Araştırma Makalesi
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The Impact of Artificial Intelligence on University Teaching Processes: An Analysis Based on Faculty Perspectives

Yıl 2025, Cilt: 33 Sayı: 3, 448 - 468, 25.07.2025
https://doi.org/10.24106/kefdergi.1748350

Öz

Artificial Intelligence (AI) technologies are developing, diversifying and increasing their impact on learning-teaching environments. Higher education, with its various disciplines and their instructional applications, is also significantly influenced by these developments. However, despite all this development, lecturers’ tendency to use AI for educational purposes is transforming depending on their thoughts and intentions on this issue. From this point of view, this study examines the views of lecturers on the possible effects of AI technologies on their teaching-learning processes. The participants were selected voluntarily from among the lecturers working at universities in Turkey with different positions and titles, taking into account the balance of experience, title and gender. The data collection package included an 8-question semi-structured data collection form developed by the researcher. The collected data were analyzed using inductive content analysis, enabling the identification of themes based on common concepts. The themes were evaluated in terms of the opportunities offered by AI in the field of higher education today with their strengths and aspects open to improvement. The findings revealed that faculty members use AI technologies especially in language models, visual content creation tools and automated assessment systems; these technologies offer advantages such as time saving, individualized learning opportunities and content enrichment. However, challenges such as ethical issues and academic integrity risks were also identified. The research provides insights into the future role of AI technologies in higher education and lays the groundwork for recommendations for educational policies.

