Yükseköğretimde Yapay Zekaya Yönelik Tutumlar: Bir Karma Yöntem Çalışması

Yıl 2025, Cilt: 8 Sayı: 4, 517 - 534, 24.12.2025

Burak Ayçiçek

Öz

Bu çalışma, öğretmen adaylarının yapay zekaya yönelik tutumlarını ve yapay zekaya ilişkin metaforik algılarını belirlemeyi amaçlamaktadır. Çalışmanın örneklemi, Türkiye'deki bir devlet üniversitesinin eğitim fakültesinde çeşitli bölümlerde öğrenim gören 721 öğretmen adayından oluşmaktadır. Bu çalışmada yakınsayan paralel desen kullanılmıştır. Çalışmanın nicel kısmı için betimsel tarama modeli kullanılmıştır. Nicel bulgular, katılımcıların genel olarak yapay zekâya karşı orta düzeyde olumlu bir tutuma sahip olduklarını ve tutum puanlarının cinsiyet, sınıf düzeyi, yapay zekâ ile ilgili ders alma durumu ve yapay zekâ ile ilgili eğitim programlarına katılım değişkenlerine göre anlamlı farklılık gösterdiğini ortaya koymuştur. Çalışmanın nitel kısmında ise katılımcıların metaforik algılarının incelendiği fenomenoloji deseni kullanılmıştır. Yapay zekâ kavramına ilişkin toplam 176 farklı metaforun üretildiği çalışmada, katılımcıların en sık kullandıkları metaforlar “asistan”, “insan”, “sihirli değnek”, “beyin”, “arkadaş”, “ansiklopedi”, “ilaç”, “kütüphane” ve “öğretmen” olmuştur. Elde edilen metaforlar, “Destekleyici Rol/Rehberlik”, “Bilgi/Hafıza”, “Sihir/Gizem”, “İnsan Doğası”, “Sosyal Rol/Kimlik”, “İşlev/Mekanizma”, “Tehdit/Zararlı Potansiyel” ve “Gelişim/Öğrenme” başta olmak üzere 19 farklı kategoriye ayrılmıştır. Araştırma sonuçları doğrultusunda, çağdaş eğitim gereklilikleriyle uyum sağlanabilmesi için yapay zekânın tüm eğitim kademelerinin öğretim programlarına dahil edilmesi önerilmektedir.

Anahtar Kelimeler

Yapay zeka , Tutum , Metafor , Öğretmen adayları

Attitudes Toward Artificial Intelligence in Higher Education: A Mixed-Methods Study

Öz

This study aims to determine preservice teachers’ attitudes toward artificial intelligence and their metaphorical perceptions regarding AI. The sample of the study consisted of 721 preservice teachers studying in various departments of the faculty of education at a state university in Turkey. The convergent parallel design was used in this study. A descriptive survey model was employed for the quantitative part of the study. The quantitative findings revealed that participants generally had a moderately positive attitude toward artificial intelligence, with significant differences according to gender, grade level, taking an artificial intelligence-related course and participation in artificial intelligence-related training programs. In the qualitative part of the study, a phenomenological design was employed to examine the participants’ metaphorical perceptions. The participants produced 176 different metaphors related to the concept of artificial intelligence, with the most frequent ones being “assistant”, “human”, “magic wand”, “brain”, “friend”, “encyclopedia”, “medicine”, “library”, and “teacher”. These metaphors were categorized into 19 different categories with the prominent ones being “Supportive Role/Guidance”, “Knowledge/Memory”, “Magic/Mystery”, “Human Nature”, “Social Role/Identity”, “Function/Mechanism”, “Threat/Harmful Potential”, and “Development/Learning”. Based on the findings, integrating artificial intelligence into the curriculum at all educational levels are recommended to keep pace with contemporary educational demands.

