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The Effect of University Students’ Attitudes toward Artificial Intelligence on Academic Motivation and Procrastination Behavior

Yıl 2025, Cilt: 22 Sayı: 4, 753 - 768, 04.08.2025
https://doi.org/10.26466/opusjsr.1663214

Öz

Nowadays, with the widespread use of artificial intelligence (AI) technologies in educational environments, how students' attitudes toward these technologies affect academic processes has become an important research topic. This study aims to examine the effects of university students' attitudes toward AI on academic motivation and procrastination behaviors. 198 university students participated in the study. The data were collected online via Google Forms using a convenience sampling method from university students who volunteered to participate and using the General Attitude s Artificial Intelligence Scale, Academic Motivation Scale, and Tuckman Procrastination Tendency Scale. Pearson Correlation Analysis, Multivariate Analysis of Variance (MANOVA), and Multiple Linear Regression Analysis were used in the analysis of data. The findings showed that a positive AI attitude increased both intrinsic and extrinsic academic motivation, while a negative AI attitude only increased amotivation. However, no significant relationship was found between attitudes toward AI and procrastination behavior. Regression analyses showed that positive AI attitude had a statistically significant effect on increasing academic motivation, but this effect was limited by low explanation percentages. The findings of the study revealed that positive attitudes toward AI may support academic motivation, but it does not have a direct relationship with procrastination behavior. These results suggest that fostering positive attitudes toward AI may enhance students’ academic motivation, whereas procrastination behavior appears to be influenced by other individual factors beyond AI attitudes. The results emphasize the importance of strategies to improve individuals' attitudes toward AI to better understand the role of AI in academic processes and to encourage its effective use in educational environments.

