Analyzing Intentions to Adopt Artificial Intelligence in Islamic Education Practices
DOI:
https://doi.org/10.30595/ajsi.v6i1.24766Keywords:
Artificial Intelligence (AI), Technology Acceptance Model (TAM), Islamic StudyAbstract
This research explored the key factors shaping the adoption of artificial intelligence (AI) in Islamic studies education, employing the Technology Acceptance Model (TAM) as the guiding framework. A quantitative approach was adopted, with data collected from 255 participants through an online survey distributed via Google Forms. The reliability and validity of the survey instrument were rigorously tested. Data was analyzed using Structural Equation Modeling (SEM) with Smart PLS software. The findings revealed that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) significantly influence educators' attitudes toward using AI in Islamic education. Notably, PEOU emerged as a stronger determinant of attitude, highlighting the importance of user-friendly technologies in fostering acceptance. Furthermore, the study established a clear link between positive attitudes toward AI and Behavioral Intention (BI) to adopt the technology, which drives actual usage. These results underscore the critical role of simplifying technology to encourage its adoption and suggest practical pathways for integrating AI into Islamic studies teaching and learning.
References
Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–7224.
Al-Hamad, M., Mbaidin, H., AlHamad, A., Alshurideh, M., Kurdi, B., & Al-Hamad, N. (2021). Investigating students’ behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic. International Journal of Data and Network Science, 5(3), 321–330.
Al-Hattami, H. M. (2023). Understanding perceptions of academics toward technology acceptance in accounting education. Heliyon, 9(1). https://doi.org/10.1016/j.heliyon.2023.e13141
Ary, D., Lucy Cheser Jacobs, Chris Sorensen, & Asghar Razavieh. (2010). Introduction to Research in Education. Wadsworth.
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100099
Azman, M. N. A., Kamis, A., Kob, C. G. C., Abdullah, A. S., Jerusalem, M. A., Komariah, K., & Budiastuti, E. (2020). How good is Myguru: The lecturers’ perceived usefulness and attitude. Cakrawala Pendidikan, 39(2), 422–431.
Cardona, M. A., Rodríguez, R. J., & Ishmael, K. (2023). Artificial Intelligence and the Future of Teaching and Learning Insights and Recommendations Artificial Intelligence and the Future of Teaching and Learning. https://tech.ed.gov
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/https://doi.org/10.1016/j.caeai.2022.100118
Creswell, J. W. (2014). Research Design Qualitative, Quantitative and Mixed Methods Approaches. Sage Publishing.
Dakakni, D., & Safa, N. (2023). Artificial intelligence in the L2 classroom: Implications and challenges on ethics and equity in higher education: A 21st century Pandora’s box. Computers and Education: Artificial Intelligence, 5, 100179. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100179
Davis, F. D. (1989a). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
Davis, F. D. (1989b). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. . MIS Quarterly, 13(3), 319–340.
De Cosmo, L. M., Piper, L., & Di Vittorio, A. (2021). The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing, 2021, 83–102.
Fathema, N., Shannon, D., & Ross, M. (2015). Expanding The Technology Acceptance Model (TAM) to Examine Faculty Use of Learning Management Systems (LMSs) In Higher Education Institutions. In MERLOT Journal of Online Learning and Teaching (Vol. 11, Issue 2).
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, Marko. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (Second Edition). SAGE Publications.
Hart, S. A., & Laher, S. (2015). Perceived usefulness and culture as predictors of teachers attitudes towards educational technology in South Africa. South African Journal of Education, 35(4).
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence In Education Promises and Implications for Teaching and Learning. http://bit.ly/AIED-
Huwaida, H., Rofi’i, Sri Imelda, Said Muhammad, & Heppy Mutammimah. (2023). E-Learning Acceptance in Business Administration Department State Polytechnic of Banjarmasin. Jurnal Multidisiplin Madani, 3(8), 1712–1725. https://doi.org/10.55927/mudima.v3i8.4428
Jameel, A. S., Karem, M. A., & Alheety, A. S. (2022). Behavioral intention and actual use of Mobile learning during the COVID-19 pandemic in the higher education system. International Conference on Emerging Technologies and Intelligent Systems, 155–167.
