy and AI anxiety as correlates of AI attitudes among speech-language therapy students in Türkiye: a cross-sectional study

Article References: Seçkin Yılmaz, Ş., & Birol, N. Y. (2026). Self-reported AI literacy and AI anxiety as correlates of AI attitudes among speech-language therapy students in Türkiye: a cross-sectional study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10280-0

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10280-0

Keywords: AI literacy, AI anxiety, attitudes toward AI, speech-language therapy, cross-sectional study, Türkiye, medical education, undergraduate students, survey research, regression analysis, Self-reported, literacy