Developing and Validating an Instrument to Measure Adoption Ambiguity in Arabic Language Students’ Use of AI-Powered Translation Tools: A Fuzzy Delphi Study بناء وتحقق أداة لقياس غموض تبنّي أدوات الترجمة المدعومة بالذكاء الاصطناعي لدى طلاب اللغة العربية: تطبيق منهجية دلفي

Nik Muhammad Najib Nik Ab Aziz, Wan Ab Aziz Wan Daud

Abstract


Generative artificial intelligence tools pose a fundamental challenge to traditional technology acceptance models, which were originally designed to evaluate static technologies and largely overlook the relational and interactive dimensions of AI-powered translation tools. This challenge is particularly pronounced in the context of learning linguistically complex languages such as Arabic, where constructs such as trust in output accuracy, perceived risk associated with over-reliance, and perceived intelligence of the tool emerge as primary drivers of adoption. To address this theoretical and methodological gap, the present study aims to develop and establish the content validity of a novel measurement instrument that moves beyond the limitations of conventional technology acceptance models and foregrounds human–AI interaction. Given the inherently fuzzy nature of these emergent constructs, the Fuzzy Delphi Method was employed to elicit systematic consensus from a panel of ten experts in Arabic language pedagogy and educational technology on an initial pool of 60 items. The analysis, applying a threshold of d ≤ 0.20 and a minimum agreement level of 75%, resulted in the retention of 58 items. Beyond offering a validated measurement instrument, this study proposes an alternative and more contextually appropriate theoretical framework for assessing AI adoption in language education, thereby opening new avenues for investigating the complex dynamics between learners and intelligent systems.

Keywords


Fuzzy Delphi Method; Content Validity; AI-Powered Translation Tools; Technology Acceptance Model; Arabic Language Education; Higher Education

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References


Ab Aziz, Analiza, Zain, Nor, Jusoh, Masnira, & Zakaria, Rozanizam. (2025). Fuzzy Delphi Method Identifies Key Training Approaches in the Parent-Implemented Intervention Model for Autism. Environment-Behaviour Proceedings Journal, 10(SI33), 71-77. https://doi.org/10.21834/e-bpj.v10iSI33.7068

Adhanawati Abdul Rapa, Ijlal Saja, & Afifah Azmi. (2024). The Use of Artificial Intelligence (AI) Translation Tools: Implications for Third Language Proficiency. International Journal of Research and Innovation in Social Science (IJRISS), 8(09), 1952-1960. https://doi.org/10.47772/IJRISS.2024.8090161

Alkhofi, A. (2025). Man vs. machine: Can AI outperform ESL student translations? PLOS Computational Linguistics, PMC12283786. https://pmc.ncbi.nlm.nih.gov/articles/PMC12283786/

Al-Shaboul, I. A., Ali, A. A., Kariem, A. A., Zarovna, I., Glushchenko, T., & Khasawneh, M. A. S. (2025). Bridging the gap: The role of artificial intelligence in enhancing Arabic language learning, translation, and speech recognition. Research Journal in Advanced Humanities, 6(2). https://doi.org/10.58256/17f30w24

Anwar, Muhammad, & Ahyarudin, Herdi. (2023). AI-Powered Arabic Language Education in the Era of Society 5.0. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 5(1), 50-57. https://doi.org/10.34306/itsdi.v5i1.607

Balaskas, S., Tsiantos, V., Chatzifotiou, S., & Rigou, M. (2025). Determinants of ChatGPT adoption intention in higher education: Expanding on TAM with the mediating roles of trust and risk. Information, 16(2), Article 82. https://doi.org/10.3390/info16020082

Brancu, L., Şahin, F., Guðmundsdóttir, S., & Çetin, F. (2022). Measurement invariance of the Cultural Intelligence Scale across three countries. International Journal of Intercultural Relations, 86, 145–157. https://doi.org/10.1016/j.ijintrel.2021.12.002

Breskas, Athanasios, Hansen-Schirra, Silvia, & Kapnas, Dimitrios. (2025). AI and digital literacy in translation competence development. SKASE Journal of Translation and Interpretation, 18(S), 117-148. https://doi.org/10.33542/JTI2025-S-7

Cheng, C. H., & Lin, Y. (2002). Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European Journal of Operational Research, 142(1), 174–186. https://doi.org/10.1016/S0377-2217(01)00280-6

Ciptono, Argo, Setiyono, Slamet, Nurhidayati, F., & Vikaliana, Resista. (2019). Fuzzy Delphi method in education: A mapping. Journal of Physics: Conference Series, 1360, 012029. https://doi.org/10.1088/1742-6596/1360/1/012029

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), Article 22. https://doi.org/10.1186/s41239-023-00392-8

Dau, L. (2025). Integrating AI into Learning English: University Students’ Perceptions and the Roles of Traditional Methods. Journal of Knowledge Learning and Science Technology, 4(2), 1-17. https://doi.org/10.60087/jklst.v4.n2.001

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Duan, H., Gao, X., & Zhang, Y. (2025). The Application of AI Translation Tools in Improving Students' Translation Fidelity and Accuracy. Arab World English Journal (AWEJ) Special Issue on Artificial Intelligence, 290-306. https://dx.doi.org/10.24093/awej/AI.16

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3

Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: The effect of AI errors on trust and reliance. Academy of Management Journal, 63(3), 627–652. https://doi.org/10.5465/amj.2018.0057

Hanandeh, Anas, Ayasrah, Samer, Kofahi, Ibrahim, & Qudah, Shorouq. (2024). Artificial Intelligence in Arabic Linguistic Landscape: Opportunities, Challenges, and Future Directions. TEM Journal, 14, 3137-3145. https://doi.org/10.18421/TEM134-48

