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Türkiye'deki Beslenme ve Diyetetik Öğrencileri Arasında Yapay Zeka, Sosyal Medya ve Akademik Başarı İlişkisi

Year 2025, Volume: 14 Issue: 3, 867 - 877, 25.09.2025
https://doi.org/10.37989/gumussagbil.1727605

Abstract

Amaç: Bu çalışma, dijital entegrasyonun ve ruh sağlığı sorunlarının belirgin ancak yeterince araştırılmamış olduğu bir öğrenci grubuna odaklanarak bu alandaki boşluğu doldurmayı amaçlamaktadır.
Yöntem: Tanımlayıcı ve kesitsel nitelikteki bu çalışma, Kasım 2024–Ocak 2025 tarihleri arasında Türkiye’deki üniversite öğrencileri üzerinde yürütülmüştür. Çevrim içi anket formunda; demografik bilgiler, Yapay Zekâya Yönelik Genel Tutum Ölçeği (GAAIS), Sosyal Medya Bağımlılığı Ölçeği (SMAS) ile Depresyon, Anksiyete ve Stres Ölçeği Kısa Formu yer almıştır. Ayrıca, katılımcıların boy ve vücut ağırlığı beyana dayalı olarak alınmıştır. Veriler SPSS 24.0 programı ile analiz edilmiştir.
Bulgular: Çalışmaya toplam 353 üniversite öğrencisi (%93,5’i kadın, yaş ortalaması 21,79 ± 2,78 yıl) katılmıştır. Katılımcıların %97,5’i sosyal medya kullanmakta olup, en çok kullanılan platform %97,5 ile Instagram olmuştur. GANO (Genel Akademik Not Ortalaması), GAAIS pozitif puanı ile zayıf düzeyde pozitif yönde ilişkilidir (r: 0,126, p< 0,05); buna karşın SMAS puanı ile zayıf düzeyde negatif ilişki göstermiştir (r: -0,115, p< 0,005). SMAS puanları; stres (r: 0,454, p< 0,001), anksiyete (r: 0,428, p< 0,001) ve depresyon (r: 0,482, p< 0,001) skorları ile orta düzeyde pozitif ilişkilidir. Ayrıca, beden kitle indeksi (BKİ), SMAS (r: -0,166, p< 0,005) ve depresyon (r: -0,134, p< 0,005) skorları ile zayıf düzeyde negatif ilişkili bulunmuştur. Çoklu doğrusal regresyon analizine göre; GAAIS pozitif alt ölçek puanındaki artış (β: 0,006, p: 0,006) ve SMAS puanındaki azalma (β: -0,064, p: 0,043), GANO’da artışla ilişkili bulunmuş ve bu değişkenler toplam varyansın %21’ini açıklamıştır.
Sonuç: Bulgular, dijital teknolojinin benimsenmesinde dengeli ve bilinçli yaklaşımların gerekliliğini ortaya koymaktadır. Konuyla ilgili daha fazla araştırmaya ihtiyaç vardır.

References

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  • 22. Ulvi, O., Karamehic-Muratovic, A., Baghbanzadeh, M., Bashir, A., Smith, J., & Haque, U. (2022). Social media use and mental health: a global analysis. Epidemiologia, 3(1), 11-25.
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  • 30. Yu, H. (2023). Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching. Frontiers in psychology, 14, 1181712.
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Artificial Intelligence, Social Media, and Academic Outcomes Among Nutrition and Dietetics Students in Türkiye

Year 2025, Volume: 14 Issue: 3, 867 - 877, 25.09.2025
https://doi.org/10.37989/gumussagbil.1727605

