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Investigating the Factors Affecting University Students’ Use of ChatGPT

Ahmet Taş(TRABZON ÜNİVERSİTESİ, UYGULAMALI BİLİMLER YÜKSEKOKULU)0000-0002-1444-6068
Sayfa
2252–2269
Yayın Tarihi
2026-09-09
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bulgular, etkileşimli öğrenme, etkileşim kalitesi, bilgi kalitesi, işbirliğine dayalı öğrenme, algılanan kullanım kolaylığı ve algılanan faydanın, öğrencilerin ChatGPT kullanımına anlamlı ve olumlu etkisi olduğunu ortaya koymuştur.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Bu çalışma, üniversite öğrencilerinin ChatGPT kullanım düzeyini etkileyen faktörleri ve bu kullanımın öğrencilerin ChatGPT’yi kullanma süreci üzerindeki etkisini incelemeyi amaçlamaktadır.
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Temel Bulgular & Veri:Veriler Türkiye genelinde 835 üniversite öğrencisinden toplanmış ve yapısal eşitlik modellemesi ile analiz edilmiştir.
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Sonuç & Katkı:Bulgular, etkileşimli öğrenme, etkileşim kalitesi, bilgi kalitesi, işbirliğine dayalı öğrenme, algılanan kullanım kolaylığı ve algılanan faydanın, öğrencilerin ChatGPT kullanımına anlamlı ve olumlu etkisi olduğunu ortaya koymuştur.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:ChatGPT · Yapay zeka · Üniversite öğrencileri

Özet

Bu çalışma, üniversite öğrencilerinin ChatGPT kullanım düzeyini etkileyen faktörleri ve bu kullanımın öğrencilerin ChatGPT’yi kullanma süreci üzerindeki etkisini incelemeyi amaçlamaktadır. Yapay zekâ tabanlı araçların yükseköğretimde giderek daha fazla entegre edilmesiyle birlikte, öğrencilerin bu teknolojilere yönelik algılarının ve deneyimlerinin anlaşılması, öğretim ve öğrenme süreçlerinin yeniden yapılandırılması açısından kritik öneme sahiptir. Çalışma, açıklayıcı ilişkisel model kapsamında nicel bir araştırma deseniyle tasarlanmış ve veri toplama aracı olarak geçerliliği ve güvenilirliği kanıtlanmış ölçekler kullanılmıştır. Veriler Türkiye genelinde 835 üniversite öğrencisinden toplanmış ve yapısal eşitlik modellemesi ile analiz edilmiştir. Bulgular, etkileşimli öğrenme, etkileşim kalitesi, bilgi kalitesi, işbirliğine dayalı öğrenme, algılanan kullanım kolaylığı ve algılanan faydanın, öğrencilerin ChatGPT kullanımına anlamlı ve olumlu etkisi olduğunu ortaya koymuştur. Ayrıca, ChatGPT kullanım düzeyinin, öğrencilerin öğrenme memnuniyetleri üzerinde anlamlı bir etkisi olduğu belirlenmiştir. Bu sonuçlar, ChatGPT’nin yalnızca bilgiye erişimi kolaylaştıran bir araç olmadığını, aynı zamanda etkileşimli ve öğrenci merkezli öğrenme ortamlarını destekleyen bir öğrenme ortağı olarak da işlev gördüğünü göstermektedir. Çalışma, yapay zekâ tabanlı sistemlerin yükseköğretime etkili bir şekilde entegre edilmesinin, öğrencilerin akademik motivasyonlarını ve öğrenme memnuniyetlerini artırabileceği sonucuna ulaşmaktadır. Bu doğrultuda, üniversitelerin dijital dönüşüm stratejilerini pedagojik ilkeleri dikkate alarak yapılandırmaları ve bu süreçleri etik kullanım ilkelerine dayalı olarak yönlendirmeleri önerilmektedir.

Abstract

This study aims to examine the factors influencing university students' level of ChatGPT use and the impact of this use on their using ChatGPT. With the increasing integration of artificial intelligence-based tools in higher education, understanding students' perceptions and experiences with such technologies is critical for restructuring teaching and learning processes. The study was designed using an explanatory correlational model, a quantitative research design, and employed validated and reliable scales as data collection tools. Data were collected from 835 university students across Türkiye and analyzed using structural equation modeling. The findings revealed that interactive learning, interaction quality, information quality, collaborative learning, perceived ease of use, and perceived usefulness all had a significant and positive effect on students’ ChatGPT usage. Furthermore, the level of ChatGPT use was found to have a meaningful impact on students’ learning satisfaction. These results suggest that ChatGPT is not only a tool that facilitates access to information but also acts as a supportive learning partner in fostering interactive and student-centered learning environments. The study concludes that effective integration of AI-based systems in higher education can enhance students' academic motivation and learning satisfaction. Accordingly, it is recommended that universities structure their digital transformation strategies with pedagogical considerations and guide their implementation based on principles of ethical use.

