Analisis Jalur Simplification Model Untuk Eksplorasi Penerimaan Aplikasi AI Sebagai Media Pembelajaran
DOI:
https://doi.org/10.62951/bridge.v2i2.72Keywords:
Artificial Intelligence, Acceptance, Simplification ModelAbstract
The development of AI is having a significant impact on education. AI applications are designed to improve student understanding, but their implementation faces challenges that affect user acceptance. Initial research with the UTAUT2 model in public high schools showed that only hedonic motivation, habit, facilitating conditions, and behavioral intention had a significant influence on application acceptance and use. Therefore, this study will use a simplification model that focuses on significant variables, namely hedonic motivation (HM) and habit (H) on behavioral intention (BI), as well as facilitating conditions (FC), habit (H), and behavioral intention (BI) on use behavior (UB). This study is expected to provide in-depth insight into the important factors that influence the acceptance and use of AI applications in learning, as well as provide practical contributions to improve the implementation of learning technology in high schools.
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References
Abdillah, W. (2018). Metode penelitian terpadu sistem informasi pemodelan teoretis, pengukuran, dan pengujian statistis (R. I. Utami, Ed.; 1st ed.).
Ain, N. U., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning management system use: An extension of UTAUT2. Information Development, 32(5), 1306–1321. https://doi.org/10.1177/0266666915597546
Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2020). Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model. Education and Information Technologies, 25(1), 419–435. https://doi.org/10.1007/s10639-019-09965-5
Anekawati, A., Otok, B. W., Purhadi, & Sutikno. (2017). Structural Equation Modelling with Three Schemes Estimation of Score Factors on Partial Least Square (Case Study: The Quality of Education Level SMA/MA in Sumenep Regency). Journal of Physics: Conference Series, 855(1). https://doi.org/10.1088/1742-6596/855/1/012006
Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society, 18(3), 659–673. https://doi.org/10.1007/s10209-019-00685-8
Baker, T., Smith, L., & Anissa, N. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Retrieved May, 12(February), 2020. https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf
Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 1–5. https://doi.org/10.1016/j.caeai.2020.100001
Kante, M., Chepken, C., & Oboko, R. (2018). Partial Least Square Structural Equation Modelling’ use in Information Systems: An updated guideline of practices in exploratory settings Facilitating Learner Group Interactions using Intelligent Agents in collaborative mobile learning View project Afri. Kabarak Journal of Research & Innovation, 6(1), 49–67. http://eserver.kabarak.ac.ke/ojs/
Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2023). Trustworthy Artificial Intelligence: A Review. ACM Computing Surveys, 55(2), 1–38. https://doi.org/10.1145/3491209
Kotamena, F., Sinaga, P., Sudibjo, N., & Hidayat, D. (2024). Student use behavior in determining majors: Is it determined by self-congruity, social influence, information usefulness, through mediating information adoption, and behavioral intention. Computers in Human Behavior Reports, 14(June 2023), 100383. https://doi.org/10.1016/j.chbr.2024.100383
Kumar, J. A., & Bervell, B. (2019). Google Classroom for mobile learning in higher education: Modelling the initial perceptions of students. Education and Information Technologies, 24(2), 1793–1817. https://doi.org/10.1007/s10639-018-09858-z
Mohd Rahim, N. I., A. Iahad, N., Yusof, A. F., & A. Al-Sharafi, M. (2022). AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach. Sustainability (Switzerland), 14(19).
Nestor, M., Loredana, F., Erik, B., John, E., Katrina, L., Terah, L., James, M., Helen, N., Juan, C. N., Vanessa, P., Yoav, S., Russell, W., Jack, C., & Raymond, P. (2023). Artificial Intelligence Index Report Introduction to the AI Index Report 2023. Human-Centered Artificial Intelligence, 1–386.
Romero-Rodríguez, J. M., Ramírez-Montoya, M. S., Buenestado-Fernández, M., & Lara-Lara, F. (2023). Use of ChatGPT at University as a Tool for Complex Thinking: Students’ Perceived Usefulness. Cultura de Los Cuidados, 12(2), 323–339. https://doi.org/10.7821/naer.2023.7.1458
Strzelecki, A. (2023). Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innovative Higher Education. https://doi.org/10.1007/s10755-023-09686-1
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.
Wong, K. K. (2013). Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS.
Zwain, A. A. A. (2019). Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system: An expansion of UTAUT2. Interactive Technology and Smart Education, 16(3), 239–254. https://doi.org/10.1108/ITSE-09-2018-0065
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