Abstract

Aim: This study aims to address this gap by focusing on a population where digital integration and mental health challenges are prominent yet underexplored.
Methods: This descriptive and cross-sectional study conducted on University students in Türkiye, between November 2024-January 2025. An online questionnaire including demographic characteristics, General Attitudes towards Artificial Intelligence Scale (GAAIS), Social Media Addiction Scale (SMAS), Depression, Anxiety, and Stress Scale Short Form were administered. Additionally, height and body weight were taken with the declaration of the participants. Data were analyzed using SPSS 24.0.
Results: A total of 353 university students (93.5% female, mean age 21.79 ± 2.78 years) completed the study. 97.5% of them used social media, and the most used social media was Instagram with 97.5%. GPA showed a weak positive correlation with GAAIS positive score (r: 0.126, p< 0.05), whereas it showed a weak negative correlation with SMAS score (r: -0.115, p< 0.005). SMAS scores showed a moderate positive correlation with stress (r: 0.454, p< 0.001), anxiety (r: 0.428, p< 0.001), and depression (r: 0.482, p< 0.001). Furthermore, body mass index showed a negative weak correlation with SMAS scores (r: -0.166, p< 0.005), and depression score (r: -0.134, p< 0.005). According to the multiple linear regression analysis, increased GAAIS positive subscale scores (β: 0.006, p: 0.006) and decreased SMAS scores (β: -0.064, p: 0.043) predicted an increase in GPA, and these results accounted for 21% of the variance.
Conclusion: These findings underline the need for balanced and informed approaches to the adoption of digital technology. Further research on the subject is needed.

Ethical Statement

The ethics committee of Istanbul Gelisim University Ethics Committee with the number: 2024-19, and date: 29.11.2024 approved this study and the principles of the Declaration of Helsinki were followed. Written and verbal informed consent was obtained from all participants.

