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International Journal of Academic Research in Business and Social Sciences

Open Access Journal

ISSN: 2222-6990

The Adoption of AI-Enabled Adaptive e-Learning Environment in Palestinian Schools: Integrating Extended Technology Acceptance Model and System Success Model

Ashraf A. Qahman, Hadi A. Dahlan. Mohamad S. Zakaria, Muhammad Hussin, Yousef K. A. Samra, Reda F. Aldaya, Rosseni Din, Nabilah Othman

http://dx.doi.org/10.6007/IJARBSS/v13-i12/20277

Open access

AI-enabled adaptive learning environment is revolutionizing educational landscapes globally by offering personalized learning experiences. Despite their proven effectiveness, the acceptance of these technologies remains largely unexplored in Palestinian schools. This study aims to fill this research gap by investigating the factors influencing the acceptance of AI-enabled adaptive e-learning environment among schools in the Gaza Strip, Palestine. Utilizing the Extended Technology Acceptance Model (ETAM) and the DeLone and McLean Information Systems (IS) Success Model, a total of 202 schools participated in the study. The study examined several latent variables, including User Interface (UI), Perceived Ease of Use (PEU), Perceived Usefulness (PU), Information Quality (IQ), System Quality (SQ), Behavioral Intentions (BI), and Actual Use (AU). Statistical analyses, including Structural Equation Modeling (SEM), revealed that PU, PEU, and SQ positively influenced schools' behavioral intentions (BI) towards the actual use of the system. However, IQ showed mixed results, suggesting room for improvement in content delivery. The findings confirm that the level of acceptability among schools is significantly influenced by the system's ease of use, quality, and perceived usefulness, leading to a positive behavioral intention for its actual adoption. Given these results, schools and policymakers should focus on these critical aspects to facilitate broader acceptance and effective implementation of adaptive learning systems. This model can also serve as a framework for future quantitative and qualitative studies in areas of Palestine currently inaccessible due to Israeli occupation, such as the West Bank, or for other countries and can be adapted for use in other contexts.

Adzhari, N. A. N., & Din, R. (2021). Enhancing English language teaching by implementing ICT as an educational tool. Journal of Personalized Learning, 4(1), 101-110.
Al-Adwan, A. S., Nofal, M., Akram, H., Albelbisi, N. A., & Al-Okaily, M. (2022). Towards a Sustainable Adoption of E-Learning Systems: The Role of Self-Directed Learning. Journal of Information Technology Education: Research, 21, 245-267.
Alonso, F., López, G., Manrique, D., & Viñes, J. M. (2005). An instructional model for web?based e?learning education with a blended learning process approach. British Journal of educational technology, 36(2), 217-235.
Alumran, A., Hou, X.-Y., Sun, J., Yousef, A. A., & Hurst, C. (2014). Assessing the construct validity and reliability of the parental perception on antibiotics (PAPA) scales. BMC public health, 14, 1-9.
Amin, M. M., & Paiman, N. (2022). University English Language Teachers’ Use of Digital Platforms for Online Teaching. International Journal of Emerging Technologies in Learning (Online), 17(20), 134-148.
Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective: Pearson.
Chang, Y.-C., Enkhjargal, U., Huang, C.-I., Lin, W.-L., & Ho, C.-M. (2020). Factors affecting the Internet banking adoption. Jurnal Ekonomi Malaysia, 54(3), 117-131.
Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in psychology, 10, 1652, 1-14.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Freeze, R. D., Alshare, K. A., Lane, P. L., & Wen, H. J. (2010). IS success model in e-learning context based on students' perceptions. Journal of Information systems education, 21(2), 173-184.
Hallam, S., Burnard, P., Robertson, A., Saleh, C., Davies, V., Rogers, L., & Kokatsaki, D. (2009). Trainee primary-school teachers’ perceptions of their effectiveness in teaching music. Music education research, 11(2), 221-240.
Hawash, B., Mokhtar, U. A., & Yusof, Z. M. (2021). Users' acceptance of an electronic record management system in the context of the oil and gas sector in Yemen: an application of ISSsxdM-TAM. International Journal of Management and Enterprise Development, 20(1), 75-98.
Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017, 1-12.
Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & management, 35(4), 237-250.
Mastan, I. A., Sensuse, D. I., Suryono, R. R., & Kautsarina, K. (2022). Evaluation of distance learning system (e-learning): a systematic literature review. Jurnal Teknoinfo, 16(1), 132-137.
Mohtar, L. E., Halim, L., Rahman, N. A., Maat, S. M., Iksan, Z. H., & Osman, K. (2019). A model of interest in STEM careers among secondary school students. Journal of Baltic Science Education, 18(3), 404-416.
Mustafa, A. S., & Garcia, M. B. (2021). Theories integrated with technology acceptance model (TAM) in online learning acceptance and continuance intention: A systematic review. Paper presented at the 2021 1st Conference on online teaching for mobile education (OT4ME), 68-72.
Obaid, T. (2020). Factors driving e-learning adoption in palestine: an integration of technology acceptance model and IS success model. 1-5.
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893-7925.
Prasetyo, Y. T., Ong, A. K. S., Concepcion, G. K. F., Navata, F. M. B., Robles, R. A. V., Tomagos, I. J. T., Redi, A. A. N. P. (2021). Determining factors Affecting acceptance of e-learning platforms during the COVID-19 pandemic: Integrating Extended technology Acceptance model and DeLone & Mclean is success model. Sustainability, 13(15), 8365.
Prasetyo, Y. T., Roque, R. A. C., Chuenyindee, T., Young, M. N., Diaz, J. F. T., Persada, S. F., . . . Perwira Redi, A. A. N. (2021). Determining factors affecting the acceptance of medical education elearning platforms during the covid-19 pandemic in the philippines: Utaut2 approach. Paper presented at the Healthcare.9(7), 780, 1-13.
Purwaningsih, R., & Kusuma, P. D. (2015). Analisis Faktor-Faktor Yang Mempengaruhi Kinerja Usaha Kecil Dan Menengah (UKM) Dengan Metode Structural Equation Modeling (Studi kasus UKM berbasis Industri Kreatif Kota Semarang). Paper presented at the Prosiding Seminar Sains Nasional dan Teknologi, 1(1),7-12.
Rajak, M., & Shaw, K. (2021). An extension of technology acceptance model for mHealth user adoption. Technology in Society, 67, 101800.
Subaih, R. H. A., Sabbah, S. S., & Al-Duais, R. N. E. (2021). Obstacles facing teachers in Palestine while implementing e-learning during the COVID-19 pandemic. Asian Social Science, 17(4), 44-45.
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11)., 1-9.
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in science education, 48, 1273-1296.
Tung, F.-C., Chang, S.-C., & Chou, C.-M. (2008). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. International journal of medical informatics, 77(5), 324-335.
Vanneste, D., Vermeulen, B., & Declercq, A. (2013). Healthcare professionals’ acceptance of BelRAI, a web-based system enabling person-centred recording and data sharing across care settings with interRAI instruments: a UTAUT analysis. BMC medical informatics and decision making, 13(1), 1-14.

(Qahman et al., 2023)
Qahman, A. A., Zakaria, H. A. D. M. S., Hussin, M., Samra, Y. K. A., Aldaya, R. F., Din, R., & Othman, N. (2023). The Adoption of AI-Enabled Adaptive e-Learning Environment in Palestinian Schools: Integrating Extended Technology Acceptance Model and System Success Model. International Journal of Academic Research in Business and Social Sciences, 13(12), 4374–4389.