Kaynakça

  • Abbass, H. (2021). What is artificial intelligence?. IEEE Transactions on Artificial Intelligence, 2(2), 94-95. https://doi.org/10.1109/TAI.
  • Abisoye, A., Udeh, C. A., & Okonkwo, C. A. (2022). The Impact of AI-Powered Learning Tools on STEM Education Outcomes: A Policy Perspective. International Journal of Multidisciplinary Research and Growth Evaluation, 3(1), 121-127. https://doi.org/10.54660/.IJMRGE.2022.-3.1.121-127
  • Abuadas, M., Albikawi, Z., & Rayani, A. (2025). The impact of an AI-focused ethics education program on nursing students’ ethical awareness, moral sensitivity, attitudes, and generative AI adoption intention: a quasi-experimental study. BMC Nursing, 24(1), 720. https://doi.-org/10.1186/s12912-025-03458-2
  • Acosta-Enriquez, B. G., Arbulú Ballesteros, M. A., Huamaní Jordan, O., López Roca, C., & Saavedra Tirado, K. (2024). Analysis of college students' attitudes toward the use of ChatGPT in their academic activities: effect of intent to use, verification of information and responsible use. BMC Psychology, 12(1), 255. https://doi.org/10.1186/s40359-024-01764-z
  • Afzal, H., Ali, I., Aslam Khan, M., & Hamid, K. (2010). A study of university students’ motivation and its relationship with their academic performance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2899435
  • Afzal, S., & Jami, H. (2018). Prevalence of academic procrastination and reasons for academic procrastination in university students. Journal of Behavioural Sciences, 28(1), 51–69.
  • Akinwalere, S. N., & Ivanov, V. (2022). Artificial intelligence in higher education: Challenges and opportunities. Border Crossing, 12(1), 1-15. https://doi.org/10.33182/bc.v12i1.2015
  • Aoun, J. E. (2017). Robot-proof: Higher education in the age of artificial intelligence. MIT Press.
  • Apolzan, I., & Cimpineanu, M. J. (2024). Benefits and challenges of using artificial intelligence in education. Euro-Atlantic Resilience Journal, 2(3), 49-72. https://doi.org/10.54675/-EWZM9535
  • Asiksoy, G. (2024). An investigation of university students' attitudes toward artificial intelligence ethics. International Journal of Engineering Pedagogy, 14(8).
  • Becker, A. (2019). Artificial intelligence in medicine: What is it doing for us today?. Health Policy and Technology, 8(2), 198-205. https://doi.org/10.1016/j.hlpt.2019.03.004
  • Berestova, A., Burdina, G., Lobuteva, L., & Lobuteva, A. (2022). Academic motivation of university students and the factors that influence it in an e-learning environment. Electronic Journal of e-Learning, 20(2), 201–210.
  • Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. S. (2025). AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students' AI self-efficacy. Computers and Education: Artificial Intelligence, 8, 100340. https://doi.org/10.1016-/j.caeai.2024.100340
  • Büyüköztürk, Ş. (2002). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum. Pegem Yayıncılık.
  • Cao, L. (2012). Differences in procrastination and motivation between undergraduate and graduate students. Journal of the Scholarship of Teaching and Learning, 12(2), 39–64.
  • Chan, C. K. Y. (2024). Exploring the Factors of" AI Guilt" Among Students--Are You Guilty of Using AI in Your Homework?. arXiv preprint arXiv:2407.10777.
  • Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002
  • Chun Chu, A. H., & Choi, J. N. (2005). Rethinking procrastination: Positive effects of "active" procrastination behavior on attitudes and performance. The Journal of Social Psychology, 145(3), 245–264. https://doi.org/10.3200/-SOCP.145.3.245-264
  • Cohen, J. (2013). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
  • Corkin, D. M., Shirley, L. Y., & Lindt, S. F. (2011). Comparing active delay and procrastination from a self-regulated learning perspective. Learning and Individual Differences, 21(5), 602–606. https://doi.org/10.1016/j.lindif.2011.07.005
  • Deci, E. L., Ryan, R. M. (1985). Conceptualizations of intrinsic motivation and self-determination. Intrinsic motivation and self-determination in human behavior, 11-40.
  • Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/-10.1207/S15327965PLI1104_01
  • Eltahir, M. E., & Babiker, F. M. E. (2024). The influence of artificial intelligence tools on student performance in e-learning environments: Case study. Electronic Journal of e-Learning, 22(9), 91-110. https://doi.org/10.34190/ejel.22.9.3639
  • Ghasemi, A., & Zahediasl, S. (2012). Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. international journal of endocrinology and metabolism, 10 (2), 486–489. https://doi.org/10.5812/ijem.3505
  • He, S. (2017). A multivariate investigation into academic procrastination of university students. Open Journal of Social Sciences, 5(10), 12–25.
  • Hensley, L. C. (2016). The draws and drawbacks of college students' active procrastination. Journal of College Student Development, 57(4), 465–471. https://doi.org/10.1353/csd.2016.0050
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  • Ibrahim, M. B., Mujahid, T., & Anshori, M. I. (2025). Optimization of artificial intelligence-based research in minimizing student academic procrastination. International Conference on Research Issues and Community Service.
  • Kağan, M. (2009). Determining the variables which explain the behavior of academic procrastination in university students. Ankara University Journal of Faculty of Educational Sciences, 42(2), 113–128.
  • Karagüven Ünal, M. H. (2012). Akademik motivasyon ölçeğinin Türkçeye adaptasyonu. Kuram ve Uygulamada Eğitim Bilimleri, 12(4), 2599–2620.
  • Kausar, F. N., Shakir, F., & Aziz, K. (2024). Effect of artificial intelligence usage on students' motivation and learning at university level. Journal of Social Signs Review, 2(4), 307–323.
  • Kim H. Y. (2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52–54. https://doi.org/-10.5395/rde.2013.38.1.52
  • Lee, E. (2005). The relationship of motivation and flow experience to academic procrastination in university students. The Journal of Genetic Psychology, 166(1), 5–15. https://doi.org/-10.3200/GNTP.166.1.5-15
  • Lu, O. H. T., Huang, A. Y. Q., Huang, J. C. H., Lin, A. J. Q., Ogata, H., & Yang, S. J. H. (2018). Applying Learning Analytics for the Early Prediction of Students’ Academic Performance in Blended Learning. Journal of Educational Technology & Society, 21(2), 220–232. http://www.jstor.org/stable/26388400
  • Ma, D., Akram, H., & Chen, I.-H. (2024). Artificial intelligence in higher education: A cross-cultural examination of students’ behavioral intentions and attitudes. International Review of Research in Open and Distributed Learning, 25(3), 134–157.
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  • Mohamed, A. M., Shaaban, T. S., Bakry, S. H., Guillén-Gámez, F. D., & Strzelecki, A. (2024). Empowering the faculty of education students: Applying AI’s potential for motivating and enhancing learning. Innovative Higher Education, 1-23. https://doi.org/10.1007/s10755-024-09747-z
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Üniversite Öğrencilerinin Yapay Zekâ Tutumlarının Akademik Motivasyon ve Erteleme Davranışı Üzerindeki Etkisi

