Sağlıkla İlişkili Önlisans Öğrencilerinde Üretken Yapay Zekâ Sohbet Robotu Kullanımı, Akademik Başarı ve Algılanan Stres

Öz

AMAÇ: Bu çalışmada, sağlıkla ilişkili önlisans programlarında öğrenim gören öğrencilerde üretken yapay zekâ sohbet robotu kullanımının akademik başarı ve algılanan stres ile ilişkisini değerlendirmek amaçlandı. GEREÇ VE YÖNTEM: Bu kesitsel gözlemsel çalışma, Türkiye’deki bir kamu sağlık hizmetleri meslek yüksekokulunda Haziran-Temmuz 2025 tarihleri arasında yürütüldü. Çevrim içi ankete elektronik onam veren 877 öğrenci dahil edildi. Güz dönemi üretken yapay zekâ sohbet robotu kullanımı, güz dönemi not ortalaması kategorisi ile; son bir ay kullanımı ise 14 maddelik Algılanan Stres Ölçeği toplam puanı ile eşleştirildi. Akademik başarı orantılı odds ordinal lojistik regresyonla, algılanan stres ise heteroskedastisiteye dayanıklı (HC3) standart hatalar kullanılan doğrusal regresyonla analiz edildi. Modeller yaş, cinsiyet ve sınıf yılına göre düzeltildi. BULGULAR: Öğrencilerin %72,9’u güz döneminde, %59,6’sı son bir ayda üretken yapay zekâ sohbet robotu kullandığını bildirdi. Düzeltilmiş analizlerde güz dönemi kullanımının daha yüksek not ortalaması kategorileriyle ilişkisi anlamlı değildi (OR = 1,07; %95 GA: 0,82-1,40; p = 0,615). Son bir ay kullanımının algılanan stres toplam puanı ile ilişkisi de anlamlı bulunmadı (β = 0,76; %95 GA: -0,27-1,79; p = 0,150). Kullanım sıklığı ile not ortalaması kategorisi arasında anlamlı fark veya doğrusal eğilim gözlenmedi. SONUÇ: Sağlıkla ilişkili önlisans öğrencilerinde üretken yapay zekâ sohbet robotu kullanımı yaygın olmakla birlikte, yalnızca kullanım varlığı veya sıklığı akademik başarı ve algılanan stresi açıklamak için yeterli görünmemektedir. Gelecek çalışmalarda kullanım amacı, eleştirel değerlendirme ve etkileşim kalitesi dikkate alınmalıdır.

Anahtar Kelimeler

• Yapay zeka
• akademik başarı
• algılanan stres
• sağlık eğitimi

Destekleyen Kurum

Bu çalışma Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmiştir [proje no: 1919B012467486]. Finansör kuruluş; çalışmanın tasarımı, veri toplama, veri analizi, sonuçların yorumlanması, makalenin hazırlanması veya makalenin yayınlanmak üzere gönderilmesi aşamalarında hiçbir rol oynamamıştır.

Proje Numarası

1919B012467486

Etik Beyan

Bu çalışma Selçuk Üniversitesi Etik Kurulu tarafından onaylanmıştır (tarih: 28.05.2025, sayı: 2025/789).

Teşekkür

Yazarlar, çalışmaya katılan Selçuk Üniversitesi Sağlık Hizmetleri Meslek Yüksekokulu öğrencilerine teşekkür eder.