Anahtar Kelimeler

Artificial intelligence , Attitude , Metaphor , Preservice teachers

Etik Beyan

Ethical approval for this study was obtained from the Tokat Gaziosmanpaşa University Social and Human Sciences Research Ethics Committee, with the decision dated 30/06/2025 and numbered 10.64.

Kaynakça

• Abdaljaleel, M., Barakat, M., Alsanafi, M. et al. (2024). A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Scientific Reports, 14. https://doi.org/10.1038/s41598-024-52549-8 Ajzen, I. (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
• Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics, 2, 431-440. https://doi.org/10.1007/s43681-021-00096-7
• Allam, H., Dempere, J., Akre, V., & Flores, P. (2023). Artificial intelligence in education (AIED): Implications and challenges. Proceedings of the HCT international general education conference (HCT-IGEC 2023) (pp. 126-140). Atlantis Press. https://doi.org/10.2991/978-94-6463-286-6_1
• Almaraz-López, C., Almaraz-Menéndez, F., & López-Esteban, C. (2023). Comparative study of the attitudes and perceptions of university students in business administration and management and in education toward artificial intelligence. Education Sciences, 13(6), 609. https://doi.org/10.3390/educsci13060609
• Al-Mashaqba, S. (2020). The impact of artificial intelligence on the outcomes of students’ learning in the Jordanian Universities, The Journal of Education and Practice, 11(1), 20-29.
• An, X., Chai, C.S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2023). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies, 28(5), 5187-5208. https://doi.org/10.1007/s10639-022-11286-z
• Aruğaslan, E. (2025). A qualitative study on doctoral students’ use of artificial intelligence. Journal of University Research, 8(1), 36-53. https://doi.org/10.32329/uad.1557111
• 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
• Banerjee, S., & Banerjee, B. (2023). College teachers’ anxiety towards artificial intelligence: A comparative study. Research Review International Journal of Multidisciplinary, 8(5), 36-43. https://doi.org/10.31305/rrijm.2023.v08.n05.005
• Bang, E., & Luft, J. A. (2013). Secondary science teachers’ use of technology in the classroom during their first 5 years. Journal of Digital Learning in Teacher Education, 29(4), 118-126. https://doi.org/10.1080/21532974.2013.10784715
• Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17(42), 1-12. https://doi.org/10.1186/s41239-020-00218-x
• Bergdahl, J., Latikka, R., Celuch, M., Savolainen, I., Mantere, E. S., Savela, N., & Oksanen, A. (2023). Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics and Informatics, 82, 102013. https://doi.org/10.1016/j.tele.2023.102013
• Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., et al. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education. 21(4). https://doi.org/10.1186/s41239-023-00436-z
• Breazeal, C. (2002). Designing sociable robots. The MIT Press. https://doi.org/10.7551/mitpress/2376.001.0001
• Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education: Contributors, collaborations, research topics, challenges, and future directions. Educational Technology and Society, 25(1), 28-47. https://www.jstor.org/stable/48647028
• 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
• Cho, K.A., & Seo, Y.H. (2024). Dual mediating effects of anxiety to use and acceptance attitude of artificial intelligence technology on the relationship between nursing students’ perception of and intention to use them: A descriptive study. BMC Nursing, 23. https://doi.org/10.1186/s12912-024-01887-z
• Chocarro, R., Cortiñas, M., & Marcos-Matás, G. (2021). Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educational Studies, 49(2), 295-313. https://doi.org/10.1080/03055698.2020.1850426
• Choung, H., David, P., & Ross, A. (2022). 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
• Chu, H.-C., Hwang, G.-H., Tu, Y.-F., & Yang, K.-H. (2022). Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most-cited articles. Australasian Journal of Educational Technology, 38(3), 22-42. https://doi.org/10.14742/ajet.7526