Yıl 2025, Cilt: 22 Sayı: 4, 753 - 768, 04.08.2025
https://doi.org/10.26466/opusjsr.1663214

Öz

Günümüzde yapay zeka (YZ) teknolojilerinin eğitim ortamlarında yaygınlaşmasıyla birlikte, öğrencilerin bu teknolojilere yönelik tutumlarının akademik süreçleri nasıl etkilediği önemli bir araştırma konusu haline gelmiştir. Bu çalışma, üniversite öğrencilerinin YZ ile ilgili tutumlarının akademik motivasyon ve erteleme davranışları üzerindeki etkisini incelemeyi amaçlamaktadır. Araştırmaya 198 üniversite öğrencisi katılmıştır. Veriler, gönüllü olarak katılmayı kabul eden üniversite öğrencilerinden kolaylık örnekleme yöntemi kullanılarak Google Formlar aracılığıyla çevrimiçi olarak ve Yapay Zekâya Yönelik Genel Tutum Ölçeği, Akademik Motivasyon Ölçeği ve Tuckman Erteleme Eğilimi Ölçeği kullanılarak toplanmıştır. Verilerin analizinde Pearson Korelasyon Analizi, Çok Değişkenli Varyans Analizi (MANOVA) ve Çoklu Doğrusal Regresyon Analizi kullanılmıştır. Bulgular, pozitif YZ tutumunun hem içsel hem de dışsal akademik motivasyonu artırdığını, negatif YZ tutumunun ise yalnızca motivasyonsuzluğu artırdığını göstermiştir. Bununla birlikte, YZ’ye yönelik tutumlar ile erteleme davranışı arasında anlamlı bir ilişki bulunmamıştır. Regresyon analizleri, pozitif YZ tutumunun akademik motivasyonu artırmada istatistiksel olarak anlamlı bir etkisinin olduğunu, ancak bu etkinin düşük açıklama yüzdeleriyle sınırlı kaldığını göstermiştir. Bu sonuçlar, yapay zekaya karşı olumlu tutumların geliştirilmesinin öğrencilerin akademik motivasyonunu artırabileceğini, erteleme davranışının ise yapay zeka tutumlarının ötesinde diğer bireysel faktörlerden etkilendiğini göstermektedir. Sonuçlar, YZ’nin akademik süreçlerdeki rolünü daha iyi anlamak ve eğitim ortamlarında etkin kullanımını teşvik etmek için bireylerin YZ’ye yönelik tutumlarını geliştirmeye yönelik stratejilerin önemini vurgulamaktadır.

