Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective

Mehmet Fikret Gelibolu

doi:10.24106/kefdergi.1939356

EN TR

Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective

Öz

This study investigates the factors influencing higher education students’ self-reported use of generative AI (Gen-AI) chatbots through an extended Technology Acceptance Model (TAM). The model incorporates trust and individual impact alongside perceived usefulness and perceived ease of use to better explain students’ adoption behavior. Usage is defined as the self-reported frequency of chatbot use rather than post-adoption continuance intention. A quantitative, cross-sectional survey was conducted with 303 higher education students. Data were analyzed using Structural Equation Modeling (SEM) in Smart-PLS after confirming the reliability and validity of the measurement model. It's shown that perceived ease-of-use significantly affects both perceived usefulness and self-reported usage. Perceived usefulness also positively influences usage frequency. Trust shapes students’ perceptions of ease of use and usefulness but does not directly affect usage. Moreover, usage has a strong positive impact on individual outcomes, indicating academic and personal benefits associated with frequent use. Ease of use and perceived usefulness are the key drivers of students’ Gen-AI use. Trust influences adoption indirectly by shaping these perceptions. Sustained use of these tools enhances academic and personal outcomes, and the extended TAM proves to be a suitable framework for explaining Gen-AI adoption in higher education contexts.

Anahtar Kelimeler

Generative artificial intelligence (Gen-AI), Technology acceptance model (TAM), Trust, Individual impact, Higher education

Yüksek Öğretim Öğrencilerinin Üretken Yapay Zekâ Sohbet Robotlarını Kullanımının Belirleyicileri: Genişletilmiş Teknoloji Kabul Modeli (TKM) Bakış Açısı

Öz

Bu çalışma, genişletilmiş bir Teknoloji Kabul Modeli (TKM) aracılığıyla, yükseköğretim öğrencilerinin üretken yapay zekâ (Gen-AI) sohbet robotlarını kendi bildirimlerine göre kullanmalarını etkileyen etmenleri araştırmaktadır. Model, öğrencilerin benimseme davranışlarını daha iyi açıklamak için algılanan yarar ve algılanan kullanım kolaylığının yanı sıra güven ve bireysel etkiyi de içermektedir. Kullanım, benimseme sonrası devam etme niyeti yerine, sohbet robotu kullanımının kendi bildirimlerine göre sıklığı olarak tanımlanmıştır. 303 yükseköğretim öğrencisiyle nicel, kesitsel bir anket çalışması yapılmıştır. Veriler, ölçüm modelinin güvenilirliği ve geçerliliği doğrulandıktan sonra Smart-PLS'de Yapısal Eşitlik Modellemesi (YEM) kullanılarak analiz edilmiştir. Algılanan kullanım kolaylığının hem algılanan yararı hem de gerçek kullanımı önemli ölçüde etkilediği gösterilmiştir. Algılanan yarar ayrıca kullanım sıklığını da olumlu yönde etkilemektedir. Güven, öğrencilerin kullanım kolaylığı ve yarar algılarını şekillendirmekte ancak kullanımı doğrudan etkilememektedir. Üstelik kullanımın bireysel sonuçlar üzerinde güçlü bir olumlu etkisi vardır ve sık kullanımla ilişkili akademik ve kişisel yararı ortaya koymaktadır. Kullanım kolaylığı ve algılanan yarar, öğrencilerin üretken yapay zekâyı kullanmalarının temel belirleyicileridir. Güven, bu algıları şekillendirerek dolaylı olarak benimsemeyi etkilemektedir. Bu araçların sürekli kullanımı akademik ve kişisel sonuçları iyileştirmekte ve genişletilmiş TKM, yükseköğretim bağlamlarında üretken yapay zekânın benimsenmesini açıklamak için uygun bir çerçeve olduğunu kanıtlamaktadır.

