Araştırma Makalesi
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Yapay Zekâ Kullanım Algısının Bireysel Belirleyicileri: Akademik Motivasyon ve Öz-Yeterliğin Rolü

Yıl 2026, Cilt: 28 Sayı: 1 , 283 - 316 , 20.04.2026
https://doi.org/10.26745/ahbvuibfd.1886236
https://izlik.org/JA83YR52JC

Öz

Çalışmanın amacı, üniversite öğrencilerinin yapay zekâ kullanım algısıyla ilişkili bireysel faktörleri incelemektir. Akademik motivasyonun içsel ve dışsal boyutları ile öz-yeterliğin yapay zekâ kullanım algısıyla olan ilişkileri ampirik olarak analiz edilmektedir. Araştırma, geleceğin iş gücünü oluşturması beklenen üniversite öğrencileri örneklemi üzerinde yürütülmüş ve veriler çevrimiçi anket yöntemiyle 584 katılımcıdan elde edilmiştir. Veriler SPSS ve AMOS yazılımları kullanılarak analiz edilmiştir. Analiz sürecinde tanımlayıcı istatistikler, güvenirlik analizleri, regresyon analizleri ile doğrulayıcı faktör analizi ve ikinci düzey doğrulayıcı faktör analizi uygulanmıştır. Ölçeklerin yapı geçerliliği açımlayıcı ve doğrulayıcı faktör analizleriyle, güvenirliği ise Cronbach alfa katsayılarıyla değerlendirilmiştir. Verilerin normal dağılıma uygunluğu, çarpıklık ve basıklık değerleri üzerinden incelenmiştir. Elde edilen bulgular, içsel motivasyon, dışsal motivasyon ve öz-yeterliğin yapay zekâ kullanım algısıyla pozitif ve istatistiksel olarak anlamlı ilişkiler gösterdiğini ortaya koymaktadır. Regresyon analizleri, bu değişkenlerin birlikte ele alındığında yapay zekâ kullanım algısındaki varyansın anlamlı bir bölümünü açıkladığını göstermektedir. Çalışma, yapay zekâ kullanım algısını bireysel psikolojik kaynaklar üzerinden ele alarak teknoloji kabul literatüründe mikro düzeydeki açıklamalara özgün bir teorik ve yönetsel katkı sunmaktadır.

Etik Beyan

Çalışma için Gazi Üniversitesi Etik Kurulu’ndan 02 sayılı ve 03.02.2026 tarihli etik kurul onayı alınmıştır.

Destekleyen Kurum

Yok

Kaynakça

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Individual Determinants of Artificial Intelligence Usage Perception: The Role of Academic Motivation and Self-Efficacy

Yıl 2026, Cilt: 28 Sayı: 1 , 283 - 316 , 20.04.2026
https://doi.org/10.26745/ahbvuibfd.1886236
https://izlik.org/JA83YR52JC

Öz

This study aims to examine the individual factors associated with university students’ perceptions of artificial intelligence (AI) use. The relationships between intrinsic and extrinsic dimensions of academic motivation, self-efficacy, and AI usage perception are analyzed empirically. The study was conducted on a sample of university students, who are expected to constitute the future workforce, and the data were collected from 584 participants through an online survey. The data were analyzed using SPSS and AMOS software. The analysis process included descriptive statistics, reliability analyses, regression analyses, confirmatory factor analysis (CFA), and second-order confirmatory factor analysis. The construct validity of the scales was assessed through exploratory and confirmatory factor analyses, while reliability was evaluated using Cronbach’s alpha coefficients. The normality of the data was examined based on skewness and kurtosis values. The findings indicate that intrinsic motivation, extrinsic motivation, and self-efficacy are positively and significantly associated with AI usage perception. Regression results further reveal that these variables jointly explain a substantial proportion of the variance in AI usage perception. By addressing AI usage perception through individual psychological resources, this study provides an original theoretical and managerial contribution to the technology acceptance literature by offering a micro-level perspective.

Etik Beyan

Ethical approval for the study was obtained from the Gazi University Ethics Committee, with decision number 02 dated 03.02.2026.