Kaynakça

  • Abbass, H. (2021). What is artificial intelligence?. IEEE Transactions on Artificial Intelligence, 2(2), 94-95. https://doi.org/10.1109/TAI.
  • Abisoye, A., Udeh, C. A., & Okonkwo, C. A. (2022). The Impact of AI-Powered Learning Tools on STEM Education Outcomes: A Policy Perspective. International Journal of Multidisciplinary Research and Growth Evaluation, 3(1), 121-127. https://doi.org/10.54660/.IJMRGE.2022.-3.1.121-127
  • Abuadas, M., Albikawi, Z., & Rayani, A. (2025). The impact of an AI-focused ethics education program on nursing students’ ethical awareness, moral sensitivity, attitudes, and generative AI adoption intention: a quasi-experimental study. BMC Nursing, 24(1), 720. https://doi.-org/10.1186/s12912-025-03458-2
  • Acosta-Enriquez, B. G., Arbulú Ballesteros, M. A., Huamaní Jordan, O., López Roca, C., & Saavedra Tirado, K. (2024). Analysis of college students' attitudes toward the use of ChatGPT in their academic activities: effect of intent to use, verification of information and responsible use. BMC Psychology, 12(1), 255. https://doi.org/10.1186/s40359-024-01764-z
  • Afzal, H., Ali, I., Aslam Khan, M., & Hamid, K. (2010). A study of university students’ motivation and its relationship with their academic performance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2899435
  • Afzal, S., & Jami, H. (2018). Prevalence of academic procrastination and reasons for academic procrastination in university students. Journal of Behavioural Sciences, 28(1), 51–69.
  • Akinwalere, S. N., & Ivanov, V. (2022). Artificial intelligence in higher education: Challenges and opportunities. Border Crossing, 12(1), 1-15. https://doi.org/10.33182/bc.v12i1.2015
  • Aoun, J. E. (2017). Robot-proof: Higher education in the age of artificial intelligence. MIT Press.
  • Apolzan, I., & Cimpineanu, M. J. (2024). Benefits and challenges of using artificial intelligence in education. Euro-Atlantic Resilience Journal, 2(3), 49-72. https://doi.org/10.54675/-EWZM9535
  • Asiksoy, G. (2024). An investigation of university students' attitudes toward artificial intelligence ethics. International Journal of Engineering Pedagogy, 14(8).
  • Becker, A. (2019). Artificial intelligence in medicine: What is it doing for us today?. Health Policy and Technology, 8(2), 198-205. https://doi.org/10.1016/j.hlpt.2019.03.004
  • Berestova, A., Burdina, G., Lobuteva, L., & Lobuteva, A. (2022). Academic motivation of university students and the factors that influence it in an e-learning environment. Electronic Journal of e-Learning, 20(2), 201–210.
  • Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. S. (2025). AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students' AI self-efficacy. Computers and Education: Artificial Intelligence, 8, 100340. https://doi.org/10.1016-/j.caeai.2024.100340
  • Büyüköztürk, Ş. (2002). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum. Pegem Yayıncılık.
  • Cao, L. (2012). Differences in procrastination and motivation between undergraduate and graduate students. Journal of the Scholarship of Teaching and Learning, 12(2), 39–64.
  • Chan, C. K. Y. (2024). Exploring the Factors of" AI Guilt" Among Students--Are You Guilty of Using AI in Your Homework?. arXiv preprint arXiv:2407.10777.
  • Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002
  • Chun Chu, A. H., & Choi, J. N. (2005). Rethinking procrastination: Positive effects of "active" procrastination behavior on attitudes and performance. The Journal of Social Psychology, 145(3), 245–264. https://doi.org/10.3200/-SOCP.145.3.245-264
  • Cohen, J. (2013). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
  • Corkin, D. M., Shirley, L. Y., & Lindt, S. F. (2011). Comparing active delay and procrastination from a self-regulated learning perspective. Learning and Individual Differences, 21(5), 602–606. https://doi.org/10.1016/j.lindif.2011.07.005
  • Deci, E. L., Ryan, R. M. (1985). Conceptualizations of intrinsic motivation and self-determination. Intrinsic motivation and self-determination in human behavior, 11-40.
  • Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/-10.1207/S15327965PLI1104_01
