Attitudes Toward Artificial Intelligence Among Audiologists and Speech-Language Pathologists: A Comparative Study

Merve İkiz Bozsoy Merve Nur Sarıyer Temelli

Abstract

Purpose: This study aimed to compare the attitudes of audiologists and speech-language pathologists (SLPs) toward artificial intelligence (AI) and to investigate the effect of the participants’ professional characteristics such as education level, duration of professional experience, workplace setting on these attitudes.

Method: A total of 157 professionals from Türkiye, participated in the study, including 87 audiologists (73 females, 14 males) and 70 SLPs (60 females, 10 males). Participants completed a Demographic and Professional Characteristics Information Form, and the General Attitudes to Artificial Intelligence Scale (GAAIS).

Results: Audiologists obtained mean scores of 46.49 ± 6.05 on the Positive GAAIS (P-GAAIS) and 23.52 ± 5.27 on the Negative GAAIS (N-GAAIS). Corresponding scores for SLPs were 45.54 ± 6.23 and 22.12 ± 4.78, respectively. No significant differences were observed between audiologists and SLPs on either subscale (p >.05). Among audiologists, attitudes toward AI did not differ significantly according to education level, years of professional experience, or workplace setting (p >.05). In contrast, P-GAAIS scores differed significantly among SLPs based on workplace setting (p = .01), with post-hoc analyses revealing differences between professionals working in private clinics and those employed in special education centers (p = .007).

Conclusion: The findings indicate that both audiologists and SLPs generally demonstrate positive and comparable attitudes toward AI. These results suggest a favorable professional climate for the adoption of AI-based technologies in audiology and speech-language therapy. Supporting positive attitudes and promoting the evidence-based integration of AI into clinical practice may facilitate its effective and responsible use in these fields.

Keywords

artificial intelligence, attitudes, audiologists, speech-language pathologists, health

References

Aggarwal, K., Ravi, R., & Yerraguntla, K. (2025a). Artificial intelligence and patient care: Perspectives of audiologists and speech-language pathologists. Intelligence-Based Medicine, 11, 1–5. https://doi.org/10.1016/j.ibmed.2025.100214

Aggarwal, K., Ravi, R., & Yerraguntla, K. (2025b). Use of artificial intelligence tools by audiologists and speech-language therapists: An international survey of academicians. Journal of Otology, 20(1), 20–25. https://doi.org/10.26599/JOTO.2025.9540004

Al-Banna, A.-K., Edirisinghe, E., Fang, H., & Hadi, W. (2022). Stuttering disfluency detection using machine learning approaches. Journal of Information & Knowledge Management, 21(02), 2250020. https://doi.org/10.1142/S0219649222500204

Austin, J., Benas, K., Caicedo, S., Imiolek, E., Piekutowski, A., & Ghanim, I. (2024). Perceptions of artificial intelligence and ChatGPT by speech-language pathologists and students. American Journal of Speech-Language Pathology, 34(1), 174–200. https://doi.org/10.1044/2024_AJSLP-24-00218

Boyacı, H., & Söyük, S. (2025). Healthcare workers’ readiness for artificial intelligence and organizational change: A quantitative study in a university hospital. BMC Health Services Research, 25(1), 1–16. https://doi.org/10.1186/S12913-025-12846-Y

ElHennawy, S. M. (2024). The impact of artificial intelligence (AI) in the assessment and treatment of communication disorders (A review of literature). The Egyptian Journal of Language Engineering, 11(2), 36–45. https://doi.org/10.21608/ejle.2024.303151.1069

Frosolini, A., Franz, L., Caragli, V., Genovese, E., de Filippis, C., & Marioni, G. (2024). Artificial intelligence in audiology: A scoping review of current applications and future directions. Sensors, 24(22), 1–21. https://doi.org/10.3390/s24227126

Habib, M. M., Hoodbhoy, Z., & Siddiqui, M. A. R. (2024). Knowledge, attitudes, and perceptions of healthcare students and professionals on the use of artificial intelligence in healthcare in Pakistan. PLOS Digital Health, 3(5), 1–14. https://doi.org/10.1371/journal.pdig.0000443

Hasan, H. E., Jaber, D., Al Tabbah, S., Lawand, N., Habib, H. A., & Farahat, N. M. (2024). Knowledge, attitude and practice among pharmacy students and faculty members towards artificial intelligence in pharmacy practice: A multinational cross-sectional study. PLOS ONE, 19(3), 1–24. https://doi.org/10.1371/journal.pone.0296884

Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), 1–27. https://doi.org/10.3390/su151612451

Kandemir, F., & Azizoğlu, F. (2024). Hemşirelerin yapay zekaya yönelik genel tutumlarının incelenmesi. Yoğun Bakım Hemşireliği Dergisi, 28(2), 113–125. https://doi.org/10.62111/ybhd.1502758

Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497–514. https://doi.org/10.1080/10447318.2022.2151730

Khan Rony, M. K., Akter, K., Nesa, L., Islam, M. T., Johra, F. T., Akter, F., Uddin, M. J., Begum, J., Noor, Md. A., Ahmad, S., Tanha, S. M., Khatun, Most. T., Bala, S. Das, & Parvin, Mst. R. (2024). Healthcare workers’ knowledge and attitudes regarding artificial intelligence adoption in healthcare: A cross-sectional study. Heliyon, 10(23), 1–15. https://doi.org/10.1016/j.heliyon.2024.e40775

Kumi, E., Osei, H. V., Asumah, S., & Yeboah, A. (2024). The impact of technology readiness and adapting behaviours in the workplace: a mediating effect of career adaptability. Future Business Journal, 10(1), 1–19. https://doi.org/10.1186/s43093-024-00355-z

Mahmoud, S. S., Kumar, A., Li, Y., Tang, Y., & Fang, Q. (2021). Performance evaluation of machine learning frameworks for aphasia assessment. Sensors, 21(8), 1–15. https://doi.org/10.3390/s21082582

Perrier, E., Rifai, M., Terzic, A., Dubois, C., & Cohen, J. F. (2022). Knowledge, attitudes, and practices towards artificial intelligence among young pediatricians: A nationwide survey in France. Frontiers in Pediatrics, 10, 1–11. https://doi.org/10.3389/FPED.2022.1065957/BIBTEX

Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 1–13. https://doi.org/10.1016/j.chbr.2020.100014

Schepman, A., & Rodway, P. (2023). The general attitudes towards artificial intelligence scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400

Suvvari, T. K. (2023). The role of artificial intelligence in diagnosis and management of laryngeal disorders (Correspondence). Ear, Nose & Throat Journal, 00(0), 1–2. https://doi.org/10.1177/01455613231175053

Trenerry, B., Chng, S., Wang, Y., Suhaila, Z. S., Lim, S. S., Lu, H. Y., & Oh, P. H. (2021). Preparing workplaces for digital transformation: an integrative review and framework of multi-level factors. Frontiers in Psychology, 12, 1–24. https://doi.org/10.3389/FPSYG.2021.620766/XML

Warren, S. E., Lopez, L. C., Anthony, T., & Coco, L. (2024). Communication public health: An integration of audiology, speech-language pathology, and public health. Journal of Speech, Language, and Hearing Research, 67, 1–18. https://doi.org/10.1044/2024_JSLHR-23-0049

Yüzbaşıoğlu, E. (2021). Attitudes and perceptions of dental students towards artificial intelligence. Journal of Dental Education, 85(1), 60–68. https://doi.org/10.1002/jdd.12385

CURRENT ISSUE

Attitudes Toward Artificial Intelligence Among Audiologists and Speech-Language Pathologists: A Comparative Study

return all articles