Kaynakça

  • Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S.S., bin Saleh, K., Alowais, S. A., Alshaya, O. A., Rahman, I., Al Yami, M. S. & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236-1242. https://doi.org/10.1016/j.sapharm.2023.05.016
  • Al Ka’bi, A. (2023). Proposed artificial intelligence algorithm and deep learning techniques for development of higher education. International Journal of Intelligent Networks, 4, 68-73. https://doi.org/10.1016/j.ijin.2023.03.002
  • Buchanan, B. G., & Feigenbaum, E. A. (1978). Dendral and meta-dendral: their applications dimension. Artificial Intelligence, 11(1-2), 5-24. https://doi.org/10.1016/0004-3702(78)90010-3
  • Burmeister, J. K. (2023). Education for a post-work society: AI, the liberal arts and the future of leisure. In D. Araya & P. Marber (Eds.), Augmented education in the global age (pp. 172-187). Routledge.
  • Burrows, S., Gurevych, I., & Stein, B. (2015). The eras and trends of automatic short answer grading. International Journal of Artificial Intelligence in Education, 25(1), 60-117. https://doi.org/10.1007/s40593-014-0026-8
  • Büyüköztürk, Ş., Akgün, Ö. E., Karadeniz, Ş., Demirel, F. ve Kılıç, E. (2013). Bilimsel araştırma yöntemleri. Pegem Akademi.
  • Campbell, M., Hoane Jr., A. J., & Hsu, F. H. (2002). Deep blue. Artificial Intelligence, 134(1-2), 57-83. https://doi.org/10.1016/S0004-3702(01)00129-1
  • Chiu, T.K.F., Xia, Q., Zhou X., Chai, C.S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118
  • Dakakni, D., & Safa, N. (2023). Artificial intelligence in the L2 classroom: implications and challenges on ethics and equity in higher education: a 21st century Pandora’s box. Computers and Education: Artificial Intelligence, 5, 100179. https://doi.org/10.1016/j.caeai.2023.100179
  • Enslin, S., & Kaul, V. (In Press). Past, present, and future: a history lesson in artificial intelligence. Gastrointestinal Endoscopy Clinics of North America, https://doi.org/10.1016/j.giec.2024.09.003
  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37-54. https://doi.org/10.1609/aimag.v17i3.1230
  • Galindo-Domínguez, H., Delgado, N., Campo, L., & Losada, D. (2024). Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research, 126, 102381. https://doi.org/10.1016/j.ijer.2024.102381
  • Grzybowski, A., Pawlikowska–Łagód, K., Lambert, W.C., & Edin, F. (2024). A history of artificial intelligence. Clinics in Dermatology, 42(3), 221-229. https://doi.org/10.1016/j.clindermatol.2023.12.016
  • Jin, Z., Goyal, S. B., & Rajawat, A.S. (2024). The informational role of artificial intelligence in higher education in the new era. Procedia Computer Science 235, 1008-1023. https://doi.org/10.1016/j.procs.2024.04.096
  • Joo, K. H., & Park, N. H. (2024). Teaching and learning model for artificial intelligence education. Procedia Computer Science 239, 226-233. https://doi.org/10.1016/j.procs.2024.06.166
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Proceedings of the 25 th International Conference on Neural Information Processing Systems, 1, 1097-1105. https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
  • Kumar, S., Rao, P., Singhania, S., Verma, S., & Kheterpal, M. (2024). Will artificial intelligence drive the advancements in higher education? a tri-phased exploration. Technological Forecasting & Social Change, 201, 123258. https://doi.org/10.1016/j.techfore.2024.123258
  • Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: a scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444. http://doi.org/10.1038/nature14539
  • Liamputtong, P. (2019). Qualitative Research Methods, 5th edition. Oxford University Press.
  • Liu, Y., Zhang, H., Jiang, M., Chen, J., & Wang, M. (2024). A systematic review of research on emotional artificial intelligence in english language education. System, 126, 103478. https://doi.org/10.1016/j.system.2024.103478
  • Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: a meta-analysis. Journal of Educational Psychology, 106(4), 901-918. https://doi.org/10.1037/a0037123
  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute.
  • McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A Proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI Magazine, 27(4), 12-14. https://doi.org/10.1609/aimag.v27i4.1904
  • McCorduck, P. (2004). Machines who think: a personal inquiry into the history and prospects of artificial intelligence. AK Peters/CRC Press.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115-133. https://doi.org/10.1007/bf02478259
  • McGrath, C., Cerratto Pargman, T., Juth, N., & Palmgren, P. J. (2023). University teachers’ perceptions of responsibility and artificial intelligence in higher education-an experimental philosophical study. Computers and Education: Artificial Intelligence, 4, 100139. https://doi.org/10.1016/j.caeai.2023.100139
  • Mitchell, T. M. (1999). Machine learning and education. Communications of the ACM, 42(11), 30-36. https://doi.org/10.1145/319382.319388
  • Muthukrishnan, N., Maleki, F., Ovens, K., Reinhold, C., Forghani, B., & Forghani, R. (2020). Brief history of artificial intelligence. Neuroimaging Clinics of North America, 30(4), 393-399. https://doi.org/10.1016/j.nic.2020.07.004
  • Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of artificial intelligence-powered digital writing assistant in higher education: randomized controlled trial. Heliyon, 7(5), e07014. https://doi.org/10.1016/j.heliyon.2021.e07014
  • Newell, A., & Simon, H. A. (1956). The logic theory machine: A complex information processing system. The RAND Corporation. https://www.rand.org/pubs/papers/P868.html
  • Newell, A., & Simon, H. A. (1961). GPS, a program that simulates human thought. The RAND Corporation.
  • Nilsson, N. J. (2009). The quest for artificial intelligence-a history of ideas and achievements. Cambridge University Press. https://doi.org/10.1017/CBO9780511819346
  • Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: a systematic review. Computers and Education: Artificial Intelligence, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033
  • Oliveira, A. L., & Figueiredo, M. A. T. (2023). Artificial intelligence: historical context and state of the art. In H. S. Antunes, P. M. Freitas, A. L. Oliveira, C. M. Pereira, E. V. de Sequeira, & L. B. Xavier (Eds.), Multidisciplinary perspectives on artificial intelligence and the law (pp. 3-10), Springer.
  • Russell, S., & Norvig, P. (2021). Artificial intelligence: a modern approach (4th ed.). Pearson.
  • Sargent, T. J. (In Press). Sources of artificial intelligence. Journal of Economic Dynamics & Control, https://doi.org/10.1016/j.jedc.2024.104989.
  • Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N.,
  • Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484-489. https://doi.org/10.1038/nature16961
  • Stahl, B. C., Schroeder, D., & Rodrigues, R. (2022). Ethics of artificial intelligence case studies and options for addressing ethical challenges. Springer.
  • Suriano, R., Plebe, A., Acciai, A., & Fabio, R. A. (2025). Student interaction with ChatGPT can promote complex critical thinking skills. Learning and Instruction, 95, 102011. https://doi.org/10.1016/j.learninstruc.2024.102011
  • Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 2(42), 230-265. https://doi.org/10.1112/plms/s2-42.1.230
  • Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460. https://doi.org/10.1093/mind/LIX.236.433
  • Tzirides, A. O., Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A., You, Y., dos Santos, T. A., Searsmith, D., O’Brien, C., Cope, B., & Kalantzis, M. (2024). Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers and Education Open 6, 100184. https://doi.org/10.1016/j.caeo.2024.100184
  • Weizenbaum, J. (1966). ELIZA-A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45. https://doi.org/10.1145/365153.365168
  • World Intellectual Property Organization (WIPO). (2024). Generative artificial intelligence: patent landscape report. WIPO.
  • 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
  • Yim, I. H. Y. (2024). A critical review of teaching and learning artificial intelligence (AI) literacy: developing an intelligence-based AI literacy framework for primary school education. Computers and Education: Artificial Intelligence, 7, 100319. https://doi.org/10.1016/j.caeai.2024.100319
  • Zhao, C., & Yu, J. T. (2024). Relationship between teacher’s ability model and students’ behavior based on emotion-behavior relevance theory and artificial intelligence technology under the background of curriculum ideological and political education. Learning and Motivation, 88, 102040. https://doi.org/10.1016/j.lmot.2024.102040

