PERCEIVED COGNITIVE SKILLS AND THEIR RELATIONSHIP TO THE USE OF ARTIFICIAL INTELLIGENCE TOOLS IN RESEARCH AND SCHOLARLY ACTIVITY AMONG TEACHER-RESEARCHERS IN THE SALAH AL-DIN DIRECTORATE OF EDUCATION

Ahmed Mahmood Tuama

Авторы

Ahmed Mahmood Tuama Ministry of Education, General Directorate of Salah al-Din Education, Salah Al-Din, Iraq

Ключевые слова:

Development Reality, Modern Methods, Guidance Work, Nineveh Governorate

Аннотация

The study was aimed at determining the status and level of use and development of modern digital tools in agricultural extension in the Nineveh Governorate, such as agricultural extension content, digital interaction and participation, digital extension, modern communication technologies, smartphones and agricultural applications, multimedia, and agricultural videos. It also explored the link between current use and development of modern tools and selected variables at personal and professional level. It was administered using two questionnaires. The first section gathered personal and professional information, and the second section had 60 items in six sections, each containing 10 items. The questionnaire had a Cronbach alpha reliability coefficient of 0.95 and showed face validity. The study was carried out on 256 extension workers in the Nineveh Agriculture Directorate and its agricultural divisions (32). A random sample of 200 extension workers (about 78% of the population) was drawn. Data were analysed in SPSS. The findings showed moderate general level of development and modern tools used in agricultural extension work, leaning towards high. The use of modern communication tools was high, while the use of smartphones and agricultural applications was moderate, tending toward high. Digital platforms and the use of multimedia was moderate and tending to low, while digital interaction and participation was high. Digital extension content development was moderate, leaning to high. There was no significant relationship with education level, specialization, years of service or participation in training courses relating to digital technologies. Age and years of service in agricultural extension were found to be significantly correlated at 0.05 level and Internet use frequency at 0.01 level, while owning a smart phone and the most frequently used digital tool were significantly correlated at 0.05 level. The study suggests aligning digital tools and resources to farmers' needs and capacities, and building partnerships between the Nineveh Agriculture Directorate, universities, and research centers to create relevant digital platforms and tools. Extension workers' perspectives regarding barriers to the utilization of modern digital technologies in agricultural extension should be explored for further research.

Библиографические ссылки

Al-Badawi Balla, A. A., & Al-Mustafa, M. M. A. (2025). Faculty members' attitudes toward using artificial intelligence applications in scientific research: A field study at the University of Al-Butana, Sudan [in Arabic]. Al-Butana Journal of Educational Sciences, (18), 25–54.

Al-Kubaisi, S. (2005). Knowledge management [in Arabic]. Arab Administrative Development Organization.

English-language sources

Al-Bukhrani, M. A., Alrefaee, Y. M. H., & Tawfik, M. (2025). Adoption of AI writing tools among academic researchers: A Theory of Reasoned Action approach. PLOS ONE, 20(1), e0313837. https://doi.org/10.1371/journal.pone.0313837

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp. 89–195). Academic Press.

Baddeley, A. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Sciences, 4(11), 417–423. https://doi.org/10.1016/S1364-6613(00)01538-2

Billing, D. (2007). Teaching for transfer of core/key skills in higher education: Cognitive skills. Higher Education, 53(4), 483–516. https://doi.org/10.1007/s10734-005-5628-5

Broadbent, D. E. (1958). Perception and communication. Pergamon Press.

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

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., … Wright, R. (2023). "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

El-Ashry, A. M., Al Saleh, N. S., AlOtaibi, N. G., Almutairi, T. Z., Sallam, L. A., Alnassar, M. M., Alshehri, K. A., Al-Ghareeb, S. A., Al-Otaibi, R. A., Almutairi, W. M., Alkubati, S. A., & El-Sayed, M. M. (2025). The impact of artificial intelligence attitudes and acceptance on critical thinking motivation among nursing students in Saudi Arabia. SAGE Open Nursing, 11. https://doi.org/10.1177/23779608251369564

El Hosayny, O., Razkane, H., & Yeou, M. (2025). Extended TAM-based acceptance of AI for supporting critical thinking in academic research: An examination of perceived benefits and challenges. Discover Artificial Intelligence, 5, 167. https://doi.org/10.1007/s44163-025-00390-x

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Igolkina, A. A., & Meshcheryakov, G. (2020). semopy: A Python package for structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 27(6), 952–963. https://doi.org/10.1080/10705511.2019.1704289

Kahneman, D. (1973). Attention and effort. Prentice-Hall.

Kim, Y. K., Edens, D., Iorio, M. F., Curtis, C. J., & Romero, E. (2015). Cognitive skills development among international students at research universities in the United States. Journal of International Students, 5(4), 526–540. https://doi.org/10.32674/jis.v5i4.413

Kleine, A.-K., Kokje, E., Lermer, E., & Gaube, S. (2023). Attitudes toward the adoption of 2 artificial intelligence–enabled mental health tools among prospective psychotherapists: Cross-sectional study. JMIR Human Factors, 10, e46859. https://doi.org/10.2196/46859

Mayer, R. E., & Wittrock, M. C. (2006). Problem solving. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 287–303). Lawrence Erlbaum Associates.

McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.

Neisser, U. (1967). Cognitive psychology. Appleton-Century-Crofts.

Pauleen, D. J., & Gorman, G. E. (Eds.). (2011). Personal knowledge management: Individual, organizational and social perspectives. Gower.

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002

Şimşek, A., Tepetaş Cengiz, G. Ş., & Bal, M. (2025). Extending the TAM framework: Exploring learning motivation and agility in educational adoption of generative AI. Education and Information Technologies, 30(15), 20913–20942. https://doi.org/10.1007/s10639-025-13591-9

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Uludağ, F., Kılıç, E., & Çelik, H. E. (2025). Artificial intelligence, social influence, and AI anxiety: Analyzing the intentions of science doctoral students to use ChatGPT with PLS-SEM. Humanities and Social Sciences Communications, 12, 1308. https://doi.org/10.1057/s41599-025-05641-x

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Wickens, C. D. (2008). Multiple resources and mental workload. Human Factors, 50(3), 449–455. https://doi.org/10.1518/001872008X288394

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, 39. https://doi.org/10.1186/s41239-019-0171-0

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7