Generative Artificial Intelligence Chatbot Use, Academic Performance, and Perceived Stress Among Health-Related Associate-Degree Students

Öz

OBJECTIVE: This study aimed to examine whether generative artificial intelligence (GenAI) chatbot use was associated with academic 
performance and perceived stress among students enrolled in health-related associate-degree programmes.
MATERIALS and METHODS: This cross-sectional observational study was conducted at a public health services vocational school in Türkiye between June and July 2025. A total of 877 students who provided electronic informed consent were included. Two temporally aligned exposure-outcome windows were specified: Fall-term GenAI chatbot use with Fall-term grade point average (GPA) category, and past-month GenAI chatbot use with perceived stress measured using the Turkish 14-item Perceived Stress Scale. GPA category was analysed using proportional-odds ordinal logistic regression, and perceived stress was analysed using linear regression with heteroscedasticity-consistent (HC3) standard errors. Models were adjusted for age, sex, and class year.
RESULTS: Fall-term GenAI chatbot use was reported by 72.9% of students, and past-month use was reported by 59.6%. In adjusted analyses, Fall-term GenAI chatbot use was not associated with higher GPA categories [odds ratio=1.07; 95% confidence interval (CI): 0.82-1.40; p=0.615]. Past-month GenAI chatbot use was also not significantly associated with perceived stress total score (β=0.76; 95% CI: -0.27 to 1.79; p=0.150). Use frequency was not associated with GPA category, and no monotonic trend was observed.
CONCLUSION: GenAI chatbot use was common among health-related associate-degree students, but use and frequency indicators alone did not explain academic performance or perceived stress. Future research should assess purpose of use, critical evaluation, and interaction quality in health education contexts.

Anahtar Kelimeler

• Artificial intelligence
• academic performance
• perceived stress
• health education

Destekleyen Kurum

This study was supported by The Scientific and Technological Research Council of Türkiye (TÜBİTAK) [project no: 1919B012467486]. The funder had no role in the study design, data collection, data analysis, interpretation of the results, manuscript preparation, or decision to submit the manuscript for publication.

Proje Numarası

1919B012467486

Etik Beyan

The study was approved by Selçuk University Ethics Committee (date: 28.05.2025, no: 2025/789).

Teşekkür

The authors thank the students of Selçuk University Health Services Vocational School who participated in this study.

Kaynakça

1. Kasneci E, Sessler K, Küchemann S, Bannert M, Dementieva D, Fischer F, et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn Individ Differ. 2023; 103: 102274. doi: 10.1016/j. lindif.2023.102274.
2. Lo CK. What is the impact of ChatGPT on education? A rapid review of the literature. Educ Sci (Basel). 2023; 13(4): 410. doi: 10.3390/educsci13040410.
3. UNESCO. Guidance for generative AI in education and research. Paris: UNESCO Publishing; 2023.
4. Crompton H, Burke D. Artificial intelligence in higher education: the state of the field. Int J Educ Technol High Educ. 2023; 20(1): 22. doi: 10.1186/s41239-023-00392-8.
5. Cotton DRE, Cotton PA, Shipway JR. Chatting and cheating: ensuring academic integrity in the era of ChatGPT. Innov Educ Teach Int. 2024; 61(2): 228-239. doi: 10.1080/14703297.2023.2190148.
6. Sullivan M, Kelly A, McLaughlan P. ChatGPT in higher education: considerations for academic integrity and student learning. J Appl Learn Teach. 2023; 6(1): 31-40. doi: 10.37074/jalt.2023.6.1.17.
7. Tlili A, Shehata B, Adarkwah MA, Bozkurt A, Hickey DT, Huang R, et al. What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn Environ. 2023; 10(1): 15. doi: 10.1186/s40561-023- 00237-x.
8. Clark RE. Reconsidering research on learning from media. Rev Educ Res. 1983; 53(4): 445-459. doi: 10.3102/00346543053004445.

9. Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education: where are the educators? Int J Educ Technol High Educ. 2019; 16(1): 39. doi: 10.1186/s41239-019-0171-0.
10. Marton F, Säljö R. On qualitative differences in learning: I—outcome and process. Br J Educ Psychol. 1976; 46(1): 4-11. doi: 10.1111/j.2044-8279.1976.tb02980.x.
11. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989; 13(3): 319-340. doi: 10.2307/249008.
12. Bandura A. Social foundations of thought and action: a social cognitive theory. Englewood Cliffs (NJ): Prentice-Hall; 1986.
13. Richardson M, Abraham C, Bond R. Psychological correlates of university students’ academic performance: a systematic review and meta-analysis. Psychol Bull. 2012; 138(2): 353-387. doi: 10.1037/a0026838.
14. Lazarus RS, Folkman S. Stress, appraisal, and coping. New York: Springer; 1984.
15. Chan CKY, Hu W. Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. Int J Educ Technol High Educ. 2023; 20(1): 43. doi: 10.1186/s41239-023-00411-8.
16. Lee WWS. Relationships among grit, academic performance, perceived academic failure, and stress in associate degree students. J Adolesc. 2017; 60: 148-152. doi: 10.1016/j.adolescence.2017.08.006.
17. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008; 61(4): 344-349. doi: 10.1016/j.jclinepi.2007.11.008.
18. Turkish Student Selection and Placement Center. 2023-YKS yerleştirme sonuçlarına ilişkin sayısal bilgiler [Internet]. Ankara: ÖSYM; 2023 [cited 2024 Dec]. Available from: https: //www.osym.gov.tr/TR,25736/2023-yks-yerlestirme-sonuclarina-iliskin-sayisal-bilgiler.html
19. Turkish Student Selection and Placement Center. 2024-YKS yerleştirme sonuçlarına ilişkin sayısal bilgiler [Internet]. Ankara: ÖSYM; 2024 [cited 2024 Dec]. Available from: https: //www.osym.gov.tr/TR,29511/2024-yks-yerlestirme-sonuclarina-iliskin-sayisal-bilgiler.html
20. Cochran WG. Sampling Techniques. 3rd ed. New York: John Wiley & Sons; 1977.
21. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983; 24(4): 385-396.
22. Eskin M, Harlak H, Demirkıran F, Dereboy Ç. Algılanan stres ölçeğinin Türkçeye uyarlanması: güvenirlik ve geçerlik analizi. Yeni Symposium. 2013; 51(3): 132-140.
23. Brant R. Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics. 1990; 46(4): 1171-1178.
24. Zeileis A. Econometric computing with HC and HAC covariance matrix estimators. Vienna: Institut für Statistik und Mathematik, WU Vienna University of Economics and Business; 2004. Research Report Series/Department of Statistics and Mathematics; No. 10. doi: 10.57938/56b40e45-d5a1-4507-81cf-934332c61890.
25. Zeileis A, Hothorn T. Diagnostic checking in regression relationships. R News. 2002; 2(3): 7-10.
26. Scheu S, Loeffler SN, Maedche A. Designing a mobile chatbot-based learning journaling system for intrinsic motivation and engagement. Int J Educ Technol High Educ. 2026; 23(1): 15. doi: 10.1186/s41239-026-00589-7.
27. Yilmaz M, Temur HB, Emmungil L, Çelik E, Gauthier A, Cukurova M. Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback. Int J Educ Technol High Educ. 2026; 23(1): 16. doi: 10.1186/s41239-026-00592-y.
28. Güler MA, Dağ S, Kayal S, Can M, Şimşekkaya SN. Physiotherapy students’ acceptance of AI-based chatbots (including ChatGPT) in education: a multi-institutional study from Turkey. BMC Med Educ. 2026; 26(1): 174. doi: 10.1186/s12909-025-08535-3.
29. Monib WK, Qazi A, Mahmud MM. Exploring learners’ experiences and perceptions of ChatGPT as a learning tool in higher education. Educ Inf Technol (Dordr). 2025; 30(1): 917-939. doi: 10.1007/s10639-024-13065-4.
30. Morell-Mengual V, Fernández-García O, Berenguer C, Ortega-Barón J, Gil-Llario MD, Estruch-García V. Characteristics, motivations and attitudes of students using ChatGPT and other language model-based chatbots in higher education. Educ Inf Technol (Dordr). 2025; 30(15): 22257-22274. doi: 10.1007/s10639-025-13650-1.