Yapay Zekânın Üniversitedeki Öğretim Süreçlerine Etkileri: Öğretim Elemanı Görüşlerine Dayalı Bir İnceleme

Yıl 2025, Cilt: 33 Sayı: 3, 448 - 468, 25.07.2025
https://doi.org/10.24106/kefdergi.1748350

Öz

Yapay zekâ teknolojileri gün geçtikçe gelişmekte, çeşitlenmekte ve öğrenme-öğretme ortamları üzerindeki etkilerini artırmaktadır. Yükseköğretim düzeyi de gerek farklı bilim alanları gerekse bunların öğretimine ilişkin yansımaları ile bu etkiden payını almaktadır. Bununla birlikte tüm bu gelişime karşın, öğretim elemanlarının yapay zekâyı eğitsel amaçlarla kullanım eğilimleri başat biçimde bu konudaki düşünce ve niyetlerine bağlı biçimde dönüşmektedir. Araştırmada bu bakıştan hareketle öğretim elemanlarının yapay zekâ teknolojilerinin kendi öğretme-öğrenme süreçleri üzerine olası etkilerine ilişkin görüşleri incelenmektedir. Katılımcılar Türkiye’deki üniversitelerde farklı kadro ve unvanlarla görev yapmakta olan öğretim elemanları arasından gönüllülük usulü ile ve deneyim, unvan ve cinsiyet dengesi gözetilerek belirlenmiştir. Veri toplama paketinde araştırmacı tarafından geliştirilen 8 soruluk yarı yapılandırılmış bir veri toplama formu yer almaktadır. Elde edilen veriler tümevarımcı içerik analizleri ile çözümlenmiş, böylece ortak kavramlar üzerinden temalara erişilmiştir. Erişilen temalar yapay zekânın günümüzde yükseköğretim alanında sunduğu fırsatlar ekseninde güçlü ve geliştirmeye açık yönleri ile değerlendirilmiştir. Bulgular, öğretim üyelerinin yapay zekâ teknolojilerini özellikle dil modelleri, görsel içerik oluşturma araçları ve otomatik değerlendirme sistemlerinde kullandıklarını; bu teknolojilerin zaman tasarrufu, bireyselleştirilmiş öğrenme fırsatları ve içerik zenginleştirme gibi avantajlar sunduğunu ortaya koymuştur. Ancak, etik sorunlar, akademik dürüstlük riskleri gibi zorluklar da tespit edilmiştir. Araştırma, yapay zekâ teknolojilerinin yükseköğretimde gelecekteki rolüne ilişkin öngörüler sunmakta ve eğitim politikalarına yönelik önerilere zemin hazırlamaktadır.