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. Coppin, B. (2004). Artificial intelligence illuminated. Jones & Bartlett Publishers.
• Costa, E. B., Fonseca, B., Santana, M. A., Araújo, F. F. d., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247-256. https://doi.org/10.1016/j.chb.2017.01.047
• Clark, D. (2020). Artificial intelligence for learning: How to use AI to support employee development. Kogan Page Publishers.
• Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. (4th ed.). Pearson.
• Creswell, J. W. (2018). Qualitative inquiry & research design: Choosing among five approaches (4th Edition). Sage.
• Cukurova, M., Luckin, R., & Kent, C. (2020). Impact of an artificial intelligence research frame on the perceived credibility of educational research evidence. International Journal of Artificial Intelligence in Education, 30, 205-235. https://doi.org/10.1007/s40593-019-00188-w
• Darayseh, A. A. (2023). Acceptance of artifical intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4, 100132 https://doi.org/10.1016/j.caeai.2023.100132
• Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt Brace Jovanovich College Publishers. Elbanna, S., & Armstrong, L. (2024). Exploring the integration of ChatGPT in education: Adapting for the future. Management & Sustainability: An Arab Review, 3(1), 16-29. https://doi.org/10.1108/MSAR-03-2023-0016
• Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage.
• Fredrickson, B. L. (2001). The role of positive emotions in positive psychology. The broaden-and-build theory of positive emotions. American Psychologist, 56(3), 218-226. https://doi.org/10.1037/0003-066X.56.3.218
• Fullan, M., Azorín, C., Harris, A., & Jones, M. (2023). Artificial intelligence and school leadership: Challenges, opportunities and implications. School Leadership & Management, 44(4), 339-346. https://doi.org/10.1080/13632434.2023.2246856
• Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2021). Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence? Psychology Learning & Teaching, 21(1), 37-56. https://doi.org/10.1177/14757257211037149
• Gao, J., Xu, Z., Chen, S., Bharathi, M. S., & Zhang, Y.-W. (2018). Computational understanding of the growth of 2D materials. Advanced Theory and Simulations, 1(11). https://doi.org/10.1002/adts.201800085
• García, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49(3), 794-808. https://doi.org/10.1016/j.compedu.2005.11.017
• Guo, S., Zheng, Y., & Zhai, X. (2024). Artificial intelligence in education research during 2013-2023: A review based on bibliometric analysis. Education and Information Technologies. 29, 16387-16409. https://doi.org/10.1007/s10639-024-12491-8
• Hajam, K. B., & Gahir, S. (2024). Unveiling the attitudes of university students toward artificial intelligence. Journal of Educational Technology Systems, 52(3), 335-345. https://doi.org/10.1177/00472395231225920 
• Harkut, D. G., & Kasat, K. (2019). Introductory chapter: Artificial intelligence-challenges and applications. In Artificial Intelligence-Scope and Limitations. IntechOpen. https://doi.org/10.5772/intechopen.84624
• Heffernan, N.T., & Heffernan, C.L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24, 470-497. https://doi.org/10.1007/s40593-014-0024-x
• Hemajothi, S., & Kumar Jain, S. (2022). Challenges of e-learning during the pandemic and its implications in education. Technoarete Transactions on Applications of Information and Communication Technology (ICT) in Education, 1(4), 1-6. https://doi.org/10.36647/TTAICTE/01.04.A001
• Hennekeuser, D., Vaziri, D. D., Golchinfar, D., Schreiber, D., & Stevens, G. (2024). Enlarged education - exploring the use of generative AI to support lecturing in higher education. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00424-y
• Holmes, W., Bialik, M., & Fadel, C. (2019) Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign. https://discovery.ucl.ac.uk/id/eprint/10139722
• Karaca, A., & Kılcan, B. (2023). The adventure of artificial intelligence technology in education: Comprehensive scientific mapping analysis. Participatory Educational Research, 10(4), 144-165. https://doi.org/10.17275/per.23.64.10.4
