The Effect of the Intensity of Artificial Intelligence (AI) Model Use on Academic Procrastination Among Current Students
DOI:
https://doi.org/10.66784/marlajarjournal.v1i2.53Keywords:
Artificial Intelligence (AI), Intensity of AI Use, Academic Procrastination, College Students, Theory of Planned Behavior (TPB)Abstract
Advances in Artificial Intelligence (AI) have transformed the learning process in higher education by making it easier to search for information, complete assignments, and write academic papers. However, these conveniences have the potential to increase the tendency toward academic procrastination. This study aims to analyze the influence of the intention to use Artificial Intelligence on academic procrastination among currently enrolled students at HKBP Nommensen University in Medan. The study is limited to measuring the intention to use AI based on the Theory of Planned Behavior (Ajzen, 1991) and academic procrastination based on the theory by Ferrari, Johnson, and McCown (1995). This study employed a quantitative approach using a survey method involving 350 students selected through purposive sampling. Data were collected using a Likert scale, while data analysis employed simple linear regression. The results indicate that the intention to use AI has a positive and significant effect on academic procrastination (R = 0.760; R² = 0.577; t = 21.808; p < 0.001). These findings indicate that 57.7% of the variation in academic procrastination is explained by the intention to use AI, while 42.3% is influenced by other factors outside the scope of this study. Most respondents exhibited moderate levels of both AI usage intent and academic procrastination. This study concludes that the higher students’ intent to use AI, the greater their tendency toward academic procrastination. It is hoped that these findings will serve as input for higher education institutions in improving students’ AI literacy, ethical use of technology, and self-regulation skills so that the use of AI continues to support the quality of learning
Downloads
References
Chun, P., Yamane, T., & Maemura, Y. (2022). A Deep Learning‐Based Image Captioning Method To Automatically Generate Comprehensive Explanations Of Bridge Damage. Computer-Aided Civil And Infrastructure Engineering, 37(11), 1387–1401. Https://Doi.Org/10.1111/Mice.12793
Davy Tsz Kit, N. G., Luo, W., Chan, H. M. Y., & Chu, S. K. W. (2022). Using Digital Story Writing As A Pedagogy To Develop Ai Literacy Among Primary Students. Computers And Education: Artificial Intelligence, 3(February), 100054. Https://Doi.Org/10.1016/J.Caeai.2022.100054
De Almeida, P. G. R., Dos Santos, C. D., & Farias, J. S. (2021). Artificial Intelligence Regulation: A Framework For Governance. Ethics And Information Technology, 23(3), 505–525. Https://Doi.Org/10.1007/S10676-021-09593-Z
Fern, T., Leiva, A. P., Elvira-Zorzo, N., & Meruvia, H. T. (2025). The Educational Revolution: New Perspectives And Innovative Practices. In The Educational Revolution: New Perspectives And Innovative Practices. Https://Doi.Org/10.14679/4198
Forootan, M. M., Larki, I., Zahedi, R., & Ahmadi, A. (2022). Machine Learning And Deep Learning In Energy Systems: A Review. Sustainability, 14(8), 4832. Https://Doi.Org/10.3390/Su14084832
Hoffmann, C. H. (2022). Technological Brave New World? Eschatological Narratives On Digitalization And Their Flaws. Journal Of Artificial Intelligence And Consciousness, 1–17. Https://Doi.Org/10.1142/S2705078522500096
Hong, S., Kim, S. H., & Kwon, M. (2022). Determinants Of Digital Innovation In The Public Sector. Government Information Quarterly, 39(4), 101723. Https://Doi.Org/Https://Doi.Org/10.1016/J.Giq.2022.101723
Innocenti, S., & Golin, M. (2022). Human Capital Investment And Perceived Automation Risks: Evidence From 16 Countries. Journal Of Economic Behavior & Organization, 195, 27–41. Https://Doi.Org/Https://Doi.Org/10.1016/J.Jebo.2021.12.027
Jia, F., Sun, D., Ma, Q., & Looi, C. K. (2022). Developing An Ai-Based Learning System For L2 Learners’ Authentic And Ubiquitous Learning In English Language. Sustainability (Switzerland), 14(23). Https://Doi.Org/10.3390/Su142315527
Kaluarachchi, T., Reis, A., & Nanayakkara, S. (2021). A Review Of Recent Deep Learning Approaches In Human-Centered Machine Learning. Sensors, 21(7), 2514. Https://Doi.Org/10.3390/S21072514
Karasievych, S., Maksymchuk, B., Kuzmenko, V., Slyusarenko, N., Romanyshyna, O., Syvokhop, E., Kolomiitseva, O., Romanishyna, L., Marionda, I., & Vykhrushch, V. (2021). Training Future Physical Education Teachers For Physical And Sports Activities: Neuropedagogical Approach. Brain. Broad Research In Artificial Intelligence And Neuroscience, 12(4), 543–564.
Kessler, G. (2018). Technology And The Future Of Language Teaching. Foreign Language Annals, 51(1), 205–218. Https://Doi.Org/10.1111/Flan.12318
Khuzaini, Muttaqin, I., Setiadi, B., Irpan, M., & Shaddiq, S. (2024). Human Resource Management In The Transformative Digital Era. International Journal Of Religion, 5(11), 4006–4021. Https://Doi.Org/10.61707/H58ygw76
Kim, A., & Su, Y. (2024). How Implementing An Ai Chatbot Impacts Korean As A Foreign Language Learners’ Willingness To Communicate In Korean. System, 122(February), 103256. Https://Doi.Org/10.1016/J.System.2024.103256
Kochetkova, U., Evdokimova, V., Skrelin, P., German, R., & Novoselova, D. (2022). Interplay Of Visual And Acoustic Cues Of Irony Perception: A Case Study Of Actor’s Speech. Conference On Artificial Intelligence And Natural Language, 82–94.
