Araştırma Makalesi
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Üniversite Öğrencilerinin Yapay Zekaya Yönelik Kaygı ve Tutumlarının İncelenmesi

Yıl 2025, Cilt: 11 Sayı: 2, 346 - 370, 01.08.2025

Öz

Yapay zeka, yükseköğretim de dahil olmak üzere çeşitli sektörleri hızla dönüştürmektedir. Yapay zekanın benimsenmesinin teknik ve pedagojik faydaları geniş çapta tartışılırken, psikolojik ve duygusal etkileri yeterince araştırılmamıştır. Bu araştırma üniversite öğrencilerinin yapay zekâya yönelik tutum ve kaygılarını anlamayı amaçlamaktadır. Araştırmada, nicel araştırma yöntemlerinden, kesitsel bir tasarım modeli kullanılmıştır. Veri toplama araçlarını demografik bilgiler, yapay zeka tutum ölçeği ve yapay zeka kaygı ölçeği oluşturmaktadır. Örneklemi üç yükseköğretim kurumundan 599 üniversite öğrencisi oluşturmaktadır. Verilerin analizinde ortalama, standart sapma, bağımsız örneklem t-testi, ANOVA ve pearson korelasyon analizleri kullanılmıştır. Araştırma sonucunda katılımcıların, yapay zekaya karşı orta ila nötr tutum sergilediği tespit edilmiştir. Yapay zeka ile ilgili kaygılarının genellikle düşük ile orta düzey arasında olduğunu göstermektedir. Bulgular yapay zekanın günlük uygulamaları ve toplumsal etkileri konusunda belirsizlik olduğuna işaret etmektedir. Yapay zeka işlevlerini anlama ve iş değiştirme gibi potansiyel toplumsal etkileri konusunda orta düzeyde kaygı tespit edilmiştir. Erkek öğrenciler, kız öğrencilere kıyasla daha olumlu tutum ve daha düşük kaygı bildirmiştir. Kurumlar arasında tutum ve kaygı düzeylerinde de farklılıklar gözlenmiştir. Ayrıca yapay zeka tutum ile yapay zeka kaygısı arasında anlamlı bir negatif korelasyon bulunmuştur. Bu da yapay zekaya ilişkin olumlu tutumu teşvik etmenin kaygıyı azaltmaya yardımcı olabileceğini işaret etmektedir. Gelecekteki araştırmalarda, yapay zekanın tutum ve kaygıları etkileyen ek demografik ve psikolojik faktörlerin incelenmesi önerilir.

Etik Beyan

Bu araştırma Muş Alparslan Üniversitesi Bilimsel Araştırma ve Yayın Etiği Kurulu’nun 10.05.2024 tarihli ve 57 sayılı kararı ile etik yönden uygun bulunmuştur.