• Karasar, N. (2024). Scientific research method (39th ed.). Nobel Academic Publishing.
• Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2022). The roles of personality traits, AI anxiety, and demographic factors in attitudes towards artificial intelligence. International Journal of Human–Computer Interaction. 40(2), 497-514. https://doi.org/10.1080/10447318.2022.2151730
• Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/j.tele.2022.101925
• Kushmar, L.V., Vornachev, A.O., Korobova, I.O., & Kaida, N.O. (2022). Artificial intelligence in language learning: What are we afraid of. Arab World English Journal (AWEJ) Special Issue on CALL (8). 262-273. https://dx.doi.org/10.24093/awej/call8.18
• Lee, J. H., Shin, D., & Noh, W. (2023). Artificial intelligence-based content generator technology for young English-as-a-Foreign-language learners’ reading enjoyment. RELC Journal, 54(2), 508-516. https://doi.org/10.1177/00336882231165060
• Li, P. ping, & Wang, B. (2023). Artificial intelligence in music education. International Journal of Human–Computer Interaction, 40(16), 4183-4192. https://doi.org/10.1080/10447318.2023.2209984
• Liang, Y., & Lee, S.A. (2017). Fear of autonomous robots and artificial intelligence: Evidence from national representative data with probability sampling. International Journal of Social Robotics 9, 379-384. https://doi.org/10.1007/s12369-017-0401-3
• Luo, Q. Z., & Hsiao-Chin, L. Y. (2023). The influence of AI-powered adaptive learning platforms on student performance in Chinese classrooms. Journal of Education, 6(3), 1-12. https://doi.org/10.53819/81018102t4181
• Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. https://doi.org/10.1016/j.futures.2017.03.006
• Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14, 81-95. https://doi.org/10.1007/s10209-014-0348-1
• McGrath, C., Pargman, T. C., 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
• Metsärinne, M., & Kallio, M. (2016). How are students’ attitudes related to learning outcomes?. International Journal of Technology and Design Education, 26, 353-371. https://doi.org/10.1007/s10798-015-9317-0
• Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Sage.
• Miranda, H. P., & Russell, M. (2012). Understanding factors associated with teacher-directed student use of technology in elementary classrooms: A structural equation modeling approach. British Journal of Educational Technology, 43(4), 652-666. https://doi.org/10.1111/j.1467-8535.2011.01228.x
• Mohamed, A. M. A., & Shaaban, T. S. K. (2021). The effects of educational games on EFL vocabulary learning of early childhood students with learning disabilities: A systematic review and meta-analysis. International Journal of Linguistics Literature and Translation, 4(3), 159-167. https://doi.org/10.32996/ijllt.2021.4.3.18
• Nilsson, N. J. (2014). Principles of artificial intelligence. Morgan Kaufmann.
• Ninaus, M., & Sailer, M. (2022). Closing the loop–The human role in artificial intelligence for education. Frontiers in Psychology, 13, 956798. https://doi.org/10.3389/fpsyg.2022.956798
• Olhede, S.C., & Wolfe, P.J. (2018). The growing ubiquity of algorithms in society: Implications, impacts and innovations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2128), 20170364. https://doi.org/10.1098/rsta.2017.0364
• Özyurt, Ö., Özyurt, H., Baki, A., & Güven, B. (2013). Integration into mathematics classrooms of an adaptive and intelligent individualized e-learning environment: Implementation and evaluation of UZWEBMAT. Computers in Human Behavior, 29(3), 726-738. https://doi.org/10.1016/j.chb.2012.11.013
• Park, J. (2023). A case study on enhancing the expertise of artificial intelligence education for pre-service teachers. Preprints. https://doi.org/10.20944/preprints202305.2006.v1
• Pellas, N. (2023). The influence of sociodemographic factors on students’ attitudes toward AI-generated video content creation. Smart Learning Environments, 10. https://doi.org/10.1186/s40561-023-00276-4
• Pinto dos Santos, D., Giese, D., Brodehl, S. et al. (2019).  Medical students’ attitude towards artificial intelligence: A multicentre survey. European Radiology, 29, 1640-1646. https://doi.org/10.1007/s00330-018-5601-1