Mehta, S. (2023). Playing Smart With Numbers: Predicting Student Graduation Using The Magic Of Naive Bayes. International Transactions On Artificial Intelligence, 2(1), 60–75.
Murumkar, S. (2026). The Role Of Machine Learning In Early Detection Of Chronic Diseases Through Wearable Data. International Journal Of Artificial Intelligence, Data Science, And Machine Learning, 6, 172–179. Https://Doi.Org/10.63282/3050-9262.Ijaidsml-V6i2p119
Nowacka, A., & Rzemieniak, M. (2021). The Impact Of The Vuca Environment On The Digital Competences Of Managers In The Power Industry. Energies, 15(1), 185. Https://Doi.Org/10.3390/En15010185
Omirali, A., Kozhakhmet, K., & Zhumaliyeva, R. (2025). Digital Trust In Transition: Student Perceptions Of Ai-Enhanced Learning For Sustainable Educational Futures. Sustainability (Switzerland), 17(17), 1–25. Https://Doi.Org/10.3390/Su17177567
Ortega-Ochoa, E., Quiroga Pérez, J., Arguedas, M., Daradoumis, T., & Marquès Puig, J. M. (2024). The Effectiveness Of Empathic Chatbot Feedback For Developing Computer Competencies, Motivation, Self-Regulation, And Metacognitive Reasoning In Online Higher Education. Internet Of Things (Netherlands), 25(February). Https://Doi.Org/10.1016/J.Iot.2024.101101
Papakostas, C. (2025). Artificial Intelligence In Religious Education: Ethical, Pedagogical, And Theological Perspectives. Religions, 16(5), 563.
Patil, S. (2025). Leading Through Innovation: The Evolving Role Of The Architect In The Cloud And Ai Era. Journal Of Artificial Intelligence, Machine Learning And Data Science, 3(1), 3129–3133. Https://Doi.Org/10.51219/Jaimld/Sandeep-Parshuram-Patil/640
Purba, N., Pujiati, D., Sihombing, P. S. R., Simanjuntak, H., & Sijabat, D. (2025). The Use Of Ai In Elementary School Learning: A Systematic Literature Review. Qalamuna: Jurnal Pendidikan, Sosial, Dan Agama, 17(1), 83–98. Https://Doi.Org/10.37680/Qalamuna.V17i1.6761
Riyanti, R. (2023). Legal Status Of Artificial Intelligence-Based Health Insurance Services: Challenges, Opportunities For Customer Protection.
Soelistiono, S., & Wahidin. (2023). Educational Technology Innovation: Ai-Integrated Learning System Design In Ails-Based Education. Influence: International Journal Of Science Review, 5(2), 470–480. Https://Doi.Org/10.54783/Influencejournal.V5i2.175
Temelkova, M. (2020). Digital Leadership Added Value In The Digital Smart Organizations. Journal Of Engineering Science And Technology Review, Special Issue, 252–257.
Toyokawa, Y., Horikoshi, I., Majumdar, R., & Ogata, H. (2023). Challenges And Opportunities Of Ai In Inclusive Education: A Case Study Of Data-Enhanced Active Reading In Japan. Smart Learning Environments, 10(1), 67. Https://Doi.Org/10.1186/S40561-023-00286-2
Vaddepally, D. (2025). Transfer Learning For Mobile-Based Computer Vision. Journal Of Artificial Intelligence, Machine Learning And Data Science, 3(3), 2901–2906. Https://Doi.Org/10.51219/Jaimld/Dheeraj-Vaddepally/605
Valluri, M. (2024). The Role Of Consumer Insights In Product Innovation: Leveraging Advanced Analytics And Emerging Technologies. Journal Of Artificial Intelligence, Machine Learning And Data Science, 1(2), 1487–1490. Https://Doi.Org/10.51219/Jaimld/Mohan-Valluri/335
Walker, H. L., Ghani, S., Kuemmerli, C., Nebiker, C. A., Müller, B. P., Raptis, D. A., & Staubli, S. M. (2023). Reliability Of Medical Information Provided By Chatgpt: Assessment Against Clinical Guidelines And Patient Information Quality Instrument. J Med Internet Res, 25, E47479. Https://Doi.Org/10.2196/47479
Zhang, L., & Zhang, X. (2025). Impact Of Digital Government Construction On The Intelligent Transformation Of Enterprises: Evidence From China. Technological Forecasting And Social Change, 210, 123787. Https://Doi.Org/Https://Doi.Org/10.1016/J.Techfore.2024.123787
Zhang, Q. (2023). Secure Preschool Education Using Machine Learning And Metaverse Technologies. Applied Artificial Intelligence, 37(1). Https://Doi.Org/10.1080/08839514.2023.2222496
Zhang, Z., & Huang, X. (2024). The Impact Of Chatbots Based On Large Language Models On Second Language Vocabulary Acquisition. Heliyon, 10(3), E25370. Https://Doi.Org/10.1016/J.Heliyon.2024.E25370
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Melicha Solagratiae Sirait, Hotpascaman Simbolon (Author)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.











