The Effect of Artificial Intelligence (AI) Use on Increasing Student Motivation in Thesis Writing
Abstract
The development of Artificial Intelligence (AI) technology in higher education has influenced various academic practices of students, including in the process of writing theses. This study aims to analyze the effect of AI use on student motivation in completing their theses. The research used a quantitative approach with a survey design involving 35 students from the Japanese Language Education Study Program at Surabaya State University who were writing their theses. Data were collected through a questionnaire with a five-point Likert scale that measured the level of AI use and student motivation in the thesis-writing process. Data analysis was performed using descriptive statistics and simple linear regression analysis. The results showed that the level of AI usage was in the high category with an average score of 3.60, while student motivation was in the moderate-to-high category with an average score of 3.12. The regression analysis results showed that AI usage had a positive and significant effect on student motivation in completing their thesis with a coefficient of determination of 0.510. These findings indicate that the use of AI technology can serve as a learning-support tool that helps students understand academic concepts, organize research ideas, and improve efficiency in the thesis-writing process. However, student motivation is not only influenced by the use of technology, but also by various internal and external factors such as academic support, psychological conditions, and the learning environment.
References
Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2016). Instruction based on adaptive learning technologies. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of research on learning and instruction (2nd ed., pp. 522–560). Routledge.
Aleven, V., Roll, I., McLaren, B., & Koedinger, K. (2017). Help helps, but only so much: Research on help seeking with intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 27(1), 205–223.
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Langensiepen, M., & Siemens, G. (2024). Generative artificial intelligence in higher education: A global perspective. International Journal of Educational Technology in Higher Education, 21. https://doi.org/10.1186/s41239-024-00479-1
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12149-0
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. Computers and Education: Artificial Intelligence, 4, 100131.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., ... Williams, M. D. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT in higher education. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846
Freeman, J. (2025). Student Generative AI Survey 2025. Higher Education Policy Institute.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Kementerian Komunikasi dan Informatika Republik Indonesia. (2023). Etika Kecerdasan Artifisial di Indonesia. Jakarta: Kementerian Komunikasi dan Informatika.
Kusumawati, A. A. (2024). Self regulation dalam meningkatkan motivasi belajar peserta didik. Jurnal Pendidikan, 13(2), 242–247.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Ouyang, F., Zheng, L., Jiao, P., & Moore, A. (2022). Artificial intelligence in online higher education: A systematic review. Education and Information Technologies, 27, 7893–7925. https://doi.org/10.1007/s10639-022-10925-9
Perdana, D. A. (2025). Integrasi large language models dalam pembelajaran analisis teks sastra Arab di perguruan tinggi. Jurnal Identik, 2(2), 32–40.
Pratama, A., & Nugroho, R. (2024). Pengaruh ChatGPT dan literasi digital terhadap motivasi belajar mahasiswa. Jurnal Pendidikan dan Pembelajaran, 8(1), 45–56.
Putri, S. N., & Alfiansyah. (2025). Implementasi pembelajaran adaptif berbasis bahasa alami untuk meningkatkan pencapaian belajar di sekolah dasar. Journal Creativity, 3(2), 378–389.
Rahmawati, D., Putri, N., & Lestari, A. (2024). Hubungan penggunaan ChatGPT terhadap motivasi belajar mahasiswa tingkat akhir. Research Journal of Education, 5(1), 55–63.
Refaldi, D. A. (2024). Pengaruh generative AI terhadap kemampuan berpikir kritis mahasiswa. Jurnal Sistem Informasi Akademik, 3(3), 9–19.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020
Salsabila, N. (2023). ChatGPT sebagai inovasi pembelajaran untuk meningkatkan motivasi belajar mandiri mahasiswa. Jurnal Pendidikan, 18(2), 125–134.
Siregar, M. (2025). Pengaruh penggunaan ChatGPT terhadap motivasi belajar mahasiswa fakultas ekonomi. Jurnal Literasi Ekonomi dan Bisnis, 3(1), 41–50.
Tasya, C. H., Sangka, K. B., & Octoria, D. (2025). Pengaruh pemanfaatan Artificial Intelligence terhadap motivasi belajar mahasiswa dengan literasi digital sebagai variabel moderating. Jurnal Pendidikan Ekonomi, 13(2), 153–165.
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, 15. https://doi.org/10.1186/s40561-023-00237-x
UNESCO. (2023). Guidance for Generative AI in Education and Research. UNESCO Publishing.
Uno, H. B. (2021). Teori motivasi dan pengukurannya. Jakarta: Bumi Aksara.
Wang, X., & Huang, Y. (2025). Self-determination theory in AI-supported learning environments. International Journal of Educational Technology in Higher Education, 22(1).
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, 39. https://doi.org/10.1186/s41239-019-0171-0
Zhai, X. (2024). Exploring the impact of ChatGPT: Conversational AI in education. Frontiers in Education, 9.