Anahtar Kelimeler

Üretken yapay zekâ, Teknoloji kabul modeli (TKM), Güven, Bireysel etki, Yüksek öğretim

Kaynakça

Agyare, B., Asare, J., Kraishan, A., Nkrumah, I., & Adjekum, D. K. (2025). A cross-national assessment of artificial intelligence (AI) Chatbot user perceptions in collegiate physics education. Computers and Education: Artificial Intelligence, 8, Article 100365. https://doi.org/10.1016/j.caeai.2025.100365
Al-Kadi, A., & Ali, J. K. M. (2024). A holistic approach to ChatGPT, Gemini, and Copilot in english learning and teaching. Language Teaching Research Quarterly, 43, 155-166. https://doi.org/10.32038/ltrq.2024.43.09
Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon, 10(11). https://doi.org/10.1016/j.heliyon.2024.e31887
Ashraf, H., Qureshi, A., & Mahmood, A. (2025). Effects of ChatGPT on students’ academic performance in Pakistan higher education classrooms. Scientific Reports, 15, Article 16434. https://doi.org/10.1038/s41598-025-92625-1
Bagozzi, R.P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of Academy of Marketing Science, 16, 74–94. https://doi.org/10.1007/BF02723327
Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
Beldad, A. D., & Hegner, S. M. (2018). Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: A structural equation modeling approach. International Journal of Human–Computer Interaction, 34(9), 882–893. https://doi.org/10.1080/10447318.2017.1403220
Boubker, O. (2024). From chatting to self-educating: Can AI tools boost student learning outcomes? Expert Systems with Applications, 238, Article 121820. https://doi.org/10.1016/j.eswa.2023.121820
Butt, S., Mahmood, A., Saleem, S., Rashid, T., & Ikram, A. (2021). Students’ performance in online learning environment: The role of task technology fit and actual usage of system during COVID-19. Frontiers in Psychology, 12, Article 759227. https://doi.org/10.3389/fpsyg.2021.759227
Büyükeke, A. (2025). Student acceptance of generative AI tools in programming courses: Findings from Türkiye through an extended technology acceptance model. Journal of University Research, 8(4), 543-556. https://doi.org/10.32329/uad.1728785