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Bu Makaleye Atıf Yap

Taş, A. (2026). Investigating the Factors Affecting University Students’ Use of ChatGPT. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 9 Sayı: 4, 2252–2269. https://doi.org/10.47495/okufbed.1810517

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Kaynakça (44)

  1. Alamri M.M., Al-Rahmi W.M., Yahaya N., Al-Rahmi A.M., Abualrejal H., Zeki A.M., Al-Maatouk Q. Towards adaptive e-learning among university students: By applying technology acceptance model (TAM). International Journal of Engineering and Advanced Technology 2019; 8(6S3): 270–276. https://doi.org/10.35940/ijeat.F1043.0986S319
  2. Alasmari T., Zhang K. Mobile learning technology acceptance in Saudi Arabian higher education: An extended framework and a mixed-method study. Education and Information Technologies 2019; 24(3): 2127–2144. https://doi.org/10.1007/s10639-019-09865-8
  3. Al-Fraihat D., Joy M., Masa’deh R., Sinclair J. Evaluating e-learning systems success: An empirical study. Computers in Human Behavior 2020; 102: 67–86. https://doi.org/10.1016/j.chb.2019.08.004
  4. Al-Hamad M.Q., Mbaidin H.O., Alhamad A.Q.M., Alshurideh M.T., Al Kurdi B.H., Al-Hamad N.Q. Investigating students’ behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic. International Journal of Data and Network Science 2021; 5(3): 321–330. https://doi.org/10.5267/j.ijdns.2021.6.001
  5. Al-Rahmi A.M., Al-Rahmi W.M., Alturki U., Aldraiweesh A., Almutairy S., Al-Adwan A.S. Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education. Education and Information Technologies 2022; 27(6): 7805–7826. https://doi.org/10.1007/s10639-022-10934-8
  6. Alraimi K.M., Zo H., Ciganek A.P. Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education 2015; 80: 28–38. https://doi.org/10.1016/j.compedu.2014.08.006
  7. Anderson J.C., Gerbing D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin 1988; 103(3): 411–423. https://doi.org/10.1037/0033-2909.103.3.411
  8. Baig M.I., Yadegaridehkordi E. ChatGPT in the higher education: A systematic literature review and research challenges. International Journal of Educational Research 2024; 127: 102411. https://doi.org/10.1016/j.ijer.2024.102411
  9. Bandura A. Social foundations of thought and action: A social cognitive theory. Prentice-Hall 1986.
  10. Bernacki M.L., Greene J.A., Crompton H. Mobile technology, learning, and achievement: Advances in understanding and measuring the role of mobile technology in education. Contemporary Educational Psychology 2020; 60: 101827. https://doi.org/10.1016/j.cedpsych.2019.101827
  11. Çelik K., Ayaz A. Evaluation of metaverse use intention in software education of university students: Combining technology acceptance model with external variables. Educational Technology Research and Development 2025; 73(1): 641–662. https://doi.org/10.1007/s11423-024-10415-4
  12. Chatterjee S., Bhattacharjee K.K. Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies 2020; 25(5): 3443–3463. https://doi.org/10.1007/s10639-020-10159-7
  13. Chatti M.A., Jarke M., Specht M. The 3P learning model. Educational Technology & Society 2010; 13(4): 74–85.
  14. Chen H.R., Tseng H.F. Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan. Evaluation and Program Planning 2012; 35(3): 398–406. https://doi.org/10.1016/j.evalprogplan.2011.11.007
  15. Chen S.C., Liu M.L., Lin C.P. Integrating technology readiness into the expectation–confirmation model: An empirical study of mobile services. Cyberpsychology, Behavior, and Social Networking 2013; 16(8): 604–612. https://doi.org/10.1089/cyber.2012.0606
  16. Cheng Y.M. Effects of quality antecedents on e-learning acceptance. Internet Research 2012; 22(3): 361–390. https://doi.org/10.1108/10662241211235699
  17. Chiu T.K.F., Lin T.J., Lonka K. Motivating online learning: The challenges of COVID-19 and beyond. The Asia-Pacific Education Researcher 2021; 30(3): 187–190. https://doi.org/10.1007/s40299-021-00566-w
  18. Davis F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 1989; 13(3): 319–340. https://doi.org/10.2307/249008
  19. DeLone W.H., McLean E.R. The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems 2003; 19(4): 9–30. https://doi.org/10.1080/07421222.2003.11045748
  20. Dillenbourg P. What do you mean by collaborative learning? In: Dillenbourg P. (Ed.), Collaborative learning: Cognitive and computational approaches. Elsevier 1999; 1–19.