References

  • 1. Zendle, D., and Bowden-Jones, H. (2019). Is excessive use of social media an addiction? BMJ 365:l2171. doi: 10.1136/bmj.l2171
  • 2. Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir-Kaya, M. (2022). The roles of personality traits, al anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 1-18. https://doi.org/10.1080/10447318.2022.2151730
  • 3. Karim, F., Oyewande, A. A., Abdalla, L. F., Ehsanullah, R. C., & Khan, S. (2020). Social media use and its connection to mental health: a systematic review. Cureus, 12(6). doi: 10.7759/cureus.8627
  • 4. Brailovskaia, J., Schillack, H., & Margraf, J. (2020). Tell me why are you using social media (SM)! Relationship between reasons for use of SM, SM flow, daily stress, depression, anxiety, and addictive SM use–An exploratory investigation of young adults in Germany. Computers in human behavior, 113, 106511. https://doi.org/10.1016/j.chb.2020.106511
  • 5. Hussain, Z., & Griffiths, M. D. (2021). The associations between problematic social networking site use and sleep quality, attention-deficit hyperactivity disorder, depression, anxiety and stress. International Journal of Mental Health and Addiction, 19(3), 686-700. Doi:10.1007/s11469-019-00175-1 6. Dontre, A. J. (2021). The influence of technology on academic distraction: A review. Human Behavior and Emerging Technologies, 3(3), 379-390. Doi: 10.1002/hbe2.229
  • 7. Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014
  • 8. Çömlekçi, M. ve Başol, O. (2019). Gençlerin sosyal medya kullanım amaçları ile sosyal medya bağımlılığı ilişkisinin incelenmesi. Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 17(4), 173-188. doi: 10.18026/cbayarsos.525652
  • 9. Lovibond SH, Lovibond PF. Manual for the Depression Anxiety Stress Scales. Psychology Foundation of Australia, Sydney. 1995.
  • 10. Sarıçam H. The psychometric properties of Turkish version of Depression Anxiety Stress Scale-21 (DASS-21) in health control and clinical samples. JCBPR. 2018; 7(1): 19-30. doi: 10.5455/JCBPR.274847
  • 11. Al-Menayes, J. J. (2015). Dimensions of social media addiction among university students in Kuwait. Psychology and Behavioral Sciences, 4(1), 23-28. Doi: 10.11648/j.pbs.20150401.14
  • 12. Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in human behavior, 26(6), 1237-1245. https://doi.org/10.1016/j.chb.2010.03.024
  • 13. Cassidy, J. (2006). Me media: How hanging out on the Internet became big business. The New Yorker, 82(13), 50.
  • 14. Kitsantas, A., Dabbagh, N., Chirinos, D. S., & Fake, H. (2016). College students’ perceptions of positive and negative effects of social networking. Social networking and education: Global perspectives, 225-238.
  • 15. Tang, J. K., Yau, H.-N., Wong, S.-F., & Wong, S.-K. (2015). The impacts on learning via social media: A study on post-secondary students in Hong Kong. Paper presented at the Technology in Education. Technology-Mediated Proactive Learning: Second International Conference, ICTE 2015, Hong Kong, China, July 2-4, 2015, Revised Selected Papers 2.
  • 16. Azizi, S. M., Soroush, A., & Khatony, A. (2019). The relationship between social networking addiction and academic performance in Iranian students of medical sciences: a cross-sectional study. BMC psychology, 7, 1-8.
  • 17. Berryman, C., Ferguson, C. J., & Negy, C. (2018). Social media use and mental health among young adults. Psychiatric quarterly, 89, 307-314
  • 18. Hill, K., Xie, J., Gallo, K., Wood, S., Parlow, M., Hynes, J., & Stewart, S. (2024). The role of a major social media platform on students’ academic performance: Perception versus reality. European Journal of Interactive Multimedia and Education, 5(1), e02401.
  • 19. Blanco, C., Chou, S.P., Saha, T.D., et al. (2011). Temporal Relationships Between Overweight and Obesity and DSM-IV Substance Use, Mood, and Anxiety Disorders: Results From a Prospective Study, the National Epidemiologic Survey on Alcohol and Related Conditions. The Journal of Clinical Psychiatry, 72(11), 20786. Doi: 10.4088/JCP.10m06077gry.
  • 20. Sadagheyani, H. E., & Tatari, F. (2021). Investigating the role of social media on mental health. Mental health and social inclusion, 25(1), 41-51.
  • 21. Scott, H., & Woods, H. C. (2019). Understanding links between social media use, sleep and mental health: recent progress and current challenges. Current sleep medicine reports, 5, 141-149.
  • 22. Ulvi, O., Karamehic-Muratovic, A., Baghbanzadeh, M., Bashir, A., Smith, J., & Haque, U. (2022). Social media use and mental health: a global analysis. Epidemiologia, 3(1), 11-25.
  • 23. Park, S., Lee, S. W., Kwak, J., Cha, M., & Jeong, B. (2013). Activities on Facebook reveal the depressive state of users. Journal of medical Internet research, 15(10), e217.
  • 24. MacMillan, A. (2017). Why Instagram is the worst social media for mental health. Retrieved July, 16, 2021.
  • 25. Nesi, J., Wolff, J. C., & Hunt, J. (2019). Patterns of Social Media Use Among Adolescents Who Are Psychiatrically Hospitalized. Journal of the American Academy of Child and Adolescent Psychiatry, 58(6), 635-639. e631.
  • 26. Royal Society for Public Health (RSPH), UK. Available online: https://www.rsph.org.uk/static/uploaded/d125b27c-0b62-41c5-a2c0155a8887cd01.pdf (accessed on 13 December 2024).
  • 27. André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., Yang, H. (2018). Consumer choice and autonomy in the age of artificial intelligence and big data. Customer needs and solutions, 5, 28-37.
  • 28. Salas-Pilco, S. Z., & Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: a systematic review. International Journal of Educational Technology in Higher Education, 19(1), 21.
  • 29. Ma, X., & Huo, Y. (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75, 102362.
  • 30. Yu, H. (2023). Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching. Frontiers in psychology, 14, 1181712.
  • 31. McGee, R. W. (2023). Is chat gpt biased against conservatives? an empirical study. An Empirical Study (February 15, 2023).
  • 32. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Hüllermeier, E. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, 102274.
  • 33. Fujii, H., & Managi, S. (2018). Trends and priority shifts in artificial intelligence technology invention: A global patent analysis. Economic Analysis and Policy, 58, 60-69.
  • 34. Hu, B., Mao, Y., & Kim, K. J. (2023). How social anxiety leads to problematic use of conversational AI: the roles of loneliness, rumination, and mind perception. Computers in human behavior, 145, 107760.
  • 35. Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019). Artificial intelligence for mental health and mental illnesses: an overview. Current psychiatry reports, 21, 1-18.
  • 36. Akdeniz, M., & Özdinç, F. (2021). Eğitimde yapay zekâ konusunda Türkiye adresli çalışmaların incelenmesi. Van Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 18(1), 912-932.
  • 37. Ballı, M., Doğan, A. E., & Eser, H. Y. (2024). Psikiyatri Hizmetlerinin Yapay Zekâ ile Geliştirilmesi: Fırsatlar ve Zorluklar. Turkish Journal of Psychiatry, 35(4).
There are 36 citations in total.

