قلق الذكاء الاصطناعي لدى طلبة الجامعة

reference

• أبو جادو، صالح محمد، ونوفل، محمد بكر. (2007). تعليم التفكير: النظرية والتطبيق. دار المسيرة للنشر والتوزيع والطباعة.

• جرار، أماني. (2023، 25 أغسطس). نحو رؤية شاملة لإعادة تصور التعليم العالي. عرض مقدم في مؤتمر ويزر، جامعة البتراء، عمان، الأردن.

• القريشي، ماهر حبيب عبيد علي (2025): اثر استخدام الذكاء الاصطناعي في تطوير مؤسسات المجتمع المدني، مجلة لاراك، المجلد 17، العدد:1، الجزء 1.

• محـمود، عبد الرازق. (2020). تطبيقات الذكاء الاصطناعي مدخـل للتطوير التعليم في قلب تحديات جائحة فيروس كورونا (COVID-19). مجلة دراسات تربوية واجتماعية، 143(244)، 171–143.

• Acemoglu, D., & Restrepo, P. (2017, April 10). Robots and jobs: Evidence from US labor markets. Voxeu. https://voxeu.org/article/robots-and-jobs-evidence-us.

• Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Thabit, S., El-Qirem, F. A., Lutfi, A., Alrawad, M., Al Mulhem, A., Alkhdour, T., Awad, A. B., & Al-Maroof, R. S. (2022). Examining the impact of artificial intelligence and social and computer anxiety in E-learning settings: Students' perceptions at the university level. Electronics, 11(22), 3662. https://doi.org/10.3390/electronics11223662.

• Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26, 600–614.

• Beel, J., Genzmehr, M., Langer, S., Nürnberger, A., & Gipp, B. (2013). A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation. In Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation.

• Bossman, J. (2016, October 21). Top 9 ethical issues in artificial intelligence. World Economic Forum. https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence.

• Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.

• Bowlby, J. (1973). Attachment and loss: Vol. II. Separation, anxiety and anger. Basic Books.

• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

• Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.

• Bull, S., & Kay, J. (2016). SMILI☺: A framework for interfaces to learning data in open learner models, learning analytics, and related fields. International Journal of Artificial Intelligence in Education, 26, 293–331.

• Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.

• Chen, L., Chen, P., & Lim, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278.

• Circnamaru, A. (2022). Futureproofing EU law: The case of algorithmic discrimination [Unpublished master's thesis]. University of Oxford.

• Eysenck, H. J. (1957). The dynamics of anxiety and hysteria. Praeger.

• Fitzpatrick, K. K., Darcy, A., & Vierhile, M. J. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785.

• Freud, S. (1975). Introductory lectures on psychoanalysis (Original work published 1917). In The standard edition of the complete psychological works of Sigmund Freud. London: Hogarth Press.

• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.

• Future of Life Institute (FLI). (2015). Autonomous weapons: An open letter from AI & robotics researchers. Retrieved from http://futureoflife.org/open-letter-autonomous-weapons/.

• Gansser, O. A., & Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535. https://doi.org/10.1016/j.techsoc.2021.101535.

• Gillespie, N., Lockey, S., & Curtis, C. (2021). Trust in artificial intelligence: A five-country study. The University of Queensland and KPMG Australia. https://doi.org/10.14264/e34bfa3.

• Hartwig. (2021, January 8). Benefits of artificial intelligence. Hackr.io. Retrieved January 18, 2022, from https://hackr.io/blog/benefits-of-artificial-intelligence.

• Johnson, D. G., & Verdicchio, M. (2017). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267–2270. https://doi.org/10.1002/asi.23867.

• Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişenoy, O., & Kaya, M. D. (2022). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2022.2151730.

• Kim, J., Nakashima, M., Fan, W., Wuthier, S., Zhou, X., Kim, I., et al. (2022). Machine learning approach to anomaly detection based on traffic monitor for secure blockchain networking. Sensors, 19(3), 3619–3632.