Castillo-Martínez I.M., Flores-Bueno D., Gómez-Puente S.M., & Vite-León V. O. (2024). AI in higher education: a systematic literature review. Frontiers in Education. 9, Article 1391485. https://doi.org/10.3389/feduc.2024.1391485
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8
Chen, X., Jiang, L., Zhou, Z., & Li, D. (2025). Impact of perceived ease of use and perceived usefulness of humanoid robots on students' intention to use. Acta Psychologica, 258, Article 105217. https://doi.org/10.1016/j.actpsy.2025.105217
Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727-1739. https://doi.org/10.1080/10447318.2022.2050543
Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on adoption and use of ChatGPT: A survey analysis. Journal of Medical Internet Research, 25, Article e47184. https://doi.org/10.2196/47184
Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290. https://doi.org/10.1016/j.compedu.2017.12.001
Dahri, N. A., Yahaya, N., & Al-Rahmi, W. M. (2025). Exploring the influence of ChatGPT on student academic success and career readiness. Education and information technologies, 30(7), 8877-8921. https://doi.org/10.1007/s10639-024-13148-2
Dai, Y., Lai, S., Lim, C. P., & Liu, A. (2023). ChatGPT and its impact on research supervision: Insights from Australian postgraduate research students. Australasian Journal of Educational Technology, 39(4), 74-88. https://doi.org/10.14742/ajet.8843
Davis, F. D. (1987). User acceptance of information systems: The technology acceptance model (TAM) (Working paper). University of Michigan, School of Business Administration, Division of Research.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
Gelibolu, M. (2024). Tüketicilerin yapay zekâ temelli sesli asistanları kullanma niyetine etki eden faktörler: Algılanan güvenin aracılık rolü. [Factors affecting consumers’ intention to use artificial intelligence-based voice assistants: mediating role of perceived trust. Tüketici ve Tüketim Araştırmaları Dergisi, 16(2). https://doi.org/10.15659/ttad.16.2.183
Gelibolu, M., & Mouloudj, K. (2025). Motivators and demotivators of consumers’ smart voice assistant usage for online shopping. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), Article 152. https://doi.org/10.3390/jtaer20030152
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660.https://doi.org/10.5465/annals.2018.0057
Gökçearslan, Ş., Yıldız Durak, H., & Esiyok, E. (2023). Emotion regulation, e‐learning readiness, technology usage status, in‐class smartphone cyberloafing, and smartphone addiction in the time of COVID‐19 pandemic. Journal of Computer Assisted Learning, 39(5), 1450-1464. https://doi.org/10.1111/jcal.12785
Göküş, Ş., & Yılmaz, Z. (2025). İlahiyat lisans öğrencilerinin din eğitiminde chatbot kullanmaya yönelik davranışsal niyetlerinin farklı değişkenler açısından incelenmesi [Investigation of theology undergraduate students' behavioral intention to use chatbot in religious education in terms of different variables]. Dinbilimleri Akademik Araştırma Dergisi, 25(2), 1003-1043. https://doi.org/10.33415/daad.1688342
Gupta, S., Arora, A., Singh, S., & Jain, J. (2025). AI feel millennials: Prioritizing the intentions towards adoption of AI-enabled chatbots using fuzzy-AHP approach. Journal of Science and Technology Policy Management. Advance online publication. https://doi.org/10.1108/JSTPM-09-2023-0159
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer. https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling (PLS-SEM). Sage. https://doi.org/10.3926/oss.37
Heilala, V., Araya, R., & Hämäläinen, R. (2025, March). Beyond text-to-text: An overview of multimodal and generative artificial intelligence for education using topic modeling. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (pp. 54-63). https://doi.org/10.1145/3672608.3707764
Henseler, J., Ringle, C.M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modelling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
Holzmann, P., Gregori, P., & Schwarz, E. J. (2025). Students’ little helper: Investigating continuous-use determinants of generative AI and ethical judgment. Education and Information Technologies, 30(17), 24991-25011. https://doi.org/10.1007/s10639-025-13708-0
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53-60. https://academic-publishing.org/index.php/ejbrm/article/view/1224/1187
Iancu, I., & Iancu, B. (2023). Interacting with chatbots later in life: A technology acceptance perspective in COVID-19 pandemic situation. Frontiers in Psychology, 13, Article 1111003. https://doi.org/10.3389/fpsyg.2022.1111003
Ibrahim, A., & Shiring, E. (2022). The relationship between educators' attitudes, perceived usefulness, and perceived ease of use of instructional and web-based technologies: implications from technology acceptance model (TAM). International Journal of Technology in Education, 5(4), 535-551. https://doi.org/10.46328/ijte.285
Iranmanesh, M., Annamalai, N., Kumar, K. M., & Foroughi, B. (2022). Explaining student loyalty towards using WhatsApp in higher education: an extension of the IS success model. The Electronic Library, 40(3), 196-220. https://doi.org/10.1108/EL-08-2021-0161
Karahan Adalı, G. & Bilgili, A. (2025). Generative AI in higher education: Students’ perspectives on adoption, ethical concerns, and academic impact. Acta Infologica, 9(1), 147-166. https://doi.org/10.26650/acin.1670197
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, Article 100328. https://doi.org/10.1016/j.caeai.2024.100328
Kumar, P., Anu, & Parmod. (2025). Exploring the impact of ChatGPT usability on attitudes and beliefs among Indian higher education students: a structural analysis. International Journal of Knowledge and Learning, 18(3), 319-330. https://doi.org/10.1504/IJKL.2025.145989
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human factors, 46(1), 50-80. https://doi.org/10.1518/hfes.46.1.50_3039
Lefever, S., Dal, M., & Matthíasdóttir, Á. (2007). Online data collection in academic research: advantages and limitations. British journal of educational technology, 38(4), 574-582. https://doi.org/10.1111/j.1467-8535.2006.00638.x
Liu, X., Liu, Y., Dai, Y., & Fu, J. (2026). Academic stress and university students’ dependency on generative artificial intelligence: A multiple mediation model using PLS-SEM. BMC Psychology, 14, Article 216. https://doi.org/10.1186/s40359-026-03986-9
Malik, R., Shrama, A., Trivedi, S., & Mishra, R. (2021). Adoption of Chatbots for learning among university students: Role of perceived convenience and enhanced performance. International Journal of Emerging Technologies in Learning, 16(18), 200–212. https://doi.org/10.3991/ijet.v16i18.24315