Kaynakça

  • Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S.S., bin Saleh, K., Alowais, S. A., Alshaya, O. A., Rahman, I., Al Yami, M. S. & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236-1242. https://doi.org/10.1016/j.sapharm.2023.05.016
  • Al Ka’bi, A. (2023). Proposed artificial intelligence algorithm and deep learning techniques for development of higher education. International Journal of Intelligent Networks, 4, 68-73. https://doi.org/10.1016/j.ijin.2023.03.002
  • Buchanan, B. G., & Feigenbaum, E. A. (1978). Dendral and meta-dendral: their applications dimension. Artificial Intelligence, 11(1-2), 5-24. https://doi.org/10.1016/0004-3702(78)90010-3
  • Burmeister, J. K. (2023). Education for a post-work society: AI, the liberal arts and the future of leisure. In D. Araya & P. Marber (Eds.), Augmented education in the global age (pp. 172-187). Routledge.
  • Burrows, S., Gurevych, I., & Stein, B. (2015). The eras and trends of automatic short answer grading. International Journal of Artificial Intelligence in Education, 25(1), 60-117. https://doi.org/10.1007/s40593-014-0026-8
  • Büyüköztürk, Ş., Akgün, Ö. E., Karadeniz, Ş., Demirel, F. ve Kılıç, E. (2013). Bilimsel araştırma yöntemleri. Pegem Akademi.
  • Campbell, M., Hoane Jr., A. J., & Hsu, F. H. (2002). Deep blue. Artificial Intelligence, 134(1-2), 57-83. https://doi.org/10.1016/S0004-3702(01)00129-1
  • Chiu, T.K.F., Xia, Q., Zhou X., Chai, C.S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118
  • Dakakni, D., & Safa, N. (2023). Artificial intelligence in the L2 classroom: implications and challenges on ethics and equity in higher education: a 21st century Pandora’s box. Computers and Education: Artificial Intelligence, 5, 100179. https://doi.org/10.1016/j.caeai.2023.100179
  • Enslin, S., & Kaul, V. (In Press). Past, present, and future: a history lesson in artificial intelligence. Gastrointestinal Endoscopy Clinics of North America, https://doi.org/10.1016/j.giec.2024.09.003
  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37-54. https://doi.org/10.1609/aimag.v17i3.1230
  • Galindo-Domínguez, H., Delgado, N., Campo, L., & Losada, D. (2024). Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research, 126, 102381. https://doi.org/10.1016/j.ijer.2024.102381
  • Grzybowski, A., Pawlikowska–Łagód, K., Lambert, W.C., & Edin, F. (2024). A history of artificial intelligence. Clinics in Dermatology, 42(3), 221-229. https://doi.org/10.1016/j.clindermatol.2023.12.016
  • Jin, Z., Goyal, S. B., & Rajawat, A.S. (2024). The informational role of artificial intelligence in higher education in the new era. Procedia Computer Science 235, 1008-1023. https://doi.org/10.1016/j.procs.2024.04.096
  • Joo, K. H., & Park, N. H. (2024). Teaching and learning model for artificial intelligence education. Procedia Computer Science 239, 226-233. https://doi.org/10.1016/j.procs.2024.06.166
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Proceedings of the 25 th International Conference on Neural Information Processing Systems, 1, 1097-1105. https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
  • Kumar, S., Rao, P., Singhania, S., Verma, S., & Kheterpal, M. (2024). Will artificial intelligence drive the advancements in higher education? a tri-phased exploration. Technological Forecasting & Social Change, 201, 123258. https://doi.org/10.1016/j.techfore.2024.123258
  • Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: a scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444. http://doi.org/10.1038/nature14539
  • Liamputtong, P. (2019). Qualitative Research Methods, 5th edition. Oxford University Press.
  • Liu, Y., Zhang, H., Jiang, M., Chen, J., & Wang, M. (2024). A systematic review of research on emotional artificial intelligence in english language education. System, 126, 103478. https://doi.org/10.1016/j.system.2024.103478
  • Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: a meta-analysis. Journal of Educational Psychology, 106(4), 901-918. https://doi.org/10.1037/a0037123
  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute.
  • McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A Proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI Magazine, 27(4), 12-14. https://doi.org/10.1609/aimag.v27i4.1904
  • McCorduck, P. (2004). Machines who think: a personal inquiry into the history and prospects of artificial intelligence. AK Peters/CRC Press.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115-133. https://doi.org/10.1007/bf02478259