• Kochanski, K., Rolnick, D., Donti, P., & Kaack, L. (2019). Climate change + AI: Tackling climate change with machine learning. In AGU Fall Meeting Abstracts.

• Korshunov, P., & Marcel, S. (2018). Deepfakes: A new threat to face recognition? arXiv preprint arXiv:1812.08685.

• Kwak, Y., Ahn, J., & Seo, Y. H. (2022). Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students' behavioral intentions. BMC Nursing, 21(1), 267. https://doi.org/10.1186/s12912-022-01048-0.

• Lemay, D. L., Bastet, R. B., & Doleck, T. (2020). Fearing the robot apocalypse: Correlates of AI anxiety. International Journal of Learning Analytics and Artificial Intelligence for Education (IJAI), 2(2), 24. https://doi.org/10.3991/ijai.v2i2.16759

• Li, S., Wang, C., & Gu, X. (2022). Foresee the future of learning: Framework development and practical approach of artificial intelligence learning readiness. Ch.ina Educational Technology, 1(10), 79–88, 96.

• Li, J., & Huang, I. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 61, 101410. http://doi.org/10.1016/j.techsoc.2020.101410.

• Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017). A future that works: Automation, employment, and productivity. McKinsey Global Institute.

• Mowrer, O. H. (1953). A stimulus-response analysis of anxiety and its role as a reinforcing agent. In L. M. Stolurow (Ed.), Readings in learning. Englewood Cliffs, NJ: Prentice Hall.

• Ohman, A. (1993). Fear and anxiety as emotional phenomena: Clinical phenomenology, evolutionary perspectives, and information-processing mechanisms. In M. Lewis & J. M. Haviland (Eds.), Handbook of the emotions . New York & London: The Guilford Press.

• Philip, A. K., & Faiyazuddin, M. (2023). An overview of artificial intelligence in drug development. Asian Journal of Health and Allied Sciences in Drug Development, 1–8.

• Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.

• Rassool, G. H. (1993). Nursing and substance misuse: responding to the challenge. Journal of Advanced Nursing, 18(9), 1401–1407.

• Reinhart, R. J. (2018, March 6). Most Americans already using artificial intelligence products. Gallup. https://perma.cc/RVY5-WP9W.

• Rhee, C. S., & Rhee, H. (2019). Expectations and anxieties affecting attitudes toward artificial intelligence revolution. Journal of the Korea Contents Association, 19(9), 17–46. https://doi.org/10.302/J.KCA.2019.19.09.037

• Sajay, S. (2020). Algorithms of oppression: How search engines reinforce racism. Ethnic and Racial Studies, 43(3), 592–594. https://doi.org/10.1080/01419870.2019.1635260.

• Schiavone, G., Businaro, S., & Zancanaro, M. (2024). Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial intelligence. Technology in Society, 77, 102537. https://doi.org/10.1016/j.techsoc.2024.102537.

• Shan, C., Wang, J., & Zhu, Y. (2023). The evolution of artificial intelligence in the digital economy: An application of the potential Dirichlet allocation model. Sustainability, 13(2), 1360.

• Siemens, G., & Baker, R. S. D. (Eds.). (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge.

• Strongman, K. T. (1995). Theories of anxiety. University of Canterbury.

• Timms, M. J. (2016). Letting artificial intelligence in education out of the box: Educational cobots and smart classrooms. International Journal of Artificial Intelligence in Education, 26(2), 701–712.

• Turgut, A. M. (2009). Computing machinery and intelligence. In Parsing the Turing Test (pp. 23–65). Springer Dordrecht.

• Wang, Y. M., Wei, C. L., Lin, H. H., Wang, S. C., & Wang, Y. S. (2022). What drives students’ AI learning behavior: A perspective of AI anxiety. Interactive Learning Environments, 1–17.

• Wang, Y. Y., & Wang, Y. S. (2019). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 1–16.