Academics’ Attitudes toward AI Challenges in Education: Tradition vs Innovation
DOI:
https://doi.org/10.57125/FP.2023.09.30.03Keywords:
artificial intelligence in education, teaching methods comparison, data privacy concerns, personalized learningAbstract
The capacity of artificial intelligence to continuously learn and improve suggests a future expansion in its application. This study aimed to identify differences in scientists' attitudes toward traditional and innovative teaching methods. A questionnaire comprising 15 questions was developed, covering demographic data, experience with artificial intelligence, and attitudes toward its use. The survey included 240 teachers from higher educational institutions in Ukraine, representing various ages and genders. The study found that 24.4% of respondents were in the 35-44 age group, while the 18-24 and over 65 age groups were the least represented. Additionally, 32.1% of respondents had 7 to 10 years of work experience, and 12.2% had less than 1 year. Respondents included 32.3% from technical specialties and 9.1% from artistic disciplines. Regarding artificial intelligence in education, 44.4% of respondents were neutral, and 1.1% were strongly negative. In response to whether they had used AI in their studies or work, 78% answered affirmatively, while 18.4% had not used it at all. Furthermore, 38.3% viewed the personalization of learning positively, but 54.5% highlighted potential risks concerning data protection, 43.1% indicated that AI implementation relies on technical capabilities, and 24.4% noted that AI might lead to job loss for teachers. The use of artificial intelligence has certain advantages, as it personalizes learning by considering individual characteristics and provides access to information. However, it also introduces challenges, including issues related to the protection of personal data and dependence on technology.
References
Adams, S. J., & Haddad, H. (2021). Artificial intelligence to diagnose heart failure based on chest X-rays and potential clinical implications. Canadian Journal of Cardiology, 37(8), 1153-1155. https://doi.org/10.1016/j.cjca.2021.02.016
Alamgir, A., Mousa, O., & Shah, Z. (2021). Artificial intelligence in predicting cardiac arrest: Scoping review. JMIR Medical Informatics, 9(12), e30798. https://doi.org/10.2196/30798
Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., Bin, O., Saleh, K., Alowais, S. A., Alshaya, O. A., Rahman, I., Yami, A. I., & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural language processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236-1242. https://doi.org/10.1016/j.sapharm.2023.05.016
Awasthi, S., & Soni, Y. (2023). Empowering education system with artificial intelligence: Opportunities and challenges. Shodhsamagam, 6(1), 1–4. https://shodhsamagam.com/uploads/issues_tbl/Empowering%20Education%20System%20with%20Artificial%20Intelligence%20%20Opportunities%20and%20Challenges.pdf
Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning. Decision Analytics Journal, 3, 100071. https://doi.org/10.1016/j.dajour.2022.100071
Chaka, C. (2023). Fourth industrial revolution – A review of applications, prospects, and challenges for artificial intelligence, robotics, and blockchain in higher education. Research and Practice in Technology Enhanced Learning (RPTEL, 18(2). http://rptel.apsce.net/index.php/RPTEL/article/view/2023-18002
Chernenko, N. I. (2022). Shtuchnyy intelekt v upravlinni personalom [Artificial intelligence in personnel management]. Tavriiskyi naukovyi visnyk. Seriia: Ekonomika – Tavriiskyi Scientific Bulletin. Series: Economics, 12, 76–83. https://doi.org/10.32851/2708-0366/2022.12.11
Chlorogiannis, D. D., Apostolos, A., Chlorogiannis, A., Palaiodimos, L., Giannakoulas, G., Pargaonkar, S., Xesfingi, S., & Kokkinidis, D. G. (2023). The role of ChatGPT in the advancement of diagnosis, management, and prognosis of cardiovascular and cerebrovascular disease. Healthcare (Basel, 6(11), 2906. https://doi.org/10.3390/healthcare11212906
