Speech and Language Therapy Students Show High AI Literacy but Clinical Hesitancy

Speech and language therapy students in Türkiye report strong technical literacy in artificial intelligence, yet many remain uncertain about how the technology should enter clinical practice, according to a descriptive and correlational study published in BMC Medical Education. The research, led by Eren Balo and colleagues, surveyed 412 undergraduate speech-language therapy (SLT) students from multiple universities, with a mean age of 21.5 years (SD = 3.43) and a heavily female sample of 366 women and 45 men. Participants completed the Artificial Intelligence Literacy Scale and the Artificial Intelligence Awareness Survey, and the researchers analyzed the results using descriptive statistics, Kruskal-Wallis tests to compare grade levels, Mann-Whitney U tests for prior AI exposure, and Spearman correlation and chi-square tests to probe relationships among subscales and categorical variables.

The headline finding was that participants demonstrated high overall AI literacy scores, and, notably, these scores did not differ significantly across academic years. Students further along in their programs were not measurably more literate about AI than those earlier in their training, which the authors interpret as evidence that current SLT curricula are not systematically building AI competence as students progress. In other words, whatever literacy these students acquired appeared to be independent of how far they had advanced in their degree, pointing to a gap between the accelerating presence of AI in healthcare and the structured instruction students receive about it.

The correlational analysis revealed a technically interesting pattern within the literacy scale. All subscales correlated positively with one another, but the strength of those associations varied in ways that carry practical meaning. The strongest positive correlation appeared between the “Use” and “Evaluation” subscales (ρ = .702, p < .001), indicating that students who used AI tools more frequently were also better at assessing the quality of the outputs those tools produced. Familiarity with the technology, in this dataset, traveled hand in hand with the ability to judge it.

A very different picture emerged for ethics. The correlation between the “Ethics” and “Use” subscales was weak (ρ = .225, p < .001), a result the authors highlight as showing that technical proficiency does not necessarily correspond with ethical awareness. A student can become skilled at operating AI systems without developing a comparable grasp of the ethical questions those systems raise, and the data suggest that hands-on exposure alone is unlikely to close that gap. This dissociation between usage and ethical reasoning is one of the study’s clearest arguments for teaching the two dimensions separately rather than assuming one follows from the other.

The awareness survey added a layer of nuance to the literacy findings. A notable proportion of participants remained neutral on several items, signaling caution and uncertainty rather than outright rejection or enthusiasm. At the same time, more than 70% expressed excitement about AI advancements and agreed that AI would enhance their professional skills. The coexistence of high literacy, majority excitement, and widespread neutrality suggests a student population that is engaged with the technology but hesitant about its clinical integration, and the authors conclude that this hesitancy reflects a perception that current knowledge is insufficient for practical application.

Grade level mattered in one specific and career-relevant way. Senior students expressed stronger concerns about AI’s potential impact on future demand for SLT services, an apprehension the authors emphasize as deserving attention in professional training. Students approaching graduation appear to be weighing what AI adoption could mean for their professional prospects, and the study frames this career-related worry as a reason to pair AI literacy instruction with explicit discussion of how the technology may reshape clinical roles rather than simply replace or reduce them.

Taken together, the findings support the authors’ central recommendation: integrating structured AI curricula into SLT education, with a focus on professional and ethical dimensions alongside technical skills. Because literacy did not increase with academic year, and because ethical awareness did not track technical use, the study argues that these competencies require deliberate, designed instruction rather than incidental exposure. The authors position AI literacy and career-related reassurance as complementary priorities for preparing future clinicians, noting that students who understand both the capabilities and the limits of AI will be better equipped for clinical practice as the technology’s role in healthcare continues to grow.

As a descriptive and correlational study of undergraduate students in Türkiye, the work captures a snapshot of one professional pipeline at a moment of rapid technological change, and its scope is limited to self-reported literacy and awareness rather than measured clinical performance. Even so, the pattern it documents, high literacy without grade-level progression, strong usage-evaluation coupling, weak usage-ethics coupling, and senior-year career anxiety, offers a concrete empirical basis for curriculum designers in speech-language therapy and, by extension, in allied health professions facing similar questions about how to ready their students for AI-integrated practice.

Subject of Research: AI literacy and awareness among speech-language therapy students in Türkiye

Article Title: Is our future ready for the future? Investigating speech and language therapy students’ literacy and awareness of artificial intelligence: a descriptive and correlational study

Article References: Balo, E., Geçay, S. E., & Selvi-Balo, S. (2026). Is our future ready for the future? Investigating speech and language therapy students’ literacy and awareness of artificial intelligence: a descriptive and correlational study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10606-y

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10606-y

Keywords: artificial intelligence, speech-language therapy, AI literacy, health professions education, BMC Medical Education, correlational study, clinical education, ethics, curriculum, Türkiye, undergraduate students, career concerns