Kaynakça

  • Acosta-Enriquez, B. G., Vargas, C. G. A. P., & Jordan, O. H. (2024). Exploring attitudes toward ChatGPT among college students. Computers & Education: Artificial Intelligence. https://www.sciencedirect.com/science/article/pii/S2666920X24001231
  • Amabile, T. M. (1993). Motivational synergy: Toward new conceptualizations of intrinsic and extrinsic motivation in the workplace. Human Resource Management Review, 3(3), 185–201. https://doi.org/10.1016/1053-4822(93)90012-S
  • Ayanwale, M. A. (2025). Building trust in AI-powered assessment through explainable machine learning models. Proceedings of the ACM Global on Computing. https://doi.org/10.1145/3736251.3747313
  • Aykan, E. B. (2025). Üniversite Öğrencilerinde Akademik Güdülenme, Özyeterlilik ve Hedef Yönelim Arasındaki İlişkinin İncelenmesi. Süleyman Demirel Üniversitesi Sağlık Bilimleri Dergisi, 16(3), 298-307.
  • Aypay, A. (2010). Genel Öz-Yeterlik Ölçeği’nin Türkçeye uyarlama çalışması. İnönü Üniversitesi Eğitim Fakültesi Dergisi, 11(2), 113–131.
  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
  • Bennett, S., Maton, K., & Kervin, L. (2008). The “digital natives” debate: A critical review of the evidence. British Journal of Educational Technology, 39(5), 775–786. https://doi.org/10.1111/j.1467-8535.2007.00793.x
  • Büyükeke, A. (2025). Öğrencilerin programlama derslerinde üretken yapay zekâ araçlarını kabulü: Genişletilmiş teknoloji kabul modeli ile Türkiye’den bulgular. Journal of University Research, 8(4), 543–556.
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  • Hu, Z., He, H., Zhang, C., & Guan, Y. (2025). Development and influencing factors of artificial intelligence literacy and computational thinking in university students. Scientific Reports. https://doi.org/10.1038/s41598-025-26888-z
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  • Liu, H., & Fan, J. (2025). AI mediated communication in EFL classrooms: The role of technical and pedagogical stimuli and the mediating effects of AI literacy and enjoyment. European Journal of Education, 60(1), e12813. https://doi.org/10.1111/ejed.12813
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
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  • Luthans, F., Youssef, C. M., & Avolio, B. J. (2007). Psychological capital: Developing the human competitive edge. Oxford University Press.
  • Lyu, W., & Salam, Z. A. (2025). AI-powered personalized learning: Enhancing self-efficacy, motivation, and digital literacy in adult education through expectancy-value theory. Learning and Motivation, 90, 102129.
  • Mohebbi, A. (2025). Enabling learner independence and self-regulation in language education using AI tools: A systematic review. Cogent Education, 12(1), 2433814. https://doi.org/10.1080/2331186X.2024.2433814
  • Ng, D. T. K., Tay, L. Y., & Lim, C. P. (2023). Developing an artificial intelligence literacy framework for K–16 education. Computers and Education: Artificial Intelligence, 4, 100127.https://doi.org/10.1016/j.caeai.2023.100127
  • Obadă, D. R., Gradinaru, C., & Gradinaru, I. A. (2025). From understanding to influence: The interplay of AI literacy, self-efficacy, and trust in predicting students’ AI adoption. Frontiers in Communication, 10, Article 1722464.https://doi.org/10.3389/fcomm.2025.1722464
  • Özdemir, N. (2015). Bilimsel araştırma yöntemleri: Kavramlar, teknikler ve uygulamalar. Pegem Akademi.
  • Güleryüz, Ö. (2025). RPA sistemleri kullanımında örgütlerin değişebilme yeteneği üzerine nicel bir araştırma. EGE 14. Uluslararası Sosyal Bilimler Kongresi bildiriler kitabı (Cilt 4). İzmir, Türkiye, 23–29 Aralık.
  • Parker, S. K., Bindl, U. K., & Strauss, K. (2010). Making things happen: A model of proactive motivation. Journal of Management, 36(4), 827–856. https://doi.org/10.1177/0149206310363732
  • Pınar, G., ve Bozkurt, Ö. Ç. (2022). Yenilikçi davranış yoluyla akademik başarıyı desteklemede yaratıcı öz-yeterlik ve dijital okuryazarlığın rolü. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, 6(1), 1–31.
