عوامل موثر بر استفاده واقعی از هوش مصنوعی در آموزش با نقش میانجی تطبیق‌پذیری با هوش مصنوعی و تعدیلگر دلبستگی به آموزش سنتی در میان اساتید دانشگاه آزاد

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استاد مدعو، دانشکده علوم تربیتی، دانشگاه آزاد، داراب، ایران

2 استاد مدعو، دانشکده علوم تربیتی، دانشگاه آزاد، داراب، ایران.

3 دانشجوی کارشاسی ارشد، دانشکده علوم تربیتی، دانشگاه آزاد، داراب، ایران.

چکیده

هدف از پژوهش حاضر تعیین عوامل موثر بر استفاده واقعی از هوش مصنوعی با نقش میانجی تطبیق‌پذیری با هوش مصنوعی و تعدیلگر دلبستگی به آموزش سنتی در میان اساتید دانشگاه است. بر اساس ادبیات، متغیرهای اثربخشی هزینه، تناسب فن‌آوری- وظیفه، انتظار عملکرد، امنیت و نگرانی‌های حفظ حریم خصوصی به عنوان عوامل موثر شناسایی شدند. نوع پژوهش توصیفی-همبستگی است. جامعه آماری پژوهش اساتید دانشگاه آزاد اسلامی شهرستان‌های جنوب و شرق استان فارس است. روش نمونه‌گیری طبقه‌ای و حجم نمونه بر اساس نرم‌افزار Gpower برآورد شد. برای جمع‌آوری داده‌ها از پرسشنامه‌های استاندارد استفاده گردید. نتایج حاصل از مدل معادلات ساختاری در نرم‌افزار پی‌ال‌اس نشان داد که اثربخشی هزینه، تناسب فن‌آوری- وظیفه وانتظار عملکرد بر تطبیق‌پذیری با هوش مصنوعی و استفاده واقعی از هوش مصنوعی تاثیر دارند. امنیت و نگرانی‌های حفظ حریم خصوصی بر تطبیق‌پذیری با هوش مصنوعی تاثیر منفی دارد؛ اما تاثیر آن بر استفاده واقعی از هوش مصنوعی رد شد. تطبیق‌پذیری با هوش مصنوعی بر استفاده واقعی از هوش مصنوعی تاثیر دارد. متغیرهای میانجی نشان داد که تطبیق‌پذیری با هوش مصنوعی بین متغیرهای پژوهش نقش میانجی دارد. در نهایت، اثر تعدیلگر دلبستگی به آموزش سنتی در رابطه علی تطبیق‌پذیری با هوش مصنوعی بر استفاده واقعی از هوش مصنوعی تایید شد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Factors affecting the actual use of artificial intelligence in education with the mediating role of adaptability to artificial intelligence and the moderating role of stickiness to traditional education among Azad University professors.

نویسندگان [English]

  • Alireza Saremi 1
  • Alireza Ramazanpoor 2
  • Ziba Sirjani 3
1 Visiting Professor, Faculty of Educational Sciences, Azad University, Darab, Iran
2 Visiting Professor, Faculty of Educational Sciences, Azad University, Darab, Iran.
3 Master's student, Faculty of Educational Sciences, Azad University, Darab, Iran.
چکیده [English]

The aim of the present study is to determine the factors affecting the actual use of artificial intelligence with the mediating role of adaptability to artificial intelligence and moderating Stickiness to traditional education among university professors. Based on the literature, the variables of cost effectiveness, technology-task fit, performance expectation, security, and privacy concerns were identified as effective factors. The type of research is descriptive-correlational. The statistical population of the study is Islamic Azad University professors in the southern and eastern counties of Fars province. The stratified sampling method and sample size were estimated based on Gpower software. Standard questionnaires were used to collect data. The results of the structural equation model in PLS software showed that cost effectiveness, technology-task fit, and performance expectations have an effect on adaptability to artificial intelligence and actual use of artificial intelligence. Security and privacy concerns have a negative effect on adaptability to artificial intelligence; but its effect on actual use of artificial intelligence was rejected. Adaptability to artificial intelligence has an effect on actual use of artificial intelligence. Mediating variables showed that adaptability to AI has a mediating role between the research variables. Finally, the moderating effect of Stickiness to traditional education in the causal relationship between adaptability to AI and actual use of AI was confirmed.