  21. Fiorella L., Mayer R.E. Eight ways to promote generative learning. Educational Psychology Review 2016; 28(4): 717–741. https://doi.org/10.1007/s10648-015-9348-9
  22. Johnson D.W., Johnson R.T. An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher 2009; 38(5): 365–379. https://doi.org/10.3102/0013189X09339057
  23. Kasneci E., Seßler K., Küchemann S., Bannert M., Dementieva D., Fischer F., Gasser U., Groh G., Günnemann S., Hüllermeier E., Krusche S., Kutyniok G., Michaeli T., Nerdel C., Pfeffer J., Poquet O., Sailer M., Schmidt A., Seidel T., Kasneci G. ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences 2023; 103: 102274. https://doi.org/10.1016/j.lindif.2023.102274
  24. Khalifa M., Albadawy M. Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update 2024; 5: 100145. https://doi.org/10.1016/j.cmpbup.2024.100145
  25. King S., Boyer J., Bell T., Estapa A. An automated virtual reality training system for teacher-student interaction: A randomized controlled trial. JMIR Serious Games 2022; 10(4): e41097. https://doi.org/10.2196/41097
  26. Lee O.K.D., Ayyagari R., Nasirian F., Ahmadian M. Role of interaction quality and trust in use of AI-based voice-assistant systems. Journal of Systems and Information Technology 2021; 23(2): 154–170. https://doi.org/10.1108/JSIT-07-2020-0132
  27. Lee Y.W., Strong D.M., Kahn B.K., Wang R.Y. AIMQ: A methodology for information quality assessment. Information & Management 2002; 40(2): 133–146. https://doi.org/10.1016/S0378-7206(02)00043-5
  28. Li K. Determinants of college students’ actual use of AI-based systems: An extension of the technology acceptance model. Sustainability 2023; 15(6): 5221. https://doi.org/10.3390/su15065221
  29. Lin H.F. Measuring online learning systems success: Applying the updated DeLone and McLean model. CyberPsychology & Behavior 2007; 10(6): 817–820. https://doi.org/10.1089/cpb.2007.9948
  30. Lo C.K. What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences 2023; 13(4): 410. https://doi.org/10.3390/educsci13040410
  31. Marangunić N., Granić A. Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society 2015; 14(1): 81–95. https://doi.org/10.1007/s10209-014-0348-1
  32. Padilla-Meléndez A., del Águila-Obra A.R., Garrido-Moreno A. Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education 2013; 63: 306–317. https://doi.org/10.1016/j.compedu.2012.12.014
  33. Parasuraman A., Zeithaml V.A., Malhotra A. E-S-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research 2005; 7(3): 213–233. https://doi.org/10.1177/1094670504271156
  34. Park S.Y. An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Educational Technology & Society 2009; 12(3): 150–162.
  35. Salloum S.A., Al-Emran M., Shaalan K., Tarhini A. Factors affecting the e-learning acceptance: A case study from UAE. Education and Information Technologies 2019; 24(1): 509–530. https://doi.org/10.1007/s10639-018-9786-3
  36. Sun P.C., Tsai R.J., Finger G., Chen Y.Y., Yeh D. What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education 2008; 50(4): 1183–1202. https://doi.org/10.1016/j.compedu.2006.11.007
  37. Susnjak T., McIntosh T.R. ChatGPT: The end of online exam integrity? Education Sciences 2024; 14(6): 656. https://doi.org/10.3390/educsci14060656
  38. Teo T. Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education 2011; 57(4): 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
  39. Venkatesh V., Davis F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 2000; 46(2): 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  40. Vygotsky L.S. Mind in society: The development of higher psychological processes. Cole M., John-Steiner V., Scribner S., Souberman E. (Eds.). Harvard University Press 1978.
  41. Wang R.Y., Strong D.M. Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems 1996; 12(4): 5–33. https://doi.org/10.1080/07421222.1996.11518099
  42. Wixom B.H., Todd P.A. A theoretical integration of user satisfaction and technology acceptance. Information Systems Research 2005; 16(1): 85–102. https://doi.org/10.1287/isre.1050.0042
  43. Zawacki-Richter O., Marín V.I., Bond M., Gouverneur F. Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education 2019; 16: 39. https://doi.org/10.1186/s41239-019-0171-0
  44. Zhou L., Xue S., Li R. Extending the technology acceptance model to explore students’ intention to use an online education platform at a university in China. SAGE Open 2022; 12(1): 21582440221085259. https://doi.org/10.1177/21582440221085259