Proje Numarası

1919B012335414

Kaynakça

  • Abid, S., Awan, B., Ismail, T., Sarwar, N., Sarwar, G., Tariq, M., … & Khan, A. (2019). Artificial intelligence: Medical student’s attitude in district peshawar Pakistan. Pakistan Journal of Public Health, 9(1), 19-21. https://doi.org/10.32413/pjph.v9i1.295
  • Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay zekâ kaygı (YZK) ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış, 5(2), 1125-1146. https://doi.org/10.29023/alanyaakademik.833668
  • Allen, B., McGough, A. S., & Devlin, M. (2021). Toward a framework for teaching artificial intelligence to a higher education audience. ACM Transactions on Computing Education (TOCE), 22(2), 1-29. https://doi.org/10.1145/3485062
  • Altun, M. (2024). Yapay zekâ üzerine fikri bir analiz. Dicle İlahiyat Dergisi, 26(2), 227-249. https://doi.org/10.58852/dicd.1386730
  • Ateş, A., & Bahşi, N. (2022). The effects of emotional intelligence of the foreign students learning Turkish language on speaking and writing anxiety. Education Quarterly Reviews, 5(4). 160-171. https://doi.org/10.31014/aior.1993.05.04.582
  • Avcı, E. (2024). Yapay zekanın toplumsal karşılığı-karşıtlığı. Yalova Üniversitesi Sosyal Bilimler Dergisi, 14(2), 239-259. https://doi.org/10.17828/yalovasosbil.1419070
  • Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: A meta-analysis. Computers ve Education, 105, 1-13. https://doi.org/10.1016/j.compedu.2016.11.003
  • Creswell, J. W., Clark, V. L. P., Gutmann, M. L., & Hanson, W. E. (2003). Advanced mixed. Handbook of Mixed Methods in Social ve Behavioral Research, 209, 209-240.
  • Çağal, M. (2023). Yapay zekâ ve robot teknolojisine yönelik risk algısı üzerine nitel bir çalışma. Hacettepe Üniversitesi Edebiyat Fakültesi Dergisi, 40(2), 577-598. https://doi.org/10.32600/huefd.1176896
  • Çakır, Z., Ceyhan, M. A., Gönen, M., & Erbaş, Ü. (2023). Yapay zeka teknolojilerindeki gelişmeler ile eğitim ve spor bilimlerinde paradigma değişimi. Dede Korkut Spor Bilimleri Dergisi, 1(2), 56-71.
  • Çivilidağ, A. (2022). Üniversite idari personeli üzeri̇nde iş stresi, sanal kaytarma ve örgütsel adalet. International Journal of Eurasia Social Sciences, 49, 823-846. http://dx.doi.org/10.35826/ijoess.3116
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003. http://dx.doi.org/10.1287/mnsc.35.8.982
  • Deng, H., Jia, W., & Chai, D. (2022). Discussion on innovative methods of higher teacher education and training based on new artificial intelligence. Security and Communication Networks, 3899413, 1-10. https://doi.org/10.1155/2022/3899413
  • Dergunova, Y., Aubakirova, R., Yelmuratova, B., Gulmira, T., Yuzikovna, P., & Antikeyeva, S. (2022). Artificial intelligence awareness levels of students. International Journal of Emerging Technologies in Learning, 17(18), 26-37. https://doi.org/10.3991/ijet.v17i18.32195
  • Fraenkel, J., Wallen, N., & Hyun, H. (2012). How to design and evaluate research in education. McGraw-Hill Education.
  • Gallix, B., & Chong, J. (2019). Artificial intelligence in radiology: Who’s afraid of the big bad wolf?. European Radiology, 29(4), 1637-1639. https://doi.org/10.1007/s00330-018-5995-9
  • Golzar, J., Noor, S., & Tajik, O. (2022). Convenience sampling in descriptive research. International Journal of Education ve Language Studies, 1(2), 72–77.
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range planning, 46(1-2), 1-12.
  • Hinojo-Lucena, F., Díaz, I., Cáceres-Reche, M., & Rodríguez, J. (2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 1-9. https://doi.org/10.3390/educsci9010051
  • Huang, M., Liu, S., Zhang, Y., Cui, K., & Wen, Y. (2022). Basic theory and practice teaching method based on the cerebellar model articulation controller learning algorithm. Wireless Communications and Mobile Computing, 2022(1), 1-11. https://doi.org/10.1155/2022/2396645
  • Jian, H., Shen, G., & Ren, X. (2021). Connotation analysis and paradigm shift of teaching design under artificial intelligence technology. International Journal of Emerging Technologies in Learning, 16(5), 73-86. https://doi.org/10.3991/ijet.v16i05.20287
  • Karasar, N. (2012). Bilimsel araştırma yöntemi. Nobel Yayınları.
  • Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497-514. https://doi.org/10.1080/10447318.2022.2151730
  • Kılıçarslan, S. (2019). Legal status of artificial intelligence and debates on its legal personality. Yıldırım Beyazıt Hukuk Dergisi, (2), 363-389. https://doi.org/10.33432/ybuhukuk.599224
  • Koyuncuoğlu, D. (2023). Yükseköğretimde yapay zekâ tabanlı sürdürülebilirlik yaklaşımı ve karşılaştırmalı bir inceleme. Journal of Academic Value Studies, 9(3), 182-194. http://dx.doi.org/10.29228/javs.72130
  • Kundu, A., & Bej, T. (2025). Transforming EFL teaching with AI: A systematic review of empirical studies. International Journal of Artificial Intelligence in Education, 1-34. https://doi.org/10.1007/s40593-025-00470-0
  • Kurtcu, F. (2024). Yapay zekâ ve tipografi bağlamında değişen süreçler. Rumelide Dil Ve Edebiyat Araştırmaları Dergisi, (40), 517-531. https://doi.org/10.29000/rumelide.1502212
  • Leant, D. B., & Feigenbaum, E. A. (1987). On the threshold of knowledge. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence (pp. 1173-1182).
  • Liu, Z., & Xu, X. (2022). Studying the impact of health education on student knowledge and behavior through big data and cloud computing. Scientific Programming, 2022, 1-11. https://doi.org/10.1155/2022/4160821
  • Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., ... & Baeßler, B. (2019). Medical students' attitude towards artificial intelligence: A multicentre survey. European radiology, 29, 1640-1646. https://doi.org/10.1007/s00330-018-5601-1
  • Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards artificial intelligence scale. Computers in Human Behavior Reports, 1, 100014. 1-13. https://doi.org/10.1016/j.chbr.2020.100014
  • Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., ... & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI- Künstliche Intelligenz, 35(1), 109-118. https://doi.org/10.1007/s13218-020-00689-0
  • Sukamolson, S. (2007). Fundamentals of quantitative research. Language Institute Chulalongkorn University, 1(3), 1-20.
  • Talaat, M. (2021). Activating the use of artificial intelligence techniques in higher education. Journal of the Egyptian Society for Information Systems and Computer Technology, 25(25), 5-12. https://doi.org/10.21608/jstc.2021.191422
  • Tomar, P., & Verma, S. (2021). Impact and role of AI technologies in teaching, learning, and research in higher education. In Impact of AI Technologies on Teaching, Learning, and Research in Higher Education (pp. 190-203). IGI Global. https://doi.org/10.4018/978-1-7998-4763-2.ch012
  • Turan, T., Turan, G., & Küçüksille, E. (2022). Yapay zekâ etiği: Toplum üzerine etkisi. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 13(2), 292-299. https://doi.org/10.29048/makufebed.1058538
  • Uslu, B. (2023). Üniversitelerde yapay zekanın kullanım alanları: Potansiyel yararları ve olası zorluklar. Eğitimde Kuram ve Uygulama, 19(2), 227-239. https://doi.org/10.17244/eku.1355304
  • Wang, Y. Y., & Wang, Y. S. (2019). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 1–16. https://doi.org/10.1080/10494820.2019.1674887
  • Yang, S. J., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008. https://doi.org/10.1016/j.caeai.2021.100008
  • Zawacki‐Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN 3312874.
  • Zhang, X., & Chen, L. (2021). College english smart classroom teaching model based on artificial intelligence technology in mobile information systems. Mobile Information Systems, 2021(1), 1-12. https://doi.org/10.1155/2021/5644604
  • Zhao, W. W., & Yang, Y. (2022). Artificial intelligence meets onto-epistemologies: Distinctive sociomaterial perspectives for organizational research at the intersection of digital business ecosystems and robotics. In Handbook on Digital Business Ecosystems (pp. 424-437). Edward Elgar Publishing.