Lawrence, J. E., & Tar, U. A. (2018). Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International, 55(1), 79–105. https://doi.org/10.1080/09523987.2018.1439712
Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining teachers’ behavioral intention to use E-learning in teaching of mathematics: An extended TAM model. Contemporary Educational Technology, 13(2), ep298.
Moses, P., Wong, S. L., Bakar, K. A., & Mahmud, R. (2013). Perceived usefulness and perceived ease of use: Antecedents of attitude towards laptop use among science and mathematics teachers in Malaysia. The Asia-Pacific Education Researcher, 22, 293–299.
Nja, C. O., Idiege, K. J., Uwe, U. E., Meremikwu, A. N., Ekon, E. E., Erim, C. M., Ukah, J. U., Eyo, E. O., Anari, M. I., & Cornelius-Ukpepi, B. U. (2023a). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00261-x
Nja, C. O., Idiege, K. J., Uwe, U. E., Meremikwu, A. N., Ekon, E. E., Erim, C. M., Ukah, J. U., Eyo, E. O., Anari, M. I., & Cornelius-Ukpepi, B. U. (2023b). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00261-x
Nuryakin, N., Rakotoarizaka, N. L. P., & Musa, H. G. (2023). The effect of perceived usefulness and perceived easy to use on student satisfaction the mediating role of attitude to use online learning. APMBA (Asia Pacific Management and Business Application), 11(3), 323–336.
Panergayo, A. A. (2021). Students’ Behavioral Intention to Use Learning Management System: The Mediating Role of Perceived Usefulness and Ease of Use. Albert Andry E. Panergayo and John Vincent C. Aliazas," Students’ Behavioral Intention to Use Learning Management System: The Mediating Role of Perceived Usefulness and Ease of Use," International Journal of Information and Education Technology, 11(11), 538–545.
Prahani, B. K., Rizki, I. A., Jatmiko, B., Suprapto, N., & Amelia, T. (2022). Artificial Intelligence in Education Research During the Last Ten Years: A Review and Bibliometric Study. International Journal of Emerging Technologies in Learning, 17(8), 169–188. https://doi.org/10.3991/ijet.v17i08.29833
Qashou, A. (2021). Influencing factors in M-learning adoption in higher education. Education and Information Technologies, 26(2), 1755–1785.
Roy, R., Babakerkhell, M. D., Mukherjee, S., Pal, D., & Funilkul, S. (2022). Evaluating the Intention for the Adoption of Artificial Intelligence-Based Robots in the University to Educate the Students. IEEE Access, 10, 125666–125678. https://doi.org/10.1109/ACCESS.2022.3225555
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00292-9
Strzelecki, A. (n.d.). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2023.2209881
Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities. Computers and Education: Artificial Intelligence, 4, 100124. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100124
Teo, T., Fan, X., & Du, J. (2015). Technology acceptance among pre-service teachers: Does gender matter? Australasian Journal of Educational Technology, 31(3).
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(1). https://doi.org/10.1186/s40561-023-00237-x
Wang Youmei, Liu Chenchen, & Tu Yun-Fang. (2021). Factors Affecting the Adoption of AI-Based Applications in Higher Education: An Analysis of Teachers’ Perspectives Using Structural Equation Modeling. Educational Technology & Society, 24(3), 116–129.
Weng, F., Yang, R. J., Ho, H. J., & Su, H. M. (2018). A tam-based study of the attitude towards use intention of multimedia among school teachers. Applied System Innovation, 1(3), 1–9. https://doi.org/10.3390/asi1030036
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00420-7.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Alhamra Jurnal Studi Islam
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