  • Pintrich, P. R., & Schunk, D. H. (2002). Motivation in education: Theory, research, and applications (2nd ed.). Merrill Prentice Hall.
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  • Schunk, D. H., Meece, J. L., & Pintrich, P. R. (2008). Motivation in education: Theory, research, and applications. Pearson.
  • Schunk, D. H., Meece, J. L., & Pintrich, P. R. (2014). Motivation in education: Theory, research, and applications (4th ed.). Pearson Education.
  • Schwarzer, R., & Jerusalem, M. (1995). Generalized self-efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35–37). NFER-NELSON.
  • Shahzad, M. F., Xu, S., & Zahid, H. (2025). Exploring the impact of generative AI-based technologies on learning performance. Education and Information Technologies. https://link.springer.com/article/10.1007/s10639-024-12949-9
  • Shatila, K., & Hernández-Lara, A. B. (2025). The role of artificial intelligence in shaping digital literacy: Enhancing higher education through student interaction and pedagogical innovation. In Multidisciplinary movements in AI. Edward Elgar Publishing.
  • Shi, S., & Zhang, H. (2025). EFL students’ motivation predicted by their self-efficacy and resilience in artificial intelligence-based contexts: A self-determination theory perspective. Learning and Motivation, 91, 102151.
  • Shi, Y., Cui, H., Zhang, Y., Hui, X., & Li, G. (2026). The impact of artificial intelligence perception on university students' academic engagement: The mediating role of academic motivation. Frontiers in Psychology. https://pmc.ncbi.nlm.nih.gov/articles/PMC12946146/.
  • Sıvacı, S. Y., ve Çöplü, F. (2020). Üniversite öğrencilerinin akademik öz-yeterlikleri, akademik motivasyonları ve yaşam boyu öğrenme eğilimleri arasındaki ilişki. Kırşehir Eğitim Fakültesi Dergisi, 21(1), 667-700.
  • Soyer, M. (2025). Yapay zekâ teknolojilerine yönelik motivasyonel eğilimler ve psikolojik sermaye ilişkisi: Üniversite öğrencileri üzerine bir araştırma. Hakemli dergi makalesi.
  • Şen, H., ve Aydoğan, E. (2025). Zeka türlerinin iş performansına etkisinde öz-yeterliğin aracı rolü. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 27(2), 713–742. https://doi.org/10.26745/ahbvuibfd.1658187
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  • Tbaishat, D., AlFandi, O., Hamad, F., Bukhari, S. M. S., & Al Muhaissen, S. (2026). Modeling generative AI adoption in higher education: An integrated TAM–TPB–SDT framework with SEM validation. Computers & Education: Artificial Intelligence, 10, 100541. https://doi.org/10.1016/j.caeai.2026.100541
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  • Xu, Q., Liu, Y., & Li, X. (2025). How AI-driven personalized feedback shapes self-efficacy and learning engagement. Learning and Motivation. https://www.sciencedirect.com/science/article/pii/S0023969025000451
  • Yu, T. (2025). Association between generative AI self-efficacy and generative AI acceptance: The mediating role of generative AI trust and the moderating role of generative AI risk perception. Acta Psychologica, 261, 105791. https://doi.org/10.1016/j.actpsy.2025.105791
  • Zhang, Q., Nie, H., Fan, J., & Liu, H. (2025). Exploring the dynamics of artificial intelligence literacy on learners’ willingness to communicate: The mediating roles of AI learning self-efficacy and anxiety. Behavioral Sciences, 15(4), 523. https://doi.org/10.3390/bs15040523
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Toplam 86 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Uluslararası Ticaret (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Nazik Erdal Akyüz 0000-0001-8792-0376

Gönderilme Tarihi 10 Şubat 2026
Kabul Tarihi 8 Nisan 2026
Yayımlanma Tarihi 20 Nisan 2026
DOI https://doi.org/10.26745/ahbvuibfd.1886236
IZ https://izlik.org/JA83YR52JC
Yayımlandığı Sayı Yıl 2026 Cilt: 28 Sayı: 1

Kaynak Göster

APA Erdal Akyüz, N. (2026). Yapay Zekâ Kullanım Algısının Bireysel Belirleyicileri: Akademik Motivasyon ve Öz-Yeterliğin Rolü. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 28(1), 283-316. https://doi.org/10.26745/ahbvuibfd.1886236