An Investigation of University Students' Anxiety and Attitudes Towards Artificial Intelligence

Yıl 2025, Cilt: 11 Sayı: 2, 346 - 370, 01.08.2025

Öz

Artificial intelligence is rapidly transforming various sectors, including higher education. While the technical and pedagogical benefits of AI adoption have been widely discussed, its psychological and emotional effects have been under-researched. This study aims to understand university students' attitudes and concerns towards artificial intelligence. In the study, a cross-sectional design model, one of the quantitative research methods, was used. Data collection tools consist of demographic information, artificial intelligence attitude scale, artificial intelligence anxiety scale. The sample consists of 599 university students from three higher education institutions. Mean, standard deviation, independent sample t-test, ANOVA and Pearson correlation analyses were used to analyse the data. As a result of the research, it was determined that the participants exhibited moderate to neutral attitudes towards artificial intelligence. It shows that their concerns about artificial intelligence are generally between low and medium level. The findings indicate that there is uncertainty about the daily applications and societal impacts of AI. Moderate anxiety was observed about understanding AI functions and potential societal impacts such as changing jobs. Male students reported more positive attitudes and lower anxiety compared to female students. Differences in attitudes and anxiety levels were also observed between institutions. In addition, a significant negative correlation was found between AI attitude and AI anxiety. This indicates that promoting a positive attitude towards AI may help reduce anxiety. In future research, it is recommended to examine additional demographic and psychological factors that affect attitudes and concerns about AI.

Etik Beyan

This research has been deemed ethically acceptable by the Scientific Research and Publication Ethics Committee of Muş Alparslan University in its decision dated 10 May 2024 and numbered 57.