  • Eltahir, M. E., & Babiker, F. M. E. (2024). The influence of artificial intelligence tools on student performance in e-learning environments: Case study. Electronic Journal of e-Learning, 22(9), 91-110. https://doi.org/10.34190/ejel.22.9.3639
  • Ghasemi, A., & Zahediasl, S. (2012). Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. international journal of endocrinology and metabolism, 10 (2), 486–489. https://doi.org/10.5812/ijem.3505
  • He, S. (2017). A multivariate investigation into academic procrastination of university students. Open Journal of Social Sciences, 5(10), 12–25.
  • Hensley, L. C. (2016). The draws and drawbacks of college students' active procrastination. Journal of College Student Development, 57(4), 465–471. https://doi.org/10.1353/csd.2016.0050
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  • Ibrahim, M. B., Mujahid, T., & Anshori, M. I. (2025). Optimization of artificial intelligence-based research in minimizing student academic procrastination. International Conference on Research Issues and Community Service.
  • Kağan, M. (2009). Determining the variables which explain the behavior of academic procrastination in university students. Ankara University Journal of Faculty of Educational Sciences, 42(2), 113–128.
  • Karagüven Ünal, M. H. (2012). Akademik motivasyon ölçeğinin Türkçeye adaptasyonu. Kuram ve Uygulamada Eğitim Bilimleri, 12(4), 2599–2620.
  • Kausar, F. N., Shakir, F., & Aziz, K. (2024). Effect of artificial intelligence usage on students' motivation and learning at university level. Journal of Social Signs Review, 2(4), 307–323.
  • Kim H. Y. (2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52–54. https://doi.org/-10.5395/rde.2013.38.1.52
  • Lee, E. (2005). The relationship of motivation and flow experience to academic procrastination in university students. The Journal of Genetic Psychology, 166(1), 5–15. https://doi.org/-10.3200/GNTP.166.1.5-15
  • Lu, O. H. T., Huang, A. Y. Q., Huang, J. C. H., Lin, A. J. Q., Ogata, H., & Yang, S. J. H. (2018). Applying Learning Analytics for the Early Prediction of Students’ Academic Performance in Blended Learning. Journal of Educational Technology & Society, 21(2), 220–232. http://www.jstor.org/stable/26388400
  • Ma, D., Akram, H., & Chen, I.-H. (2024). Artificial intelligence in higher education: A cross-cultural examination of students’ behavioral intentions and attitudes. International Review of Research in Open and Distributed Learning, 25(3), 134–157.
  • McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955, August 31). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Dartmouth College. https://www.cs.virginia.edu/~robins/Dartmouth.html
  • 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), 12-12. https://doi.org/10.1609/-aimag.v27i4.1904
  • Mohamed, A. M., Shaaban, T. S., Bakry, S. H., Guillén-Gámez, F. D., & Strzelecki, A. (2024). Empowering the faculty of education students: Applying AI’s potential for motivating and enhancing learning. Innovative Higher Education, 1-23. https://doi.org/10.1007/s10755-024-09747-z
  • Morris, L. S., Grehl, M. M., Rutter, S. B., Mehta, M., & Westwater, M. L. (2022). On what motivates us: A detailed review of intrinsic vs. extrinsic motivation. Psychological Medicine, 52(10), 1801–1816. https://doi.org/10.1017/S0033291-721000523
  • Mukhtar, M., Firdos, S. S., Zaka, I., & Naeem, S. (2025). Impact of AI dependence on procrastination among university students. Research Journal of Psychology, 3(1), 246–257.
  • Murakami, Y., Sho, Y., & Inagaki, T. (2024). Improving motivation in learning AI for undergraduate students by case study. Journal of Information Processing, 32, 175–181.
  • Namaziandost, E., & Rezai, A. (2024). Interplay of academic emotion regulation, academic mindfulness, L2 learning experience, academic motivation, and learner autonomy in intelligent computer-assisted language learning: A study of EFL learners. System, 125, 103419. https://doi.org/10.1016/j.system.2024.103419
  • Otto, F., Kling, N., Schumann, C. A., & Tittmann, C. (2023). A Conceptual Approach to an AI-Based Adaptive Study Support System for Individualized Higher Education. International Journal of Advanced Corporate Learning, 16(2), 69.https://doi.org/10.3991/ijac.v16i2.35699
  • Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26, 582-599. https://doi.org/10.1007/-s40593-016-0110-3
  • Sáez-Velasco, S., Alaguero-Rodríguez, M., Rodríguez-Cano, S., & Delgado-Benito, V. (2025). Students’ Attitudes Towards AI and How They Perceive the Effectiveness of AI in Designing Video Games. Sustainability, 17(7), 3096. https://doi.org/10.3390/su17073096
  • Selwyn, N. (2019). Should robots replace teachers?: AI and the future of education. John Wiley & Sons.
  • Shu, X., & Miao, Y. (2021). Research on the impact of artificial intelligence recommendation on academic procrastination under the background of big data: The mediating role of mobile phone addiction. Journal of Physics: Conference Series.
  • Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133(1), 65–94. https://doi.org/10.1037/-0033-2909.133.1.65
  • Steel, P., & Ferrari, J. (2013). Sex, education and procrastination: An epidemiological study of procrastinators’ characteristics from a global sample. European Journal of Personality, 27(1), 51-58. https://doi.org/10.1002/per.1851
  • Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2019). Using multivariate statistics (7th ed.). Pearson.
  • Tuckman, B. W. (1991). The development and concurrent validity of the procrastination scale. Educational and Psychological Measurement, 51(2), 473–480.
  • Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The academic motivation scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52(4), 1003–1017.
  • Van Eerde, W. (2003). A meta-analytically derived nomological network of procrastination. Personality and Individual Differences, 35(6), 1401–1418.
  • Vansteenkiste, M., Simons, J., Lens, W., Sheldon, K. M., & Deci, E. L. (2004). Motivating learning, performance, and persistence: the synergistic effects of intrinsic goal contents and autonomy-supportive contexts. Journal of personality and social psychology, 87(2), 246-260. https://doi.org/10.1037/0022-3514.87.2.246
  • Walker, R., Moraine, A. A., & Black, K. J. (2021). Running and interpreting multiple regression in JASP. Exploring Diversity with Statistics using JASP: Step-by-Step Guides.
  • Wäschle, K., Allgaier, A., Lachner, A., Fink, S., & Nückles, M. (2014). Procrastination and self-efficacy: Tracing vicious and virtuous circles in self-regulated learning. Learning and Instruction, 29, 103–114.
  • Yakut, S. G., Aslan, H. K., & Küsen, G. Y. (2025). Yapay zekâya bakış: Üniversite öğrencilerinin tutumlarına yönelik bir profil çalışması. Journal of Awareness, 10(1), e2684-e2684.
  • Yan, S. (2022). Lack of self-efficacy and resistance to innovation impact on insufficient learning capabilities: Mediating the role of demotivation and moderating the role of institutional culture. Frontiers in Psychology, 13, 923577. https://doi.org/10.3389/fpsyg.2022.923577
  • Yilmaz, R., & Yilmaz, F. G. K. (2023). The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/-j.caeai.2023.100147
  • Zainodin, H. J., Khuneswari, G., Noraini, A., & Haider, F. A. A. (2015). Selected model systematic sequence via variance inflationary factor. International Journal of Applied Physics and Mathematics, 5(2), 105-114. https://doi.org/10.17706/ijapm.2015.5.2.105-114
  • Zhang, H. (2023). Technostress, academic self-efficacy, and resistance to innovation: buffering roles of knowledge sharing culture and constructive deviant behavior. Psychology Research and Behavior Management, 3867-3881. https://doi.org/10.2147/PRBM.S424396
  • 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), 1-27. https://doi.org/10.1186/s41239-019-0171-0
  • Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s154304-21tip4102_2
Toplam 63 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Eğitim Sosyolojisi, Eğitim Psikolojisi
Bölüm Research Articles
Yazarlar

Gözde Önal

Erken Görünüm Tarihi 31 Temmuz 2025
Yayımlanma Tarihi 4 Ağustos 2025
Gönderilme Tarihi 22 Mart 2025
Kabul Tarihi 30 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 22 Sayı: 4

Kaynak Göster

APA Önal, G. (2025). The Effect of University Students’ Attitudes toward Artificial Intelligence on Academic Motivation and Procrastination Behavior. OPUS Journal of Society Research, 22(4), 753-768. https://doi.org/10.26466/opusjsr.1663214