The Effectiveness of Using GenAI Tools for Developing Digital Learning Resources: Evidence from Educators’ Perceptions
Abstract
This study aimed to examine the integration of Generative Artificial Intelligence (GenAI) tools in education, focusing on educators’ perceptions according to the Technology Acceptance Model. The study followed the quasi-experimental design using a one-group design to determine educators’ perceptions of usefulness, ease of use, and attitude toward designing and producing digital learning. Data were collected from 10 participants enrolled in a graduate course via a questionnaire and an in-depth interview with 8 educators to share their experiences with GenAI-based tools. Findings revealed that educators view GenAI tools positively, particularly for their efficiency, ease of use, and ability to enhance content creation and visual resources. Practical, hands-on exposure through targeted training significantly enhanced educators’ perceptions of technology use and their attitudes, highlighting the value of experiential learning in promoting technology acceptance. Although GenAI tools help simplify workload management and design/produce digital materials, there were challenges related to linguistic and cultural adaptability, particularly for non-English languages like Arabic. This study highlighted that GenAI is complementary to education, enhancing traditional methods rather than replacing them. Also, it highlights the need for educators’ strategic training, addressing language barriers in GenAI tools, and adopting blended approaches. Further studies should explore the long-term impacts of GenAI tools on teaching practices and student outcomes, focusing on their efficacy in diverse educational contexts and subject areas.
https://doi.org/10.26803/ijlter.24.4.2
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Alammari, A. (2024). Evaluating generative AI integration in Saudi Arabian education: A mixed-methods study. PeerJ Computer Science, 10, e1879. https://doi.org/10.7717/peerj-cs.1879
Aljohani, R. A. (2021). Teachers and students’ perceptions on the impact of artificial intelligence on English language learning in Saudi Arabia. Journal of Applied Linguistics and Language Research, 8(1), 36–47.
Allaithy, A., & Zaki, M. (2024). Evaluation of AI-generated reading comprehension materials for Arabic language teaching. Linguistic and Philosophical Investigations, 23(1). http://dx.doi.org/10.1080/09588221.2025.2474037
Alnasib, B. N. (2023). Factors affecting faculty members’ readiness to integrate artificial intelligence into their teaching practices: A study from the Saudi higher education context. International Journal of Learning, Teaching and Educational Research, 22(8), 465–491. https://doi.org/10.26803/ijlter.22.8.24
Arvin, N., Hoseinabady, M., Bayat, B., & Zahmatkesh, E. (2023). Teacher experiences with AI-based educational tools. AI and Tech in Behavioral and Social Sciences, 1(2), 26–32. https://doi.org/10.61838/kman.aitech.1.2.5
Baker, T., & Smith, L. (2019). Education rebooted: Exploring the future of artificial intelligence in schools and colleges. https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf
Bryant, J., Heitz, C., Sanghvi, S., & Wagle, D. (2020). How artificial intelligence will impact K–12 teachers. https://www.mckinsey.com/industries/education/our-insights/how-artificial-intelligence-will-impact-k-12-teachers
Carvalho, R. N., Monteiro, C. E. F., & Martins, M. N. P. (2022). Challenges for university teacher education in Brazil posed by the Alpha Generation. Research in Education and Learning Innovation Archives, 28, 61–76. https://doi.org/10.7203/realia.28.21408
Chaka, C. (2024). Currently available GenAI-powered large language models and low-resource languages: any offerings? Wait until you see. International Journal of Learning, Teaching and Educational Research, 23(12), 148–173. https://doi.org/10.26803/ijlter.23.12.9
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, 43. https://doi.org/10.1186/s41239-023-00411-8
Chang, D. H., Lin, M. P.-C., Hajian, S., & Wang, Q. Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability, 15, 12921. https://doi.org/10.3390/su151712921
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Chiu, T. K. F., & Chai, C.-S. (2020). Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability, 12(14), 5568. https://doi.org/10.3390/su12145568
Cooper, G., Park, H., Nasr, Z., Thong, L. P., & Johnson, R. (2019). Using virtual reality in the classroom: Preservice teachers’ perceptions of its use as a teaching and learning tool. Educational Media International, 56(1), 1–13. https://doi.org/10.1080/09523987.2019.1583461
Creswell, J. W., & Creswell, J. D. (2018). Research design: qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.