کلیدواژه‌ها [English]

  • Adaptability to AI
  • Actual use of AI
  • Stickiness to traditional education
Abdelaal, N. M., & Al Sawi, I. (2024). Perceptions, challenges, and prospects: University professors' use of artificial intelligence in education. Australian Journal of Applied Linguistics7(1), 1-24. https://doi.org/10.29140/ajal.v7n1.1309
Ab Hamid, M. R., Sami, W., & Sidek, M. M. (2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890, Article 012163. https://doi.org/10.1088/1742-6596/890/1/012163
Al-Adwan, A. S., & Al-Debei, M. M. (2024). The determinants of Gen Z's metaverse adoption decisions in higher education: Integrating UTAUT2 with personal innovativeness in IT. Education and Information Technologies29(6), 7413-7445. https://doi.org/10.1007/s10639-023-12080-1
Al-Mamary, Y. H., Alfalah, A. A., Alshammari, M. M., & Abubakar, A. A. (2024). Exploring factors influencing university students’ intentions to use ChatGPT: analyzing task-technology fit theory to enhance behavioral intentions in higher education. Future Business Journal10(1), Article 119. https://doi.org/10.1186/s43093-024-00406-5
Alrayes, A., Henari, T. F., & Ahmed, D. A. (2024). ChatGPT in education–Understanding the Bahraini academics perspective. Electronic Journal of E-Learning22(2), 112-134. https://doi.org/10.34190/ejel.22.2.3250
Al-Zahrani, A. M. (2024). From traditionalism to algorithms: Embracing artificial intelligence for effective university teaching and learning. IgMin Research2(2), 102-112. https://doi.org/10.61927/igmin151
Anh, N. T. M., Hoa, L. T. K., Thao, L. P., Nhi, D. A., Long, N. T., Truc, N. T., & Ngoc Xuan, V. (2024). The effect of technology readiness on adopting artificial intelligence in accounting and auditing in Vietnam. Journal of Risk and Financial Management17(1), Article 27. https://doi.org/10.3390/jrfm17010027
Awa, H. O., Ukoha, O. & Emecheta, B. C. (2016). Using T-O-E theoretical framework to study the adoption of ERP solution. Cogent Business and Management, Cogent, 3(1), 1-23.  https://doi.org/10.1080/23311975.2016.1196571
Becker, J. M., Rai, A., & Rigdon, E. (2013). Predictive validity and formative measurement in structural equation modeling: Embracing practical relevance. Research Methods and Philosophy: Thirty Fourth International Conference on Information Systems, Milan.
Begum, I. U. (2024). Role of artificial intelligence in higher education-an empirical investigation. International Research Journal on Advanced Engineering and Management (IRJAEM)2(03), 49-53. https://doi.org/10.47392/IRJAEM.2024.0009
binti Mohd Nazri, I. S., Rodzi, Z. M., binti Razali, I. N., Abdul Rahman, H., binti Abd Rahman, A., & Al-Sharqi, F. (2023, September). Unraveling the factors influencing the adoption of Artificial Intelligence (AI) in education. 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS) (pp. 186-193). IEEE.
Buabeng-Andoh, C., & Baah, C. (2020). Determinants of students’ actual use of the learning management system (LMS): An empirical analysis of a research model. Advances in Science, Technology and Engineering Systems Journal5(2), 614-620. https://doi.org/10.25046/aj050277
Buele, J., & Lerena-Aguirre, L. (2025, July). Transformations in academic work and faculty perceptions of artificial intelligence in higher education. Frontiers in Education.  10, Article 1603763. https://doi.org/10.3389/feduc.2025.1603763
Chang, T. S., & Bau, D. Y. (2026). Help me summarize a book: User continues to use intentions in AI reading assistants from a generative AI quality viewpoint. Library Hi Tech, 44(1), 148-170. https://doi.org/10.1108/LHT-03-2024-0158
Chatterjee, S., Rana, N. P., Khorana, S., Mikalef, P. & Sharma5, A. (2023). Assessing organizational users’ intentions and behavior to AI integrated CRM systems: A meta-UTAUT approach. Information Systems Frontiers, 25, 1299–1313. https://doi.org/10.1007/s10796-021-10181-1
Chin, W. W. (2010). How to write up and report PLS analyses. In V., Esposito Vinzi, W. Chin, J. Henseler, & H. Wang (Eds), Handbook of partial least squares: Concepts, methods and applications in marketing and related fields (pp. 655 – 690). Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_29