Proje Numarası

1919B012335414

Kaynakça

  • Abid, S., Awan, B., Ismail, T., Sarwar, N., Sarwar, G., Tariq, M., … & Khan, A. (2019). Artificial intelligence: Medical student’s attitude in district peshawar Pakistan. Pakistan Journal of Public Health, 9(1), 19-21. https://doi.org/10.32413/pjph.v9i1.295
  • Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay zekâ kaygı (YZK) ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış, 5(2), 1125-1146. https://doi.org/10.29023/alanyaakademik.833668
  • Allen, B., McGough, A. S., & Devlin, M. (2021). Toward a framework for teaching artificial intelligence to a higher education audience. ACM Transactions on Computing Education (TOCE), 22(2), 1-29. https://doi.org/10.1145/3485062
  • Altun, M. (2024). Yapay zekâ üzerine fikri bir analiz. Dicle İlahiyat Dergisi, 26(2), 227-249. https://doi.org/10.58852/dicd.1386730
  • Ateş, A., & Bahşi, N. (2022). The effects of emotional intelligence of the foreign students learning Turkish language on speaking and writing anxiety. Education Quarterly Reviews, 5(4). 160-171. https://doi.org/10.31014/aior.1993.05.04.582
  • Avcı, E. (2024). Yapay zekanın toplumsal karşılığı-karşıtlığı. Yalova Üniversitesi Sosyal Bilimler Dergisi, 14(2), 239-259. https://doi.org/10.17828/yalovasosbil.1419070
  • Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: A meta-analysis. Computers ve Education, 105, 1-13. https://doi.org/10.1016/j.compedu.2016.11.003
  • Creswell, J. W., Clark, V. L. P., Gutmann, M. L., & Hanson, W. E. (2003). Advanced mixed. Handbook of Mixed Methods in Social ve Behavioral Research, 209, 209-240.
  • Çağal, M. (2023). Yapay zekâ ve robot teknolojisine yönelik risk algısı üzerine nitel bir çalışma. Hacettepe Üniversitesi Edebiyat Fakültesi Dergisi, 40(2), 577-598. https://doi.org/10.32600/huefd.1176896
  • Çakır, Z., Ceyhan, M. A., Gönen, M., & Erbaş, Ü. (2023). Yapay zeka teknolojilerindeki gelişmeler ile eğitim ve spor bilimlerinde paradigma değişimi. Dede Korkut Spor Bilimleri Dergisi, 1(2), 56-71.
  • Çivilidağ, A. (2022). Üniversite idari personeli üzeri̇nde iş stresi, sanal kaytarma ve örgütsel adalet. International Journal of Eurasia Social Sciences, 49, 823-846. http://dx.doi.org/10.35826/ijoess.3116
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003. http://dx.doi.org/10.1287/mnsc.35.8.982
  • Deng, H., Jia, W., & Chai, D. (2022). Discussion on innovative methods of higher teacher education and training based on new artificial intelligence. Security and Communication Networks, 3899413, 1-10. https://doi.org/10.1155/2022/3899413
  • Dergunova, Y., Aubakirova, R., Yelmuratova, B., Gulmira, T., Yuzikovna, P., & Antikeyeva, S. (2022). Artificial intelligence awareness levels of students. International Journal of Emerging Technologies in Learning, 17(18), 26-37. https://doi.org/10.3991/ijet.v17i18.32195
  • Fraenkel, J., Wallen, N., & Hyun, H. (2012). How to design and evaluate research in education. McGraw-Hill Education.
  • Gallix, B., & Chong, J. (2019). Artificial intelligence in radiology: Who’s afraid of the big bad wolf?. European Radiology, 29(4), 1637-1639. https://doi.org/10.1007/s00330-018-5995-9
  • Golzar, J., Noor, S., & Tajik, O. (2022). Convenience sampling in descriptive research. International Journal of Education ve Language Studies, 1(2), 72–77.
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range planning, 46(1-2), 1-12.
  • Hinojo-Lucena, F., Díaz, I., Cáceres-Reche, M., & Rodríguez, J. (2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 1-9. https://doi.org/10.3390/educsci9010051
  • Huang, M., Liu, S., Zhang, Y., Cui, K., & Wen, Y. (2022). Basic theory and practice teaching method based on the cerebellar model articulation controller learning algorithm. Wireless Communications and Mobile Computing, 2022(1), 1-11. https://doi.org/10.1155/2022/2396645
  • Jian, H., Shen, G., & Ren, X. (2021). Connotation analysis and paradigm shift of teaching design under artificial intelligence technology. International Journal of Emerging Technologies in Learning, 16(5), 73-86. https://doi.org/10.3991/ijet.v16i05.20287
  • Karasar, N. (2012). Bilimsel araştırma yöntemi. Nobel Yayınları.
  • Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497-514. https://doi.org/10.1080/10447318.2022.2151730