Hashim, Mohd, Idris, Muhammad Zaffwan, & Said, Che. (2021). Applying Fuzzy Delphi Method (FDM) to obtaining the Expert Consensus in Aesthetic Experience (AX) and User Experience (UX) Elements in Augmented Reality Comic (AR Comic). https://www.researchgate.net/publication/348759096_Applying_Fuzzy_Delphi_Method_FDM_to_obtaining_the_Expert_Consensus_in_Aesthetic_Experience_AX_and_User_Experience_UX_Elements_in_Augmented_Reality_Comic_AR_Comic

Hobeika, Eva, Hallit, Rabih, Malaeb, Diana, Sakr, Fouad, Dabbous, Mariam, Merdad, Nisma, Rashid, Tabassum, Amin, Rizwana, Jebreen, Kamel, Zarrouq, Btissame, Alhuwailah, Amthal, Shuwiekh, Hanaa, Hallit, Souheil, Obeid, Sahar, & Fekih-Romdhane, Feten. (2024). Multinational validation of the Arabic version of the Artificial Intelligence Literacy Scale (AILS) in university students. Cogent Psychology, 11. https://doi.org/10.1080/23311908.2024.2395637

Ibrahim, M., Hamzah, A., & Zakaria, N. (2025). Trends and research landscape of the Fuzzy Delphi Method: 2015–2024. GA Excellence Journal of Education.

Ismail, N., & Joshy, L. M. (2025). Exploring the Role of Perceived Usefulness and AI Anxiety in Technology-driven Self-directed Learning: A Paradox Mindset Perspective. Management and Labour Studies, 50(3), 448-465. https://doi.org/10.1177/0258042X251357415

Jailani, Mohd Azli, & Loy, Chong. (2024). THE APPLICATION OF FUZZY DELPHI METHOD IN CONTENT VALIDITY ANALYSIS. https://www.researchgate.net/publication/378774648_THE_APPLICATION_OF_FUZZY_DELPHI_METHOD_IN_CONTENT_VALIDITY_ANALYSIS

Kaharuddin, Andi, Ahmad, Djuwairiah, Mardiana, Latif, Ismail, Arafah, Burhanuddin, & Suryadi, Ray. (2024). Defining the Role of Artificial Intelligence in Improving English Writing Skills Among Indonesian Students. Journal of Language Teaching and Research, 15(2), 568-678. https://doi.org/10.17507/jltr.1502.25

Khalifa, Mohamed, & Albadawy, Mona. (2024). Using Artificial Intelligence in Academic Writing and Research: An Essential Productivity Tool. Computer Methods and Programs in Biomedicine Update, 5, 100145. https://doi.org/10.1016/j.cmpbup.2024.100145

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003

Lai, C. Y., Cheung, K. Y., Chan, C. S., & Law, K. K. (2024). Integrating the adapted UTAUT model with moral obligation, trust and perceived risk to predict ChatGPT adoption for assessment support: A survey with students. Computers and Education: Artificial Intelligence, 6, Article 100246. https://doi.org/10.1016/j.caeai.2024.100246

Marikyan, D., Papagiannidis, S., & Alamanos, E. (2022). A systematic review of the technology acceptance model: A technology and methodology-centric classification. International Journal of Information Management, 63, 102455. https://doi.org/10.1016/j.ijinfomgt.2021.102455

Montes, R., et al. (2024). A 2-tuple fuzzy linguistic Delphi method to test content validity of questionnaires. arXiv preprint arXiv:2403.05550.

Moussawi, S., & Koufaris, M. (2019). Perceived intelligence and perceived anthropomorphism of personal intelligent agents: Scale development and validation. Proceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2019.001

Nagasundram, Usha, & Subramaniam, Vijayaletchumy. (2024). Application of Fuzzy Delphi Method in Developing Common Nouns and Proper Nouns Innovations. International Journal of Academic Research in Progressive Education and Development, 13(3), 22704. http://dx.doi.org/10.6007/IJARPED/v13-i3/22704

Nkomoki, Tony, el Attari, Amal, & Nkomoki, Florence. (2025). The Perception and Utilization of AI-Powered Language Translation Tools for Communication among International Students. International Journal of Research and Innovation in Social Science, VIII(12), 2115-2126. https://doi.org/10.47772/IJRISS.2024.8120178

Paterson, Kate. (2022). Machine translation in higher education: Perceptions, policy, and pedagogy. TESOL Journal, 14(e690). https://doi.org/10.1002/tesj.690

Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147

Septia, Fina, Khodr, Tamer, Abdullah, & Ilah, Nushrotul. (2024). Unlocking AI's Potential in Language Learning: A Comparative Study of Adoption and Effectiveness in Indonesia and Arab Countries. Language, Technology, and Social Media, 2(1), 60-71. https://doi.org/10.70211/ltsm.v2i1.78

Wan Muhd Hafizudin Wan Amri Faizal, Mohd Fauzi Abdul Hamid, & Mohamad Lukman Al Hakim Md. Noor. (2025). Applying the Fuzzy Delphi Method in the Design and Development of the Ar-Kahf Module. International Journal of Research and Innovation in Social Science (IJRISS), 9(08), 7932-7944. https://doi.org/10.47772/IJRISS.2025.908000659

Yusof, Norhanisha, Hashim, Nor Laily, & Hussain, Azham. (2022). A review of fuzzy Delphi method application in human-computer interaction studies. AIP Conference Proceedings, 2472, 040026. https://doi.org/10.1063/5.0094417




DOI: https://doi.org/10.18860/ijazarabi.v9i3.40645

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