Dawson, J. F. (2014). Moderation in management research: What, why, when, and how. Journal of Business and Psychology, 29(1), 1–19. https://doi.org/10.1007/s10869-013-9308-7
Eden, C. A., Chisom, O. N., & Adeniyi, I. S. (2024). Integrating AI in education: Opportunities, challenges, and ethical considerations. Magna Scientia Advanced Research and Reviews10(2), 6-13. https://doi.org/10.30574/msarr.2024.10.2.0039
Esakkiammal, S., & Kasturi, K. (2024). Advancing educational outcomes with artificial intelligence: Challenges, opportunities, and future directions. International Journal of Computational and Experimental Science and Engineering10(4), 1749-1756. https://doi.org/10.22399/ijcesen.799
Familoni, B. T., & Onyebuchi, N. C. (2024). Advancements and challenges in AI integration for technical literacy: A systematic review. Engineering Science & Technology Journal5(4), 1415-1430. https://doi.org/10.51594/estj.v5i4.1042
Ferri, L., Maffei, M., Spano, R., & Zagaria, C. (2023). Uncovering risk professionals' intentions to use artificial intelligence: empirical evidence from the Italian setting. Management Decision. https://doi.org/10.1108/MD-02-2023-0178
Goertzel, B. (2014). Artificial general intelligence: Concept, state of the art, and future prospects. Journal of Artificial General Intelligence, 5(1), 1-46. https://doi.org/10.2478/jagi-2014-0001
Goodboy, A. K., & Martin, M. M. (2020). Omega over alpha for reliability estimation of unidimensional communication measures. Annals of the International Communication Association, 44(4), 422–439. https://doi.org/10.1080/23808985.2020.1846135
Guo, T., Wu, Q., & Cai, F. (2025). When privacy concerns drive AI adoption: A psychological perspective. Journal of Research in Interactive Marketing, 1-15. https://doi.org/10.1108/JRIM-02-2024-0122
Gusnan, Z. K., & Utomo, R. G. (2024). Factors affecting user’s acceptance of adopting biometrics technologies using the tam model. Jurnal Teknik Informatika (Jutif)5(4), 309-320. https://doi.org/10.52436/1.jutif.2024.5.4.2249
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Hair Jr, J. F., Gabriel, M., L. D. S., de Silva, D., & Junior, S. B. (2019). Development and validation of attitudes measurement scales: Fundamental and practical aspects. RAUSP Management Journal54(4), 490-507. https://doi.org/10.1108/RAUSP-05-2019-0098
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2024). Advanced issues in partial least squares structural equation modeling (PLS-SEM) (2nd ed.), Thousand Oaks, CA: Sage.
Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Howard, M. C., & Hair Jr, J. F. (2023). Integrating the expanded task-technology fit theory and the technology acceptance model: A multi-wave empirical analysis. AIS Transactions on Human-Computer Interaction15(1), 83-110. https://doi.org/10.17705/1thci.00184
Ifenthaler, D., Majumdar, R., Gorissen, P., Judge, M., Mishra, S., Raffaghelli, J., & Shimada, A. (2024). Artificial intelligence in education: Implications for policymakers, researchers, and practitioners. Technology, Knowledge and Learning29(4), 1693-1710. https://doi.org/10.1007/s10758-024-09747-0
Iqbal, M., Khan, N. U., & Imran, M. (2024). The role of artificial intelligence (AI) in transforming educational practices: Opportunities, challenges, and implications, Qlantic Journal of Social Sciences and Humanities, 5(2), 348 – 359. https://doi.org/10.55737/qjss.349319430
Ivezic, M. (March 3, 2017). The 1956 Dartmouth Workshop: The Birthplace of Artificial Intellicene (AI). PostQuantum.
Jaruwanakul, T. (2024). The influence of AI-CRM adoption and big data analytical capability on firm performance of large enterprises in Thailand, Global Business & Finance Review, 29(2), 112-126. https://doi.org/10.17549/gbfr.2024.29.2.112
Khuong, N. V., Anh, L. H. T., Ha, L. T. N., Cuong, L. V., Ngan, N. P. T., Thu, H. T. M., & Minh, L. K. (2023). Factors affecting decision to adopt artificial intelligence during COVID-19 pandemic period: Evidence from PLS-SEM and fsQCA. Vision: The Journal of Business Perspective. https://doi.org/10.1177/09722629221149847