Memon, M. Q., Lu, Y., Memon, A. R., Memon, A., Munshi, P., & Shah, S. F. A. (2022). Does the impact of technology sustain students’ satisfaction, academic and functional performance: An analysis via interactive and self-regulated learning? Sustainability, 14(12), Article 7226. https://doi.org/10.3390/su14127226
Mirriahi, N., Marrone, R., Barthakur, A., Gabriel, F., Colton, J., Yeung, T. N., ... & Kovanovic, V. (2025). The relationship between students’ self-regulated learning skills and technology acceptance of GenAI. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.10006
Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in classrooms: Five strategies, including prompts. The Wharton School Research Paper. https://dx.doi.org/10.2139/ssrn.4391243
Mustofa, R. H., Kuncoro, T. G., Atmono, D., & Hermawan, H. D. (2025). Extending the technology acceptance model: The role of subjective norms, ethics, and trust in AI tool adoption among students. Computers and Education: Artificial Intelligence, 8, Article 100379. https://doi.org/10.1016/j.caeai.2025.100379
Ofosu-Ampong, K., Acheampong, B., Kevor, M. O., & Amankwah-Sarfo, F. (2023). Acceptance of artificial intelligence (ChatGPT) in education: Trust, innovativeness and psychological need of students. Innovativeness and Psychological Need of Students. https://dx.doi.org/10.2139/ssrn.5255328
Özgül, E. (2026). A systematic review of studies on the use of generative artificial intelligence tools in programming education. Kastamonu Education Journal, 34(1), 172-185. https://doi.org/10.24106/kefdergi.1878122
Parsonage, G., Horton, M., & Read, J. (2023). Trust acceptance mapping-designing intelligent systems for use in an educational context. In International Conference on Human-Computer Interaction (pp. 34-50). Springer. https://doi.org/10.1007/978-3-031-34735-1_3
Pitts, G., & Motamedi, S. (2025). Understanding human-AI trust in education. Telematics and Informatics Reports, 100270. https://doi.org/10.1016/j.teler.2025.100270
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Sakib, M. N., Islam, M., Fahlevi, M., Rahman, M. S., Younus, M., & Rahman, M. M. (2025). Factors influencing users' intention to adopt ChatGPT based on the extended technology acceptance model. Computers in Human Behavior: Artificial Humans, Article 100204. https://doi.org/10.1016/j.chbah.2025.100204
Senali, M. G., Iranmanesh, M., Ghobakhloo, M., Foroughi, B., Asadi, S., & Rejeb, A. (2024). Determinants of trust and purchase intention in social commerce: Perceived price fairness and trust disposition as moderators. Electronic Commerce Research and Applications, 64, Article 101370. https://doi.org/10.1016/j.elerap.2024.101370
Shahzad, M. F., Xu, S., & Asif, M. (2025). Factors affecting generative artificial intelligence, such as ChatGPT, use in higher education: An application of technology acceptance model. British Educational Research Journal, 51(2), 489-513. https://doi.org/10.1002/berj.4084
Sharma, V., Jangir, K., Gupta, M., & Rupeika-Apoga, R. (2024). Does service quality matter in FinTech payment services? An integrated SERVQUAL and TAM approach. International Journal of Information Management Data Insights, 4(2), Article 100252. https://doi.org/10.1016/j.jjimei.2024.100252
Shin, D. (2020). How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance. Computers in Human Behavior, 109, Article 106344. https://doi.org/10.1016/j.chb.2020.106344
Shin, D. (2021). Embodying algorithms, enactive artificial intelligence, and the extended cognition: You can see as much as you know about algorithm. Journal of Information Science, 1–14. https://doi.org/10.1177/0165551520985495
Sio, C. I., Mann, A., Fan, L., Cheung, A., & Lee, L. H. (2025). Perceptions of AI-CBT: trust and barriers in Chinese postgrads. arXiv preprint arXiv:2602.03852. https://doi.org/10.48550/arXiv.2602.03852
Sledgianowski, D., & Kulviwat, S. (2009). Using social networking sites: The effects of playfulness, critical mass and trust in a hedonic context. The Journal of Computer Information Systems, 49(4), 74–83. https://doi.org/10.1080/08874417.2009.11645342
Stöhr, C., Ou, A. W., & Malmström, H. (2024). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7, Article 100259. https://doi.org/10.1016/j.caeai.2024.100259
Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355-366. https://doi.org/10.1177/20965311231168423
Suh, B., & Han, I. (2002). Effect of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications, 1(3–4), 247–263. https://doi.org/10.1016/S1567-4223(02)00017-0
Tian, W., Ge, J., Zhao, Y., & Zheng, X. (2024). AI Chatbots in Chinese higher education: adoption, perception, and influence among graduate students—an integrated analysis utilizing UTAUT and ECM models. Frontiers in Psychology, 15, Article 1268549. https://doi.org/10.3389/fpsyg.2024.1268549
Vartiainen, H., & Tedre, M. (2023). Using artificial intelligence in craft education: crafting with text-to-image generative models. Digital Creativity, 34(1), 1-21. https://doi.org/10.1080/14626268.2023.2174557
Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of operations research, 308(1), 641-652. https://doi.org/10.1007/s10479-020-03918-9
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management Information Systems Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanities and Social Sciences Communications, 12(1), 1-21. https://doi.org/10.1057/s41599-025-04787-y
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167
Wu, K., Zhao, Y., Zhu, Q., Tan, X., & Zheng, H. (2011). A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31(6), 572-581. https://doi.org/10.1016/j.ijinfomgt.2011.03.004
Xue, L., Mahat, J., Ghazali, N., & Shi, X. (2025). Technology acceptance model (TAM) in artificial intelligence in higher education (AIHEd): A systematic literature review. Journal of Institutional Research South East Asia, 23(1).
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., ... & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/10.1111/bjet.13370
Yu, C., Yan, J., & Cai, N. (2024). ChatGPT in higher education: Factors influencing ChatGPT user satisfaction and continued use intention. Frontiers in Education, 9, Article 1354929. https://doi.org/10.3389/feduc.2024.1354929
Zhang, S., Meng, Z., Chen, B., Yang, X., & Zhao, X. (2021). Motivation, social emotion, and the acceptance of artificial intelligence virtual assistants: Trust-based mediating effects. Frontiers in Psychology, 12, Article 728495. https://doi.org/10.3389/fpsyg.2021.728495

