Document Type : Original Article

Author

Assistant Professor, Department of Educational Administration, Farhangian University, Tehran, Iran

Abstract

The rapid and widespread advancement of artificial intelligence technology in the 21st century has led to the emergence of the concept of AI literacy.The present study purposed to explain the role of artificial intelligence literacy in enhancing the higher-order thinking skills of pre-service teachers, mediated by behavioral engagement and peer interaction, and was conducted using a descriptive correlational method.  The statistical population of the study  included  all male and female pre-service teachers at Farhangian University in Chaharmahal and Bakhtiari Province, totaling 2003 individuals, from whom 322 individuals were selected using the stratified random sampling method based on the Krejcie and Morgan table. Data were collected using four questionnaires: Artificial Intelligence Literacy (Wang et al., 2023), Behavioral Engagement (Lu et al., 2024), Peer Interaction (Hwang et al., 2018), and Higher-Order Thinking Skills (Hwang et al., 2018).  Content validity of the questionnaires was assessed and confirmed using the Content Validity Ratio (CVR) and the Content Validity Index (CVI). Convergent and discriminant validity of the questionnaires were evaluated and confirmed using the Average Variance Extracted (AVE) and the Fornell-Larcker test, respectively. Reliability of the questionnaires was also examined using Cronbach's alpha and composite reliability, with all values obtained being above 0.70.  Data analysis was conducted using structural equation modeling (SEM) techniques  and SPSS and Amos software. The research findings indicated that artificial intelligence literacy has a positive and significant effect on the higher-order thinking skills of pre-service teachers, both directly and indirectly (through behavioral engagement and peer interaction).  Accordingly, serious planning and appropriate investment to develop artificial intelligence literacy in all pre-service teachers appear to be important and necessary.

Keywords

پهلوان شریف، سعید و شریف‌نیا، سید حمید. (1403). تحلیل عامل و مدل‌سازی معادلات ساختاری با نرم‌افزار SPSS و AMOS. تهران: انتشارات جامعه‌نگر.
حاجی‌انوری، لادن و رمضانی، عباس. (1403). بررسی وضعیت سواد، کاربست و عوامل‌ مؤثر بر پذیرش هوش مصنوعی در بین اعضای هیأت علمی. نامه آموزش عالی، 17 (68)، 131ـ106. DOI: 10.22034/hel.2024.2036769.1985
عبدالهی، عباس و طاهری، آزاده. (1398). مدل‌سازی معادله‌های ساختاری به کمک نرم‌افزار AMOS. تهران: سازمان جهاد دانشگاهی تهران.
کرسول، جان.دبلیو. (1394). طرح پژوهش (رویکردهای کمّی، کیفی و شیوة ترکیبی). ترجمة حسن دانایی‌فرد و علی صالحی. تهران: مؤسسه کتاب مهربان نشر.
مطلبی‌نژاد، علیرضا؛ فاضلی، فرزانه و نوائی، الهام. (1402). بررسی نظام‌مند نویدها و چالش‌های هوش مصنوعی برای معلمان، فناوری و دانش‌پژوهش در تعلیم و تربیت، 3 (1)، 23-44.  https://doi.org/10.30473/t-edu.2023.68819.1101
Asio, J. M. R. (2024). AI literacy, self-efficacy, and self-competence among college students: variances and interrelationships among variables. MOJES: Malaysian Online Journal of Educational Sciences12(3), 44-60.‏ https://doi.org/10.22452/aldad.
Avcı, Ü & Ergün, E. (2022). Online students’ LMS activities and their effect on engagement, information literacy and academic performance. Interactive Learning Environments30(1), 71-84.‏ https://doi.org/10.1080/10494820.2019.1636088
Ayanwale, M. A. Adelana, O. P. Molefi, R. R. Adeeko, O & Ishola, A. M. (2024). Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and education open6, 100179.‏ https://doi.org/10.1016/j.caeo.2024.100179
Bajaj, R & Sharma, V. (2018). Smart Education with artificial intelligence based determination of learning styles. Procedia computer science132, 834-842.‏ https://doi.org/10.1016/j.procs.2018.05.095
Bergdahl, N. Nouri, J & Fors, U. (2020). Disengagement, engagement and digital skills in technologyenhanced learning. Education and Information Technologies, 25(2), 957–983. https://doi.org/10.1007/s10639-019-09998-w.
 