Details

Primary Language English
Subjects Nutrition and Dietetics (Other)
Journal Section Original Article
Authors

Berna Madalı 0000-0002-3917-5874

Sedat Arslan 0000-0002-3356-7332

Hatice Merve Bayram 0000-0002-7073-2907

Early Pub Date September 25, 2025
Publication Date September 25, 2025
Submission Date June 26, 2025
Acceptance Date September 23, 2025
Published in Issue Year 2025 Volume: 14 Issue: 3

Cite

APA Madalı, B., Arslan, S., & Bayram, H. M. (2025). Artificial Intelligence, Social Media, and Academic Outcomes Among Nutrition and Dietetics Students in Türkiye. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, 14(3), 867-877. https://doi.org/10.37989/gumussagbil.1727605
AMA Madalı B, Arslan S, Bayram HM. Artificial Intelligence, Social Media, and Academic Outcomes Among Nutrition and Dietetics Students in Türkiye. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi. September 2025;14(3):867-877. doi:10.37989/gumussagbil.1727605
Chicago Madalı, Berna, Sedat Arslan, and Hatice Merve Bayram. “Artificial Intelligence, Social Media, and Academic Outcomes Among Nutrition and Dietetics Students in Türkiye”. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 14, no. 3 (September 2025): 867-77. https://doi.org/10.37989/gumussagbil.1727605.
EndNote Madalı B, Arslan S, Bayram HM (September 1, 2025) Artificial Intelligence, Social Media, and Academic Outcomes Among Nutrition and Dietetics Students in Türkiye. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 14 3 867–877.
IEEE B. Madalı, S. Arslan, and H. M. Bayram, “Artificial Intelligence, Social Media, and Academic Outcomes Among Nutrition and Dietetics Students in Türkiye”, Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, vol. 14, no. 3, pp. 867–877, 2025, doi: 10.37989/gumussagbil.1727605.
ISNAD Madalı, Berna et al. “Artificial Intelligence, Social Media, and Academic Outcomes Among Nutrition and Dietetics Students in Türkiye”. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 14/3 (September2025), 867-877. https://doi.org/10.37989/gumussagbil.1727605.
JAMA Madalı B, Arslan S, Bayram HM. Artificial Intelligence, Social Media, and Academic Outcomes Among Nutrition and Dietetics Students in Türkiye. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi. 2025;14:867–877.
MLA Madalı, Berna et al. “Artificial Intelligence, Social Media, and Academic Outcomes Among Nutrition and Dietetics Students in Türkiye”. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, vol. 14, no. 3, 2025, pp. 867-7, doi:10.37989/gumussagbil.1727605.
Vancouver Madalı B, Arslan S, Bayram HM. Artificial Intelligence, Social Media, and Academic Outcomes Among Nutrition and Dietetics Students in Türkiye. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi. 2025;14(3):867-7.