• Prahani, B., Rizki, I., Jatmiko, B., Suprapto, N., & Tan, A. (2022). Artificial intelligence in education research during the last ten years: A review and bibliometric study. International Journal of Emerging Technology in Learning, 17(8), 169-188. https://doi.org/10.3991/ijet.v17i08.29833
• Sahin, F., & Sahin, Y.L. (2022). Drivers of technology adoption during the COVID-19 pandemic: The motivational role of psychological needs and emotions for pre-service teachers. Social Psychology of Education, 25, 567-592. https://doi.org/10.1007/s11218-022-09702-w
• Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education Sciences, 12(8), 569. https://doi.org/10.3390/educsci12080569
• Salas-Pilco, S.Z., & Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: A systematic review.  International Journal of Educational Technology in Higher Education, 19, 1-20. https://doi.org/10.1186/s41239-022-00326-w
• Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, (1), 100014. https://doi.org/10.1016/j.chbr.2020.100014
• Schepman, A., & Rodway, P. (2022). The general attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human-Computer Interaction, 39(13), 2724-2741. https://doi.org/10.1080/10447318.2022.2085400
• Sindermann, C., Yang, H., Elhai, J.D., Yang, S., Quan, L., Li, M., & Montag, C. (2022). Acceptance and fear of artificial intelligence: Associations with personality in a German and a Chinese sample. Discover Psychology, 2. https://doi.org/10.1007/s44202-022-00020-y
• Soofi, A. A., & Ahmed, M. U. (2019). A systematic review of domains, techniques, delivery modes and validation methods for intelligent tutoring systems. International Journal of Advanced Computer Science and Applications, 10(3). https://doi.org/10.14569/IJACSA.2019.0100312
• Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed, D., McCarty, C., et al. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127
• Su, J., & Yang, W. (2023). Artificial Intelligence (AI) literacy in early childhood education: an intervention study in Hong Kong. Interactive Learning Environments, 32(9), 5494-5508. https://doi.org/10.1080/10494820.2023.2217864
• Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
• Temur, S. (2025). Artificial intelligence and education: Current applications and future perspectives. Trakya University Journal of Social Sciences, 27(1), 162-178. https://doi.org/10.26468/trakyasobed.1539210
• Toksoy Çağal, M., & Keskin, Y.M. (2023). A qualitative study on risk perception of AI and robot technology. Hacettepe University Journal of Faculty of Letters, 40(2), 577-598. https://doi.org/10.32600/huefd.1176896
• Tomczak, M., & Tomczak, E. (2014). The need to report effect size estimates revisited: An overview of some recommended measures of effect size. Trends in Sport Sciences, 1(21), 19-25.
• Turkaya, A., & Benli Özdemir, E. (2024). The effect of the use of artificial intelligence technologies on the digital literacy levels of associate degree students. Journal of University Research, 7(4), 459-472. https://doi.org/10.32329/uad.1486583
• Vachkova, S.N., Vachkov, I.V., Klimov, I.A., Petryaeva, E.Y., Salakhova, V.B. (2022). Lessons of the pandemic for family and school - the challenges and prospects of network education. Sustainability 14(4), 2087. https://doi.org/10.3390/su14042087
• 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
• Yıldırım, A., & Şimşek, H. (2008). Qualitative research methods in social sciences (7th ed.). Seçkin Publishing.
• Yüzbaşıoğlu, E. (2021). Attitudes and perceptions of dental students towards artificial intelligence. Journal of Dental Education, 85(1), 60-68. https://doi.org/10.1002/jdd.12385
• Zawacki-Richter, O., Marín, V. I., 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, 1-27. https://doi.org/10.1186/s41239-019-0171-0
• Zhang C., & Lu Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23. https://doi.org/10.1016/j.jii.2021.100224
• Zhou, C. (2023). Integration of modern technologies in higher education on the example of artificial intelligence use. Education and Information Technologies, 28, 3893-3910. https://doi.org/10.1007/s10639-022-11309-9