Bewersdorff, A. Hornberger, M. Nerdel, C & Schiff, D. S. (2025). AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students' AI self-efficacy. Computers and Education: Artificial Intelligence8, 100340.‏ https://doi.org/10.1016/j.caeai.2024.100340
Celebi, C. Yılmaz, F. Demir, U & Karakus, F. (2023). Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning4(2), 291-306.‏ https://doi.org/10.52911/itall.1401740
Cheung, P. C & Lau, S. (2013). A tale of two generations: Creativity growth and gender differences over a period of education and curriculum reforms. Creativity Research Journal25(4), 463-471.‏ https://doi.org/10.1080/10400419.2013.843916
Choi, S. Jang, Y & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction39(4), 910-922. ‏ https://doi.org/10.1080/10447318.2022.2049145
Christudason, A. (n. d.). What is peer interaction/learning. Retrieved January 3, 2025, from What is Peer Interaction/Learning | IGI Global Scientific Publishing
Collins, R. (2014). Skills for the 21st Century: teaching higher-order thinking. Curriculum  & Leadership Journal12(14), 1-8.‏
Delcker, J. Heil, J. Ifenthaler, D. Seufert, S & Spirgi, L. (2024). First-year students AI-competence as a predictor for intended and de facto use of AI-tools for supporting learning processes in higher education. International Journal of Educational Technology in Higher Education21(1), 18.‏ https://doi.org/10.1186/s41239-024-00452-7
Deranty, J. P & Corbin, T. (2024). Artificial intelligence and work: a critical review of recent research from the social sciences. Ai  & Society39(2), 675-691.‏ https://doi.org/10.1007/s00146-022-01496-x
Di, W. Danxia, X & Chun, L. (2019). The effects of learner factors on higher-order thinking in the smart classroom environment. Journal of Computers in Education6(4), 483-498.‏ https://doi.org/10.1007/s40692-019-00146-4
Elfeky, A. I. M. (2019). The effect of personal learning environments on participants’ higher order thinking skills and satisfaction. Innovations in Education and Teaching International56(4), 505–516. https://doi.org/10.1080/14703297.2018.1534601
Fornell, C & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.2307/3150980
Fosnacht, K. (2020). Information literacy ’ s influence on undergraduates’ learning and development: Results from a large multi-institutional study. College  & Research Libraries, 18(2), 272–287. https://doi.org/10.5860/crl.81.2.272
Frazier, K & Reynolds, E. (2012). Power up your creative mind. Pieces of Learning.‏
Fredricks, J. A. Blumenfeld, P. C & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of educational research74(1), 59-109.‏ https://doi.org/10.3102/00346543074001059
Greene, J. O & Burleson, B. R. (Eds.). (2003). Handbook of communication and social interaction skills. UK, LONDON: Routledge.‏
Guo, F. Yao, M. Wang, C. Yan, W & Zong, X. (2016). The effects of service learning on student problem solving: The mediating role of classroom engagement. Teaching of Psychology43(1), 16-21.‏  https://doi.org/10.1177/0098628315620064
Holmes, W. Bialik, M & Fadel, C. (2023). Artificial Intelligence in Education. Globethics Publications, 621-653.
https://doi.org/10.58863/20.500.12424/4276068
Hoya, F. Mah, D. K. Prilop, C. N. Jacobsen, L. J & Weber, K. E. (2024). Pre-service teachers’ AI usage: The effects of perceived usefulness, subjective norm, behavioral intention, and self-efficacy.‏ https://doi.org/10.31219/osf.io/284gk
Huang, J. Saleh, S & Liu, Y. (2021). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies10(3),‏   206-217. https://doi.org/10.36941/ajis-2021-0077
Huang, Y. M. Silitonga, L. M & Wu, T. T. (2022). Applying a business simulation game in a flipped classroom to enhance engagement, learning achievement, and higher-order thinking skills. Computers  & Education183, 104494.‏ https://doi.org/10.1016/j.compedu.2022.104494
Hwang, G. J & Lai, C. L. (2017). Facilitating and bridging out-of-class and in-class learning: An interactive e-book-based flipped learning approach for math courses. Journal of Educational Technology  & Society20(1), 184-197.‏
Hwang, G. J. Lai, C. L. Liang, J. C. Chu, H. C & Tsai, C. C. (2018). A long-term experiment to investigate the relationships between high school students’ perceptions of mobile learning and peer interaction and higher-order thinking tendencies. Educational Technology Research and Development66, 75-93.‏ https://doi.org/10.1007/s11423-017-9540-3