Dave, M., & Patel, N. (2023). Artificial intelligence in healthcare and education. British Dental Journal, 234(10), 761-764. https://doi.org/10.1038/s41415-023-5845-2
Dong, C., Qiao, Y., Shang, C., Liao, X., Yuan, X., Cheng, Q., Li, Y., Zhang, J., Wang, Y., Chen, Y., & Ge, Q. (2022). Non-contact screening system for COVID-19 based on XGBoost and logistic regression. Computers in Biology and Medicine, 41, 105003. https://doi.org/10.1016/j.compbiomed.2021.105003
Duong, M. T., Rauschecker, A. M., Rudie, J. D., Chen, P. H., Cook, T. S., Bryan, R. N., & Mohan, S. (2019). Artificial intelligence for precision education in radiology. British Journal of Radiology, 92(1103), 20190389. https://doi.org/10.1259/bjr.20190389
Eynde, J., Lachmann, M., Laugwitz, K. L., Manlhiot, C., & Kutty, S. (2023). Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review. Trends in Cardiovascular Medicine, 33(5), 265-271. https://doi.org/10.1016/j.tcm.2022.01.010
García-Martínez, I., Fernández-Batanero, J. M., Fernández-Cerero, J., & León, S. P. (2023). Analysing the impact of artificial intelligence and computational sciences on student performance: Systematic review and meta-analysis. Journal of New Approaches in Educational Research, 12(1), 171-197. https://doi.org/10.7821/naer.2023.1.1240
Garrison, C. M., Hockenberry, K., & Lacue, S. (2023). Adapting simulation education during a pandemic. Nursing Clinics of North America, 58(1), 1-10. https://doi.org/10.1016/j.cnur.2022.10.008
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
Hairani, H., Anggrawan, A., Wathan, I., Latif, K. A., Marzuki, A., & Zulfikri, M. (2021). The abstract of thesis classifier by using naive Bayes method. In International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM) (pp. 312-315). IEEE. https://doi.org/10.1109/ICSECS52883.2021.00063
Hong, W., Zhou, X., Jin, S., Lu, Y., Pan, J., Lin, Q., Yang, S., Xu, T., Basharat, Z., Zippi, M., Fiorino, S., Tsukanov, V., Stock, S., Grottesi, A., Chen, Q., & Pan, J. (2022). A comparison of XGBoost, random forest, and nomograph for the prediction of disease severity in patients with COVID-19 pneumonia: Implications of cytokine and immune cell profile. Frontiers in Cellular and Infection Microbiology, 12, 819267. https://doi.org/10.3389/fcimb.2022.819267
Ikemura, K., Bellin, E., Yagi, Y., Billett, H., Saada, M., Simone, K., Stahl, L., Szymanski, J., Goldstein, D. Y., & Reyes, Gil M. (2021). Using automated machine learning to predict the mortality of patients with COVID-19: Prediction model development study. Journal of Medical Internet Research, 23(2), e23458. https://doi.org/10.2196/23458
Khan, I., Ahmad, A. R., Jabeur, N., & Mahdi, M. N. (2021). An artificial intelligence approach to monitor student performance and devise preventive measures. Smart Learning Environments, 1(1), 1-18. https://doi.org/10.1186/s40561-021-00161-y
Kivrak, M., Guldogan, E., & Colak, C. (2021). Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods. Computers in Biology and Medicine, 201, 105951. https://doi.org/10.1016/j.cmpb.2021.105951
Lamanauskas, V., & Makarskaitė-Petkevičien, R. (2016). University study quality: Understanding, improvement, influential factors. Quality Issues and Insights in the 21st Century, 5(1), 31-46. https://oaji.net/articles/2017/451-1505927122.pdf
Landry, M. D., van Wijchen, J., Hellinckx, P., Rowe, M., Ahmadi, E., Coninx, K., Mercelis, S., Hansen, D., & Vissers, D. (2022). Artificial intelligence and data-driven rehabilitation: The next frontier in the management of cardiometabolic disorders. Archives of Physical Medicine and Rehabilitation, 103(8), 1693-1695. https://doi.org/10.1016/j.apmr.2022.03.022
Lin, K.-Y., & Chang, K.-H. (2023). Artificial intelligence and information processing: A systematic literature review. Mathematics, 11, 2420. https://doi.org/10.3390/math11112420