Darwin, Rusdin, D., Mukminatien, N., Suryati, N., Laksmi, E. D., & Marzuki. (2023). Critical thinking in the AI era: An exploration of EFL students’ perceptions, benefits, and limitations. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2023.2290342
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. http://dx.doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Farjon, D., Smits, A., & Voogt, J. (2019). Technology integration of pre-service teachers explained by attitudes and beliefs, competency, access, and experience. Computers & Education, 130, 81–93. https://doi.org/10.1016/j.compedu.2018.11.010
Fliki. (n.d.). Text to video with AI voiceovers. https://fliki.ai/
Gamma. (n.d.). Create beautiful presentations in seconds with AI. https://gamma.app/
Grájeda, A., Burgos, J., Córdova, P., & Sanjinés, A. (2023). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 11(1). https://doi.org/10.1080/2331186x.2023.2287917
Kuleto, V., Ili?, M., Dumangiu, M., Rankovi?, M., Martins, O. M. D., P?un, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424. https://doi.org/10.3390/su131810424
McKinsey Global Teacher and Student Survey. (2017). Average of Canada, Singapore, United Kingdom, and United States in 2017. How artificial intelligence will impact K–12 teachers. https://www.mckinsey.com/industries/education/our-insights/how-artificial-intelligence-will-impact-k-12-teachers
Milberg, T. (2024). The future of learning: How AI is revolutionizing education 4.0. World Economic Forum. https://www.weforum.org/stories/2024/04/future-learning-ai-revolutionizing-education-4-0/
Mosquera-Gende, I. (2023). Digital tools and active learning in an online university: Improving the academic performance of future teachers. Journal of Technology and Science Education, 13(3), 632–645. https://doi.org/10.3926/jotse.2084
Murphy, R. (2019, January 23). Artificial intelligence applications to support K–12 teachers and teaching: A review of promising applications, challenges, and risks. RAND. https://doi.org/10.7249/pe315
Namatherdhala, B., Mazher, N., & Sriram, G. (2022). A comprehensive overview of artificial intelligence trends in education. International Research Journal of Modernization in Engineering Technology and Science, 4(7). https://www.researchgate.net/publication/361912952_A_COMPREHENSIVE_OVERVIEW_OF_ARTIFICIAL_INTELLIGENCE_TENDS_IN_EDUCATION
Neto, A. J., & Fernandes, M. A. (2019). Chatbot and conversational analysis to promote collaborative learning in distance education. 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 2161-377X, 324–326. http://dx.doi.org/10.1109/ICALT.2019.00102
Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied Sciences, 13, 5783. https://doi.org/10.20944/preprints202303.0473.v1
Ruan, S., Willis, A., Xu, Q., Davis, G. M., Jiang, L., Brunskill, E., & Landay, J. A. (2019). BookBuddy: Turning digital materials into interactive foreign language lessons through a voice chatbot. Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale, L@S’19. https://doi.org/10.1145/3330430.3330431
Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Educ. Sci, 12, 569. https://doi.org/10.3390/educsci12080569
Saudi Data & AI Authority. (2023). Generative AI in education. https://sdaia.gov.sa/en/MediaCenter/KnowledgeCenter/ResearchLibrary/GenAIE.pdf
Saudi Data & AI Authority. (2024). SDAIA academy. https://sdaia.gov.sa/ar/Sectors/BuildingCapacity/academy/bootcamps/Pages/default.aspx
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), 1–23. https://doi.org/10.1186/s41239-021-00292-9
Sergeeva, O. V., Zheltukhina, M. R., Sizova, Z. M., Ishmuradova, A. M., Khlusyanov, O. V., & Kalashnikova, E. P. (2024). Exploring pre-service teachers’ ICT competence beliefs. Contemporary Educational Technology, 16(2), ep500. https://doi.org/10.30935/cedtech/14331
Singh, S. V., & Hiran, K. K. (2022). The impact of AI on teaching and learning in higher education technology. Journal of Higher Education Theory and Practice, 22(13). https://doi.org/10.33423/jhetp.v22i13.5514
Sofia, H. (2023). Embracing digital tools to design materials for a new humanity. International Journal of English, 12(1), 444–51. https://doi.org/10.34293/rtdh.v12iS1-Dec.139
Song, J., Yu, J., Yan, L., Zhang, L., Liu, B., Zhang, Y., & Lu, Y. (2023). Develop AI teaching and learning resources for compulsory education in China. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16033–16039. https://doi.org/10.1609/aaai.v37i13.26904
Stoimenova, N., & Price, R. (2020). Exploring the nuances of designing (with/for) artificial intelligence. Design Issues, 36(4), 45–55. https://doi.org/10.1162/desi_a_00613
Tanwar, R., Bhatia, S., Sapra, V., & Ahuja, N. J. (Eds.). (2023). Artificial intelligence and machine learning: An intelligent perspective of emerging technologies (1st ed.). CRC Press. https://doi.org/10.1201/9781003388319
Terwiesch, C. (2023). Would Chat GPT3 get a Wharton MBA? A prediction based on its performance in the operations management course. Mack Institute for Innovation Management at the Wharton School, University of Pennsylvania.
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(15). https://doi.org/10.1186/s40561-023-00237-x
Utami, S. P. T., Andayani, A., Winarni, R., & Sumarwati, S. (2023). Utilization of artificial intelligence technology in an academic writing class: How do Indonesian students perceive? Contemporary Educational Technology, 15(4), ep450. https://doi.org/10.30935/cedtech/13419
Van den Berg, G., & du Plessis, E. (2023). ChatGPT and generative AI: Possibilities for its contribution to lesson planning, critical thinking, and openness in teacher education. Education Sciences, 13(10), 998. https://doi.org/10.3390/educsci13100998
Wang, P. (2019). On Defining Artificial Intelligence. Journal of Artificial General Intelligence, 10(2), 1–37. https://doi.org/10.2478/jagi-2019-0002
Yilmaz, R., & Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy, and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147
Zaki, M., & Ali, A. (2024). Can AI-generated materials help in Arabic teaching? A study of potential and pitfalls. The Sharjah International Conference on AI & Linguistics, 1(1). https://doi.org/10.54878/h5j8b767
Zhang, C. (Xinyi), Wang, L. H., & Rice, R. E. (2025). U.S. college students’ acceptability and educational benefits of ChatGPT from a digital divide perspective. Computers and Education: Artificial Intelligence, 8, 100385. https://doi.org/10.1016/j.caeai.2025.100385
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research and future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025
Zhao, L., Wu, X., & Luo, H. (2022). Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modeling analysis. Sustainability, 14(11), 14549. https://doi.org/10.3390/su142114549
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