THE UTILISATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES BY MATHEMATICS TEACHERS IN THE PROCESS OF RESEARCH-BASED LEARNING TO MOTIVATE STUDENTS’ LEARNING AND COGNITIVE ACTIVITY: AN INVESTIGATION OF THE CHALLENGES, THREATS AND PROSPECTS
DOI:
https://doi.org/10.32782/ped-uzhnu/2024-4-18Keywords:
research-based learning, artificial intelligence, neural networks, didactics of mathematics, mathematics lesson, variational models, COMSRLAbstract
The study analyses the feasibility of pedagogically balanced and methodologically motivated use of artificial intelligence (AI) technologies designed to solve mathematical problems, offering users various functions and opportunities for work in the classroom, and suggests methods and techniques for research-based mathematics teaching. The advantages and disadvantages of using AI in the process of research-based mathematics education are considered, and examples of the use of neural networks in the educational process are substantiated. The study evaluates the correctness, completeness and speed of calculating mathematical expressions using intellectual and special-purpose AI tools by mathematics teachers in the process of research teaching of mathematical subjects in the context of motivating students’ learning and cognitive activity. using the MathGPTPro and ChatGPT services in the process of solving problems, we obtained an unsatisfactory result. It is worth noting that MathGPTPro solved twice as many tasks correctly as ChatGPT, but the accuracy of these tools does not correspond to the necessary and sufficient level of mathematical services. Attention is drawn to the need for a thorough filling of the AI alphabet, since numerous incomprehensible signs and symbols were displayed in the process of displaying the results of solving problems. Based on the analysis of the study results, it can be concluded that it is inexpedient and inefficient to use thoughtless and unreasonable AI tools in the process of solving mathematical problems, including problems of increased complexity. Examples of designing and options for refining the structure of mathematics lessons using AI tools are considered. To improve the efficiency of educational institution management, AI takes over routine operations, helps to make decisions and correct teachers’ actions. The effectiveness of using the individual trajectory of student research learning recommended by AI depends on the properties of information in the context of avoiding false decisions.
References
Гриб’юк О. О. Форми і методи використання технологій штучного інтелекту для професійного розвитку педагогічних кадрів: дидактичні та психофізіологічні аспекти дослідницького навчання. Габітус: науковий журнал. Одеса: Видавничий дім «Гельветика», 2024. Вип. 60. С. 55–68. URL: https://doi.org/10.32782/2663-5208.2024.60.9.
Гриб’юк О. О. Педагогічне проектування варіативних моделей комп’ютерно орієнтованих методичних систем дослідницького навчання предметів природничо-математичного циклу з використанням технологій штучного інтелекту. Педагогіка формування творчої особистості у вищій і загальноосвітній школах : зб. наук. пр. [редкол.: А.В. Сущенко (голов. ред.) та ін.]. Одеса : Видавничий дім «Гельветика», 2024. Вип. 92. С. 93–102. URL: https://doi.org/10.32782/1992-5786.2024.92.15.
Baierle I.L.F., Gluz J.C. Programming Intelligent Embodied Pedagogical Agents to Teach the Beginnings of Industrial Revolution. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds) Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science, 2018. vol 10858. P. 3–12. Springer, Cham. URL: https://doi.org/10.1007/978-3-319-91464-0_1 (date of access: 17.04.2024).
Bebbington K., MacLeod C. The sky is falling: Evidence of negativity bias in the social transmission of information. Evolution and Human Behavior. 2017. Vol. 38 (1). P. 92–101. URL: https://doi.org/10.1016/j.evolhumbehav.2016.07.004 (date of access: 27.04.2024).
Cannoni E., Scalis T. G. Indagine sui bambini di 5–6 anni cheusano quotidianamente i dispositivi mobili in ambito familiare: caratteristiche personali e contestuali e problematiche cognitive ed emotive. Rassegna Italiana di Psicologia. 2018. Vol. 35 (1). P. 41–56. URL: https://hdl.handle.net/11573/1116494 (date of access: 07.05.2024).
Chakraborty B., Chakma K., Mukherjee, A. A density-based clustering algorithm and experiments on student dataset with noises using rough set theory. IEEE International Conference on Engineering and Technology, 2016. P. 431–436. URL: https://doi.org/10.1109/ICETECH.2016.7569290 (date of access: 09.05.2024).
ChatGPT Shared Links FAQ. Help OpenAI. URL: https://help.openai.com/en/articles/7925741-chatgpt-sharedlinks-faq/ (дата звернення: 27.05.2024).
Gebru T., Morgenstern J., Vecchione B., Vaughan J. W., Wallach H., Daumé H., Crawford K. Datasheets for datasets. arXiv.org. URL: https://arxiv.org/abs/1803.09010/ (date of access: 25.05.2024).
Hrybiuk O., Kant G. S. CleverCOMSRL: Implementation of an AI Computer-Aided Design System in the Context of the Cognitive Science Paradigm for the Research Training Process. Lecture Notes in Mechanical Engineering. Cham, 2024. P. 351–362. URL: https://doi.org/10.1007/978-3-031-61575-7_32 (date of access: 05.07.2024).
Hrybiuk O. Improvement of the Educational Process by the Creation of Centers for Intellectual Development and Scientific and Technical Creativity. Lecture Notes in Mechanical Engineering. Cham, 2019. P. 370–382. URL: https://doi.org/10.1007/978-3-030-18789-7_31 (date of access: 27.05.2024).
Yang Z., Talha M. A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data. Computational and Mathematical Methods in Medicine. 2021. Vol. 2021. P. 1–11. URL: https://doi.org/10.1155/2021/1725490 (date of access: 23.04.2024).
Levendowski A. How copyright law can fix artificial intelligence’s implicit bias problem. Washington Law Review. 2018. Vol. 93 (2). P. 579–630. URL: https://digitalcommons.law.uw.edu/wlr/vol93/iss2/2 (date of access: 23.04.2024).
Maseleno А. Demystifying Learning Analytics in Personalised Learning. International Journal of Engineering & Technology. 2018. Vol. 7, no. 3. P. 1124–1129. URL: https://doi.org/10.14419/ijet.v7i3.9789 (date of access: 17.05.2024).
Nkambou R., Azevedo R., Vassileva J. Intelligent Tutoring Systems. 14th International Conference. 2018, ITS 2018. Montreal, QC, Canada. https://link.springer.com/book/10.1007/978-3-319-91464-0 (date of access: 25.05.2024).
Penprase B. E. The Fourth Industrial Revolution and Higher Education. Higher Education in the Era of the Fourth Industrial Revolution. Singapore, 2018. P. 207–229. URL: https://doi.org/10.1007/978-981-13-0194-0_9 (date of access: 24.04.2024).
Software for Exascale Computing - SPPEXA 2016-2019 / ed. by H.-J. Bungartz et al. Cham : Springer International Publishing, 2020. URL: https://doi.org/10.1007/978-3-030-47956-5 (date of access: 29.04.2024).
Van Brummelen J., Tabunshchyk V., Heng T. “Alexa, Can I Program You?”: Student Perceptions of Conversational Artificial Intelligence Before and After Programming Alexa. IDC’21: Interaction Design and Children, Athens Greece. New York, NY, USA, 2021. Р. 305–313. URL: https://doi.org/10.1145/3459990.3460730 (date of access: 17.05.2024).
Zanetti M. Prejudice and labelling: the role of the teacher in the development of deviant behaviours. Formazione & Insegnamento. 2018. Vol. 16 (2). P. 193–204. URL: https://ojs.pensamultimedia.it/index.php/siref/article/view/3044 (date of access: 27.05.2024).