Igbokwe, I. C. (2023). Application of artificial intelligence (AI) in educational management. International Journal of Scientific and Research Publications13(3), 300-307.‏ DOI: http://dx.doi.org/10.29322/IJSRP.13.03.2023.p13536
Jones, A. C. Scanlon, E & Clough, G. (2013). Mobile learning: Two case studies of supporting inquiry learning in informal and semiformal settings. Computers  & Education, 61, 21–32. https://doi.org/10.1016/j.compedu.2012.08.008
Kim, H. J. Yi, P & Hong, J. I. (2020). Students’ academic use of mobile technology and higher-order thinking skills: The role of active engagement. Education Sciences10(3), 47.‏ https://doi.org/10.3390/educsci10030047
Kong, S. C. (2014). Developing information literacy and critical thinking skills through domain knowledge learning in digital classrooms: An experience of practicing flipped classroom strategy. Computers  & Education, 78, 160–173. https://doi.org/10.1016/j.compedu.2014.05.009
Kong, S. C. Chan, T. W. Griffin, P. Hoppe, U. Huang, R. Kinshuk & Yu, S. (2014). E-learning in school education in the coming 10 years for developing 21st century skills: Critical research issues and policy implications. Journal of Educational Technology  & Society17(1), 70-78.‏ https://www.jstor.org/stable/jeductechsoci.17.1.70
Kong, S. C. Cheung, W. M. Y & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence2, 100026.‏ https://doi.org/10.1016/j.caeai.2021.100026
Lai, C. L & Hwang, G. J. (2014). Effects of mobile learning time on students' conception of collaboration, communication, complex problem–solving, meta–cognitive awareness and creativity. International Journal of Mobile Learning and Organisation8(3-4), 276-291.‏ https://doi.org/10.1504/IJMLO.2014.067029
Laupichler, M. C. Aster, A. Schirch, J & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence3, 100101.‏ https://doi.org/10.1016/j.caeai.2022.100101
Lein, A. E. Jitendra, A. K. Starosta, K. M. Dupuis, D. N. Hughes-Reid, C. L & Star, J. R. (2016). Assessing the relation between seventh-grade students’ engagement and mathematical problem solving performance. Preventing School Failure: Alternative Education for Children and Youth60(2), 117-123.‏ https://doi.org/10.1080/1045988X.2015.1036392
Li, H. Zhu, S. Wu, D. Yang, H. H & Guo, Q. (2023). Impact of information literacy, self-directed learning skills, and academic emotions on high school students’ online learning engagement: A structural equation modeling analysis. Education and information technologies28(10), 13485-13504.‏ https://doi.org/10.1007/s10639-023-11760-2
Long, D & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-16).‏
Lu, K. Pang, F & Shadiev, R. (2021b). Understanding the mediating effect of learning approach between learning factors and higher order thinking skills in collaborative inquiry-based learning. Educational Technology Research and Development69(5), 2475-2492.‏ https://doi.org/10.1007/s11423-021-10025-4
Lu, K. Yang, H. H. Shi, Y & Wang, X. (2021a). Examining the key influencing factors on college students’ higher-order thinking skills in the smart classroom environment. International Journal of Educational Technology in Higher Education18, 1-13.‏ https://doi.org/10.1186/s41239-020-00238-7
Lu, K. Zhu, J. Pang, F & Shadiev, R. (2024). Understanding the relationship between colleges students’ artificial intelligence literacy and higher order thinking skills using the 3P model: the mediating roles of behavioral engagement and peer interaction. Educational technology research and development, 1-24.‏
Martin, A. (2006). A European framework for digital literacy. Nordic Journal of Digital Literacy1(2), 151-161.‏ https://doi.org/10.18261/ISSN1891-943X-2006-02-06
Muthmainnah, Ibna Seraj, P. M & Oteir, I. (2022). Playing with AI to Investigate Human‐Computer Interaction Technology and Improving Critical Thinking Skills to Pursue 21st Century Age. Education Research International2022(1), 6468995.‏ https://doi.org/10.1155/2022/6468995
Ng, D. T. K. Leung, J. K. L. Chu, S. K. W & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence2, 100041.‏ https://doi.org/10.1016/j.caeai.2021.100041
Ng, D. T. K. Leung, J. K. L. Su, M. J. Yim, I. H. Y. Qiao, M. S & Chu, S. K. W. (2022). AI literacy in K-16 classrooms.  Switzerland: Springer.‏