  • McGrath, C., Cerratto Pargman, T., Juth, N., & Palmgren, P. J. (2023). University teachers’ perceptions of responsibility and artificial intelligence in higher education-an experimental philosophical study. Computers and Education: Artificial Intelligence, 4, 100139. https://doi.org/10.1016/j.caeai.2023.100139
  • Mitchell, T. M. (1999). Machine learning and education. Communications of the ACM, 42(11), 30-36. https://doi.org/10.1145/319382.319388
  • Muthukrishnan, N., Maleki, F., Ovens, K., Reinhold, C., Forghani, B., & Forghani, R. (2020). Brief history of artificial intelligence. Neuroimaging Clinics of North America, 30(4), 393-399. https://doi.org/10.1016/j.nic.2020.07.004
  • Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of artificial intelligence-powered digital writing assistant in higher education: randomized controlled trial. Heliyon, 7(5), e07014. https://doi.org/10.1016/j.heliyon.2021.e07014
  • Newell, A., & Simon, H. A. (1956). The logic theory machine: A complex information processing system. The RAND Corporation. https://www.rand.org/pubs/papers/P868.html
  • Newell, A., & Simon, H. A. (1961). GPS, a program that simulates human thought. The RAND Corporation.
  • Nilsson, N. J. (2009). The quest for artificial intelligence-a history of ideas and achievements. Cambridge University Press. https://doi.org/10.1017/CBO9780511819346
  • Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: a systematic review. Computers and Education: Artificial Intelligence, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033
  • Oliveira, A. L., & Figueiredo, M. A. T. (2023). Artificial intelligence: historical context and state of the art. In H. S. Antunes, P. M. Freitas, A. L. Oliveira, C. M. Pereira, E. V. de Sequeira, & L. B. Xavier (Eds.), Multidisciplinary perspectives on artificial intelligence and the law (pp. 3-10), Springer.
  • Russell, S., & Norvig, P. (2021). Artificial intelligence: a modern approach (4th ed.). Pearson.
  • Sargent, T. J. (In Press). Sources of artificial intelligence. Journal of Economic Dynamics & Control, https://doi.org/10.1016/j.jedc.2024.104989.
  • Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N.,
  • Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484-489. https://doi.org/10.1038/nature16961
  • Stahl, B. C., Schroeder, D., & Rodrigues, R. (2022). Ethics of artificial intelligence case studies and options for addressing ethical challenges. Springer.
  • Suriano, R., Plebe, A., Acciai, A., & Fabio, R. A. (2025). Student interaction with ChatGPT can promote complex critical thinking skills. Learning and Instruction, 95, 102011. https://doi.org/10.1016/j.learninstruc.2024.102011
  • Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 2(42), 230-265. https://doi.org/10.1112/plms/s2-42.1.230
  • Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460. https://doi.org/10.1093/mind/LIX.236.433
  • Tzirides, A. O., Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A., You, Y., dos Santos, T. A., Searsmith, D., O’Brien, C., Cope, B., & Kalantzis, M. (2024). Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers and Education Open 6, 100184. https://doi.org/10.1016/j.caeo.2024.100184
  • Weizenbaum, J. (1966). ELIZA-A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45. https://doi.org/10.1145/365153.365168
  • World Intellectual Property Organization (WIPO). (2024). Generative artificial intelligence: patent landscape report. WIPO.
  • 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
  • Yim, I. H. Y. (2024). A critical review of teaching and learning artificial intelligence (AI) literacy: developing an intelligence-based AI literacy framework for primary school education. Computers and Education: Artificial Intelligence, 7, 100319. https://doi.org/10.1016/j.caeai.2024.100319
  • Zhao, C., & Yu, J. T. (2024). Relationship between teacher’s ability model and students’ behavior based on emotion-behavior relevance theory and artificial intelligence technology under the background of curriculum ideological and political education. Learning and Motivation, 88, 102040. https://doi.org/10.1016/j.lmot.2024.102040
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri (Diğer)
Bölüm Research Article
Yazarlar

Serap Samsa Yetik 0000-0002-2419-8010

Yayımlanma Tarihi 25 Temmuz 2025
Gönderilme Tarihi 19 Aralık 2024
Kabul Tarihi 25 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 33 Sayı: 3

Kaynak Göster

APA Samsa Yetik, S. (2025). The Impact of Artificial Intelligence on University Teaching Processes: An Analysis Based on Faculty Perspectives. Kastamonu Education Journal, 33(3), 448-468. https://doi.org/10.24106/kefdergi.1748350

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