Analyzing the Use of Artificial Intelligence to Support Education and Scientific Writing among Students Using the UTAUT2 Model
DOI:
https://doi.org/10.35870/ljit.v4i2.8605Keywords:
artificial intelligence, students, education, scientific writing, technology acceptanceAbstract
The rapid advancement of artificial intelligence (AI) has created new opportunities to support learning and scientific writing among students. This study examines the factors influencing students’ adoption of AI for educational and scientific writing purposes using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. A quantitative research design was employed, with data collected from 400 students using simple random sampling. A structured questionnaire measured seven UTAUT2 constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit. The results show that performance expectancy, effort expectancy, and facilitating conditions significantly influenced students’ behavioral intention to use AI. Hedonic motivation and habit also emerged as substantial predictors, while price value had a moderate effect. Social influence demonstrated a relatively weaker effect. These findings confirm the relevance of UTAUT2 in explaining AI adoption among students and provide practical implications for educators and libraries in developing AI-supported resources, guidance, and services to enhance learning and scientific writing productivity.
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Baihaqi, W. M., Sulistiyana, F., & Fadholi, A. (2021). Pengenalan artificial intelligence untuk siswa dalam menghadapi dunia kerja di era Revolusi Industri 4.0. RESWARA: Jurnal Pengabdian Kepada Masyarakat, 2(1), 79–88. https://doi.org/10.46576/rjpkm.v2i1.876
Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229–254.
Brown, A., Green, R., & Hill, M. (2021). AI and accessibility in educational resources. Computers & Education, 160, 104–112.
Chen, X., Wu, Z., & Liu, H. (2020). Developing AI tools for library use: A practical approach. Information Processing & Management, 57(3), 102–114.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008
Devianto, Y., & Dwiasnati, S. (2020). Kerangka kerja sistem kecerdasan buatan dalam meningkatkan kompetensi sumber daya manusia Indonesia. Jurnal Telekomunikasi dan Komputer, 10(1), 19. https://doi.org/10.22441/incomtech.v10i1.7460
Dewi, A. O. P. (2020). Kecerdasan buatan sebagai konsep baru pada perpustakaan. Anuva: Jurnal Kajian Budaya, Perpustakaan, dan Informasi, 4(4), 453–460. https://doi.org/10.14710/anuva.4.4.453-460
Dinev, T., McConnell, A., & Smith, H. J. (2015). Informing privacy research through information systems, psychology, and behavioral economics: Thinking outside the "APCO" box. Information Systems Research, 26(4), 639–655. https://doi.org/10.1287/isre.2015.0600
Garcia, M., & Rodriguez, A. (2019). AI in library management: Strategies and outcomes. Library Management, 40(6), 401–413.
Goodhue, D., & Thompson, R. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2). https://aisel.aisnet.org/misq/vol19/iss2/5/
Gursoy, D., Chi, C. G., & Karadag, E. (2019). The impact of technology on travel and tourism: A comprehensive literature review. Tourism Management Perspectives, 29, 70–76.
Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1). https://doi.org/10.9707/2307-0919.1014
Huang, J., Chen, Y., & Xu, L. (2019). Enhancing information retrieval efficiency with AI in libraries. Journal of Library Science, 35(4), 123–134.
Johnson, R., Jones, P., & Smith, K. (2020). Personalization of library services through AI: A case study. Library and Information Science Research, 42(2), 201–215.
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/IJEC.2015100101
Lee, H., & Kim, S. (2021). AI as a tool for scientific writing support. Journal of Educational Technology, 45(1), 67–80.
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737.
Martinez, F., Gomez, S., & Torres, L. (2020). Successful AI integration in library services. Library Hi Tech, 38(2), 289–303.
Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124, 54–74. https://doi.org/10.1037/0033-2909.124.1.54
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Raup, A., Ridwan, W., Khoeriyah, Y., Supiana, S., & Zaqiah, Q. Y. (2022). Deep learning dan penerapannya dalam pembelajaran. JIIP - Jurnal Ilmiah Ilmu Pendidikan, 5(9), 3258–3267. https://doi.org/10.54371/jiip.v5i9.805
Singh, R., & Patel, K. (2021). Implementing AI in academic libraries: Challenges and opportunities. College & Research Libraries, 82(1), 15–28.
Smith, T., & Thompson, L. (2019). AI in academic data analysis: Benefits and challenges. Educational Technology Research and Development, 67(3), 456–470.
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
Wang, Y., & Li, J. (2018). Adoption of AI in libraries: A quantitative analysis. Journal of Academic Librarianship, 44(5), 600–612.
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