  • Kılıçarslan, S. (2019). Legal status of artificial intelligence and debates on its legal personality. Yıldırım Beyazıt Hukuk Dergisi, (2), 363-389. https://doi.org/10.33432/ybuhukuk.599224
  • Koyuncuoğlu, D. (2023). Yükseköğretimde yapay zekâ tabanlı sürdürülebilirlik yaklaşımı ve karşılaştırmalı bir inceleme. Journal of Academic Value Studies, 9(3), 182-194. http://dx.doi.org/10.29228/javs.72130
  • Kundu, A., & Bej, T. (2025). Transforming EFL teaching with AI: A systematic review of empirical studies. International Journal of Artificial Intelligence in Education, 1-34. https://doi.org/10.1007/s40593-025-00470-0
  • Kurtcu, F. (2024). Yapay zekâ ve tipografi bağlamında değişen süreçler. Rumelide Dil Ve Edebiyat Araştırmaları Dergisi, (40), 517-531. https://doi.org/10.29000/rumelide.1502212
  • Leant, D. B., & Feigenbaum, E. A. (1987). On the threshold of knowledge. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence (pp. 1173-1182).
  • Liu, Z., & Xu, X. (2022). Studying the impact of health education on student knowledge and behavior through big data and cloud computing. Scientific Programming, 2022, 1-11. https://doi.org/10.1155/2022/4160821
  • Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., ... & Baeßler, B. (2019). Medical students' attitude towards artificial intelligence: A multicentre survey. European radiology, 29, 1640-1646. https://doi.org/10.1007/s00330-018-5601-1
  • Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards artificial intelligence scale. Computers in Human Behavior Reports, 1, 100014. 1-13. https://doi.org/10.1016/j.chbr.2020.100014
  • Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., ... & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI- Künstliche Intelligenz, 35(1), 109-118. https://doi.org/10.1007/s13218-020-00689-0
  • Sukamolson, S. (2007). Fundamentals of quantitative research. Language Institute Chulalongkorn University, 1(3), 1-20.
  • Talaat, M. (2021). Activating the use of artificial intelligence techniques in higher education. Journal of the Egyptian Society for Information Systems and Computer Technology, 25(25), 5-12. https://doi.org/10.21608/jstc.2021.191422
  • Tomar, P., & Verma, S. (2021). Impact and role of AI technologies in teaching, learning, and research in higher education. In Impact of AI Technologies on Teaching, Learning, and Research in Higher Education (pp. 190-203). IGI Global. https://doi.org/10.4018/978-1-7998-4763-2.ch012
  • Turan, T., Turan, G., & Küçüksille, E. (2022). Yapay zekâ etiği: Toplum üzerine etkisi. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 13(2), 292-299. https://doi.org/10.29048/makufebed.1058538
  • Uslu, B. (2023). Üniversitelerde yapay zekanın kullanım alanları: Potansiyel yararları ve olası zorluklar. Eğitimde Kuram ve Uygulama, 19(2), 227-239. https://doi.org/10.17244/eku.1355304
  • Wang, Y. Y., & Wang, Y. S. (2019). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 1–16. https://doi.org/10.1080/10494820.2019.1674887
  • Yang, S. J., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008. https://doi.org/10.1016/j.caeai.2021.100008
  • Zawacki‐Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN 3312874.
  • Zhang, X., & Chen, L. (2021). College english smart classroom teaching model based on artificial intelligence technology in mobile information systems. Mobile Information Systems, 2021(1), 1-12. https://doi.org/10.1155/2021/5644604
  • Zhao, W. W., & Yang, Y. (2022). Artificial intelligence meets onto-epistemologies: Distinctive sociomaterial perspectives for organizational research at the intersection of digital business ecosystems and robotics. In Handbook on Digital Business Ecosystems (pp. 424-437). Edward Elgar Publishing.
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Eğitim Teknolojisi ve Bilgi İşlem
Bölüm Teknoloji Eğitimi
Yazarlar

Sevgi Çelik 0009-0007-4824-0766

İsmail Dönmez 0000-0002-7792-0169

Proje Numarası 1919B012335414
Erken Görünüm Tarihi 1 Ağustos 2025
Yayımlanma Tarihi 1 Ağustos 2025
Gönderilme Tarihi 5 Şubat 2025
Kabul Tarihi 30 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 11 Sayı: 2

Kaynak Göster

APA Çelik, S., & Dönmez, İ. (2025). Üniversite Öğrencilerinin Yapay Zekaya Yönelik Kaygı ve Tutumlarının İncelenmesi. Gazi Eğitim Bilimleri Dergisi, 11(2), 346-370.