Lee, S., Suk, J., Ha, H. R., Song, X. X., & Deng, Y. (2020, January). Consumer’s information privacy and security concerns and use of intelligent technology. In International Conference on Intelligent Human Systems Integration (pp. 1184-1189). Cham: Springer International Publishing.
Levy, P. S., & Lemeshow, S. (2013). Sampling of populations: Methods and applications. John Wiley & Sons.
Li, S., Zhang, H., & Du, Z. (2025a). Factors influencing college students’ willingness to use generative artificial intelligence tools-based on the UTAUT Model. 11th International Conference on Education and Training Technologies (ICETT), Macao, China, pp. 57-70, https://doi.org/10.1109/ICETT66247.2025.11136950.
Li, X., Song, Y., & Zhu, Z. (2025b). The impact of public privacy concerns on the acceptance of AI in Social Good Projects. Highlights in Science, Engineering and Technology131, 247-255. https://doi.org/10.54097/bfp6m518
Lin, T. C. & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: an integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410-417. https://doi.org/10.1016/j.im.2008.06.004
Lowry, P. B., & Gaskin, J. (2014). Partial Least Squares (PLS) Structural Equation Modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use It. IEEE Transactions on Professional Communication, 57(2), 123–146. https://doi.org/10.1109/TPC.2014.2312452
Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management34(1), 1-13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
Mauti, J. M., & Ayieko, D. S. O. (2025). Ethical implications of artificial intelligence in university education. East African Journal of Education Studies8(1), 159-167. https://doi.org/10.37284/eajes.8.1.2583
Mellat, N., Ebrahimi Qavam, S., Gholamali Lavasani, M., Moradi, M., & Sadipour, E. (2023). The role of cognitive, emotional, and spiritual development in adult psychological well-being. Journal of Spirituality in Mental Health, 25(1), 31-54. https://doi.org/10.1080/19349637.2022.2121239
Montoya, A. K. (2019). Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models. Behavior Research Methods, 51(1), 61–82. https://doi.org/10.3758/s13428-018-1088-6
Moradi, M., & Miralmasi, A. (2020). Pragmatic research method (1st ed.). Tehran: School of quantitative and qualitative research. Retrieved from: https://analysisacademy.com. [In Persian]
Nagy, A. S., Tumiwa, J. R., Arie, F. V., & Erdey, L. (2024). An exploratory study of artificial intelligence adoption in higher education. Cogent Education11(1), Article 2386892. https://doi.org/10.1080/2331186X.2024.2386892
Namoro, I. K. E. (2025). Fulfilling customer contentment: The impact of passenger’s preferences and characteristics on the use of catboats for booking and inquiries. International Journal for Research in Applied Science and Engineering Technology13(4), 3417-3439. https://doi.org/ 10.22214/ijraset.2025.68988
Neuman, W. L. (2014). Social research methods: Qualitative and quantitative approaches (7th ed.). Pearson.
Orlanda-Ventayen, C. C. (2024). Empowering education through transformative role of Artificial Intelligence (AI) in teaching and learning: Educators' perspective and research trends. 9th International Conference on Information Technology and Digital Applications (ICITDA), Nilai, Negeri Sembilan, Malaysia, 07-08 November 2024
Osei, H. V., Kwateng, K. O., & Boateng, K. A. (2022). Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic. Education and Information Technologies, 1-26. https:// doi.org/ 10. 1007/ s10639- 022- 11047-y
Patel, D., & Kore, S. A. (2020). Artificial intelligence: Future impacts, challenges and recommendations on healthcare services. International Journal of Community Medicine and Public Health7(4), 1596-1598. https://doi.org/10.18203/2394-6040.ijcmph20201480
Phua, J. T. K., Neo, H. F., & Teo, C. C. (2025). Evaluating the impact of artificial intelligence tools on enhancing student academic performance: Efficacy Amidst security and privacy concerns. Big Data and Cognitive Computing9(5), Article 131. https://doi.org/10.3390/bdcc9050131
Pillai, R., & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An international Journal27(9), 2599-2629. https://doi.org/10.1108/BIJ-04-2020-0186
Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4. SmartPLS. https://www.smartpls.com