Lubko, D. V., & Sharov, S. V. (2019). Methods and systems of artificial intelligence: Education manual. Melitopol: FOP Odnorog. http://www.tsatu.edu.ua/kn/wp-content/uploads/sites/16/knyha.-msshy-v-byblyoteku.pdf
Masters, K. (2019). Artificial intelligence in medical education. Medical Teacher, 41(9), 976-980. https://doi.org/10.1080/0142159X.2019.1595557
Mohamed, M. Z. B., Hidayat, R., Suhaizi, N. N. B., Sabri, N. B. M., Mahmud, M. K. H. B., & Baharuddin, S. N. B. (2022). Artificial intelligence in mathematics education: A systematic literature review. International Electronic Journal of Mathematics Education, 17(3), em0694. https://doi.org/10.29333/iejme/12132
Nagi, F., Salih, R., Alzubaidi, M., Shah, H., Alam, T., Shah, Z., & Househ, M. (2023). Applications of artificial intelligence (AI) in medical education: A scoping review. Studies in Health Technology and Informatics, 29, 305, 648-651. https://doi.org/10.3233/SHTI230581
Namoun, A., & Alshanqiti, A. (2020). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 1, 237. https://doi.org/10.3390/app11010237
Oka, K., Shiode, R., Yoshii, Y., Tanaka, H., Iwahashi, T., & Murase, T. (2021). Artificial intelligence to diagnose distal radius fracture using biplane plain X-rays. Journal of Orthopaedic Surgery and Research, 16(1), 694. https://doi.org/10.1186/s13018-021-02845-0
Panukhnyk, O. (2023). Artificial intelligence in the educational process and scientific research of higher education applicants: Responsible boundaries of AI content. Galician Economic Journal, 83(4), 202-211. https://doi.org/10.33108/galicianvisnyk_tntu2023.04.202
Pizhuk, O. I. (2019). Artificial intelligence as one of the key drivers of the economy digital transformation. Economics, Management and Administration, (3(89), 41–46. https://doi.org/10.26642/ema-2019-3(89)-41-46
Punn, N. S., & Agarwal, S. (2022). Modality specific U-Net variants for biomedical image segmentation: A survey. Artificial Intelligence Review, 55(7), 5845-5889. https://doi.org/10.1007/s10462-022-10152-1
Quasim, M. A., Khan, S., Srivastava, V. K., Ghaznavi, A. A., & Ahmad, A. H. M. (2021). Role of cementation and compaction in controlling the reservoir quality of the middle to late Jurassic sandstones, Jara Dome, Kachchh Basin, Western India. Geological Journal, 56, 976–994. https://doi.org/10.1002/gj.3989
Salcedo, J., Rosales, M., Kim, J. S., Nuno, D., Suen, S. C., & Chang, A. H. (2021). Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study. PLoS One, 16(7), e0254950. https://doi.org/10.1371/journal.pone.0254950
Sekandi, J. N., Shi, W., Zhu, R., Kaggwa, P., Mwebaze, E., & Li, S. (2023). Application of artificial intelligence to the monitoring of medication adherence for tuberculosis treatment in Africa: Algorithm development and validation. JMIR AI, 2(1), e40167. https://doi.org/10.2196/40167
Viswanathan, V. S., Toro, P., Corredor, G., Mukhopadhyay, S., & Madabhushi, A. (2022). The state of the art for artificial intelligence in lung digital pathology. Journal of Pathology, 257(4), 413-429. https://doi.org/10.1002/path.5966
Yaacob, W. F. W., Nasir, S. A. M., Yaacob, W. F. W., & Sobri, N. M. (2019). Supervised data mining approach for predicting student performance. Indonesian Journal of Electrical Engineering and Computer Science, 16(3), 1584-1592. https://doi.org/10.11591/ijeecs.v16.i3.pp1584-1592
Zhang, L., Li, J., Wang, W., Li, C., Zhang, Y., Jiang, S., Jia, T., & Yan, Y. (2022). Diagenetic facies characteristics and quantitative prediction via wireline logs based on machine learning: A case of Lianggaoshan tight sandstone, Fuling area, Southeastern Sichuan Basin, Southwest China. Frontiers in Earth Science, 10, 1018442. https://doi.org/10.3389/feart.2022.1018442
Zhou, X., Zhang, C., Zhang, Z., Zhang, R., Zhu, L., & Zhang, C. (2019). A saturation evaluation method in tight gas sandstones based on diagenetic facies. Marine and Petroleum Geology, 107, 310–325. https://doi.org/10.1016/j.marpetgeo.2019.05.022
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