Ng, D. T. K. Su, J. Leung, J. K. L & Chu, S. K. W. (2024). Artificial intelligence (AI) literacy education in secondary schools: a review. Interactive Learning Environments32(10), 6204-6224.‏ https://doi.org/10.1080/10494820.2023.2255228
Nguyen, A. Kremantzis, M. Essien, A. Petrounias, I & Hosseini, S. (2024). Enhancing student engagement through artificial intelligence (AI): Understanding the basics, opportunities, and challenges. Journal of University Teaching and Learning Practice21(6), 1-13.‏ https://orcid.org/0000-0002-0759-9656
Nguyen, T. D. Cannata, M & Miller, J. (2018). Understanding student behavioral engagement: Importance of student interaction with peers and teachers. The journal of educational research111(2), 163-174.‏ https://psycnet.apa.org/doi/10.1080/00220671.2016.1220359
Osasona, F. Amoo, O. O. Atadoga, A. Abrahams, T. O. Farayola, O. A & Ayinla, B. S. (2024). Reviewing the ethical implications of AI in decision making processes. International Journal of Management  & Entrepreneurship Research6(2), 322-335.‏ https://doi.org/10.51594/ijmer.v6i2.773
Polatgil, M & Guler, A. (2023). Adaptation of artificial intelligence literacy scale into Turkish. Sosyal Bilimlerde Nicel Araştırmalar Dergisi3(2), 99-114.
Prior, D. D. Mazanov, J. Meacheam, D. Heaslip, G & Hanson, J. (2016). Attitude, digital literacy and self efficacy: Flow-on effects for online learning behavior. The Internet and Higher Education29, 91-97.‏ http://dx.doi.org/10.1016/j.iheduc.2016.01.001
Promma, W. Imjai, N. Usman, B & Aujirapongpan, S. (2025). The influence of AI literacy on complex problem-solving skills through systematic thinking skills and intuition thinking skills: An empirical study in Thai gen Z accounting students. Computers and Education: Artificial Intelligence, 100382.‏ https://doi.org/10.1016/j.caeai.2025.100382
Ramos, J. L. S. Dolipas, B. B & Villamor, B. B. (2013). Higher order thinking skills and academic performance in physics of college students: A regression analysis. International Journal of Innovative Interdisciplinary Research4(1), 48-60.‏ https://www.researchgate.net/publication/333506487_Higher_Order_Thinking_Skills_and_Academic_Performance_in_Physics_of_College_Students_A_Regression_Analysis
Robert, A. Potter, K & Frank, L. (2024). The impact of artificial intelligence on students' learning experience. Wiley Interdisciplinary Reviews: Computational Statistics2(01), 1-16. http://dx.doi.org/10.2139/ssrn.4716747
Rus-Casas, C. Eliche-Quesada, D. Aguilar-Peña, J. D., Jiménez-Castillo, G & La Rubia, M. D. (2020). The impact of the entrepreneurship promotion programs and the social networks on the sustainability entrepreneurial motivation of engineering students. Sustainability12(12), 4935.‏ https://doi.org/10.3390/su12124935
Southworth, J. Migliaccio, K. Glover, J. Glover, J. N. Reed, D. McCarty, C. & Thomas, A. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence4, 100127.‏ https://doi.org/10.1016/j.caeai.2023.100127
Stancu, M. S & Dutescu, A. (2021). The impact of the Artificial Intelligence on the accounting profession, a literature’s assessment. In Proceedings of the International Conference on Business Excellence (Vol. 15, No. 1, pp. 749-758). Sciendo.‏ http://dx.doi.org/10.2478/picbe-2021-0070
Tugtekin, E. B & Koc, M. (2020). Understanding the relationship between new media literacy, communication skills, and democratic tendency: Model development and testing. New media  & society22(10), 1922-1941.‏ https://doi.org/10.1177/1461444819887705
Turgut, M. Kutlu, G & Mut, S. (2018). Determination of the relationship between communication skills and social media use of health management department students. The Journal of Business Science6(1), 185-205.‏ https://doi.org/10.22139/jobs.361049
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 Sciences13(10), 998.‏ https://doi.org/10.3390/educsci13100998
Wang, B. Rau, P. L. P & Yuan, T. (2023). Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour  & information technology42(9), 1324-1337.‏ https://doi.org/10.1080/0144929X.2022.2072768
Yao, N & Wang, Q. (2024). Factors influencing pre-service special education teachers’ intention toward AI in education: Digital literacy, teacher self-efficacy, perceived ease of use, and perceived usefulness. Heliyon10(14), 1-13. ‏ https://doi.org/10.1016/j.heliyon.2024.e34894
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. Sustainability14(21), 14549.‏ https://doi.org/10.3390/su142114549