DAMPAK PEMANFAATAN ARTIFICIAL INTELLIGENCE (AI) TERHADAP PRODUKTIVITAS AKADEMIK MAHASISWA DENGAN DIGITAL WELL-BEING SEBAGAI INTERVENING
Published 2025-12-20
Keywords
- Adopsi Kecerdasan Buatan,
- Kepercayaan terhadap Teknologi,
- Literasi Digital,
- Motivasi Belajar,
- Pembelajaran Smart
How to Cite
Abstract
Penelitian ini bertujuan untuk mengevaluasi dampak literasi digital, motivasi belajar, dan pembelajaran yang teratur terhadap pemanfaatan teknologi kecerdasan buatan dalam proses belajar. Hal ini juga mempertimbangkan peran pandangan tentang manfaat, kemudahan penggunaan, serta percaya pada teknologi sebagai variabel yang mempengaruhi dan moderasi. Perubahan dalam pembelajaran digital yang semakin maju membuat mahasiswa perlu menyesuaikan teknologi AI dengan baik. Oleh karena itu, penting untuk memahami faktor-faktor yang mendukung penerimaan dan pemanfaatan teknologi ini. Penelitian ini menggunakan pendekatan kuantitatif melalui metode penelitian penjelasan. Data dikumpulkan menggunakan kuesioner dan dianalisis dengan Structural Equation Modeling – Partial Least Squares, sehingga dapat menggambarkan hubungan antara variabel secara menyeluruh. Hasil dari penelitian ini menunjukkan bahwa literasi digital, motivasi belajar, dan pembelajaran yang teratur memiliki peranan penting dalam membentuk pandangan mahasiswa terhadap manfaat dan kemudahan penggunaan teknologi AI. Selain itu, pandangan tentang kemanfaatan dan kemudahan ternyata juga berkontribusi untuk meningkatkan penggunaan AI dalam kegiatan belajar. Keyakinan terhadap teknologi juga memperkuat hubungan ini, sehingga semakin tinggi kepercayaan mahasiswa, semakin besar kecenderungan mereka untuk terus memanfaatkan AI. Penemuan ini memberikan implikasi untuk mengembangkan strategi pembelajaran digital dan pemanfaatan optimal teknologi AI di dunia pendidikan tinggi
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References
- Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning. Computers in Human Behavior, 56, 238–256.
- Alalwan, A. (2022). Students’ adoption of Artificial Intelligence–based learning tools. Education and Information Technologies, 27(4), 567–589.
- Aoun, J. (2017). Robot-proof: Higher education in the age of artificial intelligence. MIT Press.
- Artino, A. (2008). Motivational beliefs and SRL in technology-supported learning. Computers & Education, 50, 1344–1363.
- Belshaw, D. (2014). The essential elements of digital literacies. CreateSpace.
- Bond, M. (2021). Digital transformation in higher education. Educational Technology Review, 29(4), 445–463.
- Broadbent, J. (2017). Self-regulated learning and academic success. Internet and Higher Education, 34, 1–13.
- Chen, L. (2023). The role of AI-assisted learning in improving academic performance. Journal of Educational Computing Research, 61(2), 301–320.
- Chin, W. W. (1998). The partial least squares approach. Modern Methods for Business Research, 295–336.
- Cho, M. H., & Shen, D. (2013). Self-regulation in online learning environments. Internet and Higher Education, 17(1), 19–26.
- Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance. MIS Quarterly, 13(3), 319–340.
- Dwivedi, Y. et al. (2021). Artificial Intelligence in education: Opportunities and challenges. International Journal of Information Management, 58, 102–120.
- Fitriani, D. (2022). College students’ readiness toward AI integration in online learning. Journal of Modern Education Studies, 13(2), 144–159.
- Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online systems. MIS Quarterly, 27(1), 51–90.
- Gilster, P. (1997). Digital literacy. Wiley.
- Gunawan, I. (2022). Students’ behavioral patterns in the use of AI for learning. Journal of Digital Innovation in Education, 3(4), 40–55.
- Hair, J., Hult, G., Ringle, C., & Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage.
- Harrell, P. (2018). Students’ intrinsic motivation and technology use. Journal of Educational Research, 12(1), 55–70.
- Henseler, J., Ringle, C., & Sarstedt, M. (2015). A new criterion for assessing validity in PLS-SEM. Journal of the Academy of Marketing Science, 43(1), 115–135.
- King, W. R., & He, J. (2006). A meta-analysis of TAM. Information & Management, 43(6), 740–755.
- Kukulska-Hulme, A. (2020). Mobile and AI learning in higher education. Educational Technology Research and Development, 68, 2451–2466.
- Lee, M. (2010). Motivation and acceptance of technology in learning contexts. Educational Technology Research & Development, 58(5), 599–624.
- Lestari, K., & Putra, I. (2023). Technology trust and student confidence in AI. International Journal of Digital Learning, 6(3), 120–134.
- List, A. (2019). Defining digital literacy for research. Computers & Education, 138, 103–123.
- McKnight, D. et al. (2002). Developing trust measures for e-commerce. Information Systems Research, 13(3), 334–359.
- Mohammadi, H. (2015). Investigating users’ adoption of e-learning services. Computers in Human Behavior, 51, 381–391.
- Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078.
- Park, S. (2019). Digital literacy and learning outcomes among university students. Journal of Educational Technology, 16(2), 45–61.
- Pavlou, P. (2003). Consumer acceptance of electronic commerce. MIS Quarterly, 27(1), 101–121.
- Rahmawati, S. (2023). AI-supported learning and academic success. Journal of Applied Educational Research, 7(1), 56–70.
- Rosyid, A. (2023). Trends in AI adoption among university students. Journal of Future Learning Technologies, 11(1), 66–83.
- Ryan, R., & Deci, E. (2000). Intrinsic and extrinsic motivations. Contemporary Educational Psychology, 25(1), 54–67.
- Schunk, D. (2012). Learning theories: An educational perspective. Pearson.
- Selwyn, N. (2016). Education and technology: Key issues and debates. Routledge.
- Siau, K., & Shen, Z. (2003). Building customer trust in mobile commerce. Communications of the ACM, 46(4), 91–94.
- Teo, T. (2011). Factors influencing teachers’ intention to use technology. Interactive Learning Environments, 19(3), 247–262.
- Venkatesh, V., & Davis, F. (2000). The extension of TAM. Management Science, 46(2), 186–204.
- Wulandari, R. (2023). AI tools in academic writing improvement. Journal of Language and Educational Technology, 9(3), 120–134.
- Yoon, C. (2009). The moderating effect of cognitive load on perceived usefulness and ease of use in technology adoption. Computers & Education, 52(2), 462–470.
- Zimmerman, B. (2002). Becoming a self-regulated learner. Theory Into Practice, 41(2), 64–70.