Ayrıntılar

Birincil Dil

İngilizce

Konular

Öğretim Teknolojileri

Bölüm

Araştırma Makalesi

Yazarlar

Mehmet Fikret Gelibolu
0000-0002-3780-3005
Türkiye

Yayımlanma Tarihi

30 Nisan 2026

Gönderilme Tarihi

9 Kasım 2025

Kabul Tarihi

26 Mart 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 34 Sayı: 2

DOI

https://doi.org/10.24106/kefdergi.1939356

IZ

https://izlik.org/JA96NA47ED

APA

Gelibolu, M. F. (2026). Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective. Kastamonu Education Journal, 34(2), 290-310. https://doi.org/10.24106/kefdergi.1939356

AMA

1.Gelibolu MF. Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective. Kastamonu Eğitim Dergisi. 2026;34(2):290-310. doi:10.24106/kefdergi.1939356

Chicago

Gelibolu, Mehmet Fikret. 2026. “Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective”. Kastamonu Education Journal 34 (2): 290-310. https://doi.org/10.24106/kefdergi.1939356.

EndNote

Gelibolu MF (01 Nisan 2026) Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective. Kastamonu Education Journal 34 2 290–310.

IEEE

[1]M. F. Gelibolu, “Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective”, Kastamonu Eğitim Dergisi, c. 34, sy 2, ss. 290–310, Nis. 2026, doi: 10.24106/kefdergi.1939356.

ISNAD

Gelibolu, Mehmet Fikret. “Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective”. Kastamonu Education Journal 34/2 (01 Nisan 2026): 290-310. https://doi.org/10.24106/kefdergi.1939356.

JAMA

1.Gelibolu MF. Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective. Kastamonu Eğitim Dergisi. 2026;34:290–310.

MLA

Gelibolu, Mehmet Fikret. “Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective”. Kastamonu Education Journal, c. 34, sy 2, Nisan 2026, ss. 290-1, doi:10.24106/kefdergi.1939356.

Vancouver

1.Mehmet Fikret Gelibolu. Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective. Kastamonu Eğitim Dergisi. 01 Nisan 2026;34(2):290-31. doi:10.24106/kefdergi.1939356

Kastamonu Eğitim Dergisi, Creative Commons Atıf 4.0 Uluslararası Lisansı (CC BY) ile lisanslanmıştır.

Determinants of Higher Education Students’ Use of Generative AI Chatbots: An Extended Technology Acceptance Model (TAM) Perspective

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