Sabiteka, M., Yu, X., & Sun, C. (2025). A model for educational technology adoption in developing countries. https://doi.org/10.20944/preprints202503. 0603. v1
Samimi, R., & Okazaki, T. (2025). Sequential Bayesian SEM for task technology fit. International Journal of Advanced Research in Computer Science16(1), 1-5. https://doi.org/10.26483/ijarcs.v16i1.7176
Shakhina, I. Y., & Podzygun, O. A. (2025). Integration of artificial intelligence technologies in education: Challenges and prospects. Modern Information Technologies and Innovation Methodologies of Education in Professional Training Methodology Theory Experience Problems, 75, 161-172. https://doi.org/10.31652/2412-1142-2025-75-161-172
Sivathanu, B. (2019). Adoption of industrial IoT (IIoT) in auto-component manufacturing SMEs in India, Information Resources Management Journal (IRMJ), IGI Global Scientific Publishing, 32(2), 52-75.
Spies, R., Grobbelaar, S., & Botha, A. (2020, April). A scoping review of the application of the task-technology fit theory. Responsible Design, Implementation and Use of Information and Communication Technology, Article 12066. https://doi.org/10.1007/978-3-030-44999-5_33
Ștefan, S. C., Olariu, A. A., & Popa, Ș. C. (2024). Implications of artificial intelligence on organizational agility: A PLS-SEM and PLS-POS Approach. Amfiteatru Economic, 26(66), 403-420. https://doi.org/10.24818/EA/2024/66/403
Tang, T. C., Chi, L. C., & Tang, E. (2025). Effect of AI Technology Acceptance and Use on Behavioral Intentions and Career Adaptability. Environment-Behavior Proceedings Journal10(SI27), 195-200. https://doi.org/10.21834/e-bpj.v10iSI27.6835
Tarka, P. (2018). An overview of structural equation modeling: Its beginnings, historical development, usefulness and controversies in the social sciences. Quality and Quantity, 52(1), 313–354. https://doi.org/10.1007/s11135-017-0469-8
Tovar, I. Z., & Gutiérrez Ocegueda, G. J. R. (2025). Attitudes of university professors towards the use of artificial intelligence in teaching and learning. International Journal of Multidisciplinary Research and Analysis, 8(1), 364-387. https://doi.org/10.47191/ijmra/v8-i01-46
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Virdyananto, A. L., Dewi, M. A. A., Hidayanto, A. N., & Hanief, S. (2017). User acceptance of human resource information system: An integration model of unified theory of acceptance and use of technology (UTAUT), task technology fit (TTF), and symbolic adoption. International Conference on Information Technology Systems and Innovation, ICITSI 2016–Proceedings. https://doi.org/10.1109/ICITSI.2016.7858227
Wang, S., Wang, F., Zhu, Z., Wang, J., Tean. T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review.  Expert Systems with Application, 252, Part A. Article 124167. https://doi.org/10.1016/j.eswa.2024.124167
Yadegaridehkordi, E., Nilashi, M., Shuib, L., Hairul Nizam bin Md Nasir, M., Asadi, S., Samad, S., & Awang, N. F. (2020). The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications, 40, Article 100921. https://doi.org/10.1016/j.elerap.2019.100921
Zafar, H. (2013). Human resource information systems: Information security concerns for organizations, Human Resource Management Review, 23(1), 105-113. https://doi.org/10.1016/j.hrmr.2012.06.010
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Zhong, H. X., Chang, J. H., Lai, C. F., Chen, P. W., Ku, S. H., & Chen, S. Y. (2024). Information undergraduate and non‑information undergraduate on an artificial intelligence learning platform: An artificial intelligence assessment model using PLS‑SEM analysis, Education and Information Technologies, 29, 4371–4400. https://doi.org/10.1007/s10639-023-11961-9
Zhu, K., Dong, S., Xu, S. X., & Kraemer, K. L. (2006). Innovation diffusion in global contexts: Determinants of post-adoption digital transformation of European companies, European Journal of Information Systems, 15(6), 601-616. https://doi.org/10.1057/palgrave.ejis.3000650
  • تاریخ دریافت: 12 آبان 1404
  • تاریخ بازنگری: 27 بهمن 1404
  • تاریخ پذیرش: 24 فروردین 1405
  • تاریخ اولین انتشار: 24 فروردین 1405
  • تاریخ انتشار: 01 فروردین 1405