Keywords:
psychology
philosophy
higher education
adult education
academic integrity
educational technologies

  • Abstract

    The study of the interaction between STEM education and artificial intelligence is relevant for ensuring academic integrity because of the need to guarantee the efficacy and impartiality of the educational process, which must align with contemporary technological demands and ethical standards. The objective of this research article is to identify the challenges and potential for the utilisation of artificial intelligence in the development of STEM education programmes and curricula. This article examines the efficacy of current preventive measures against students' misuse of AI technologies in the context of STEM education. In the course of composing the research article, the authors employed a systematic approach to analysing and generalising the findings of their review of the literature, with the objective of identifying the critical aspects of academic integrity in the use of artificial intelligence in STEM education. The study employed expert assessment to collate data on the total number of coursework items examined and the number of works in which the GPT detector identified indications of AI usage. The calculation of the percentage of violations of academic integrity through the use of artificial intelligence to the total number of coursework for each speciality revealed that the percentage of violations of academic integrity is greater at the Igor Sikorsky Kyiv Polytechnic Institute, where the coursework was checked for the first time for the use of AI (7.46%), than at Taras Shevchenko National University of Kyiv (4.03%), where the check is carried out for two semesters. Concurrently, there is an emerging concern regarding the increasing incidence of academic integrity violations facilitated by AI technologies. This necessitates the formulation of transparent guidelines governing the deployment of AI in the educational sphere, enhancements to the assessment framework, and the integration of AI-based detection tools into the evaluation of student performance. Moreover, it is imperative to cultivate a heightened ethical consciousness among both students and educators.

  • References

    1. Ahmad, T. (2020). Scenario based approach to re-imagining future of higher education which prepares students for the future of work. Higher Education, Skills and Work-Based Learning, 10(1), 217-238. https://doi.org/10.1108/HESWBL-12-2018-0136
    2. Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440. https://doi.org/10.1007/s43681-021-00096-7
    3. Bakhmat, N., Sydoruk, L., Poberezhets, H., Misenyova, V., Boyarova, O., & Mazur, Y. (2023). Features of Using the Opportunities of the Digital Environment of the Higher Educational Institution for the Development of Future Economists' Professional Competence. Economic Affairs, 68(1s), 43-50. https://doi.org/10.46852/0424-2513.1s.2023.6
    4. Baracchini, F., & Stary, C. (2023). What is the Impact of ChatGPT on Facilitating Knowledge Provision and Creation? Examples and Criticalities in Classroom Education. Proceedings of the International Conference on Knowledge Management (ICKM), 23, 18. https://www.researchgate.netf. Accessed on August 22, 2024.
    5. Bhutoria, A. (2022). Personalised education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100068. https://doi.org/10.1016/j.caeai.2022.100068
    6. Bureau of Labor Statistics. (2023). Employment in STEM occupations U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/tables/stem-employment.htm. Accessed on August 22, 2024.
    7. Chan, C. K. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3
    8. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
    9. Chiu, T. K., & Li, Y. (2023). How Can Emerging Technologies Impact STEM Education? Journal for STEM Education Research, 6(3), 375-384. https://doi.org/10.1007/s41979-023-00113-w
    10. Dai, C. P., & Ke, F. (2022). Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computers and Education: Artificial Intelligence, 3, 100087. https://doi.org/10.1016/j.caeai.2022.100087
    11. Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134-147. https://doi.org/10.1016/j.ijis.2020.09.001
    12. Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504-526. https://doi.org/10.1007/s40593-021-00239-1
    13. Joseph, O. B., & Uzondu, N. C. (2024). Integrating AI and Machine Learning in STEM education: Challenges and opportunities. Computer Science & IT Research Journal, 5(8), 1732-1750. https://doi.org/10.51594/csitrj.v5i8.1379
    14. Kubitskyi, S., Saienko, V., Demianiuk, V., & Mykhasiuk, K. (2022). Management of pedagogical and sports educational institutions in Ukraine. SPORT TK-Revista EuroAmericana de Ciencias del Deporte, 11(Sup3), 19. https://doi.org/10.6018/sportk.538991
    15. Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
    16. Maiyanti, A. A., Yusal, Y., & Anggraini, A. (2023). Using ChatGPT to Help Make Hot Questions Quickly and Easily. International Conference on Education, 95-102. https://jurnalfaktarbiyah.iainkediri.ac.id/index.php/proceedings/article/view/1744. Accessed on August 22, 2024.
    17. Merelo, J. J., Castillo, P. A., Mora, A. M., Barranco, F., Abbas, N., Guillén, A., & Tsivitanidou, O. (2024). Chatbots and messaging platforms in the classroom: an analysis from the teacher's perspective. Education and Information Technologies, 29(2), 1903-1938. https://doi.org/10.1007/s10639-023-11703-x
    18. Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Working Papers on Education Policy, 07, 48. https://hdl.handle.net/20.500.12799/6533. Accessed on August 22,2024.
    19. Pudasaini, S., Miralles-Pechuán, L., Lillis, D., & Salvador, M. L. (2024). Survey on Plagiarism Detection in Large Language Models: The Impact of ChatGPT and Gemini on Academic Integrity. arXiv preprint arXiv:2407.13105. https://doi.org/10.48550/arXiv.2407.13105
    20. Rane, N., Choudhary, S., & Rane, J. (2023). Education 4.0 and 5.0: Integrating artificial intelligence (AI) for personalised and adaptive learning. Journal of Artificial Intelligence and Robotics, 1(1), 29-43. http://dx.doi.org/10.2139/ssrn.4638365
    21. Rowland, D. R. (2023). Two frameworks to guide discussions around levels of acceptable use of generative AI in student academic research and writing. Journal of Academic Language and Learning, 17(1), T31-T69. https://journal.aall.org.au/index.php/jall/article/view/915. Accessed on August 22, 2024.
    22. Shkola, O. M., Otravenko, O. V., Donchenko, V. I., Zhamardiy, V. O., Saienko, V. G., & Tolchieva, H. V. (2022). The influence of tae-bo on the development of motor potential of students of medical and pedagogical specialties and its efficiency in the process of extracurricular activities. Wiadomości Lekarskie, 75(4 p1), 865-870. https://doi.org/10.36740/WLek202204121
    23. Slobodianiuk, O. M. (2021). Academic Integrity in Higher Education of Ukraine: Features of Implementation and Development Trends. Pedagogy of the 21st century: Current state and development trends: A collective monograph. Liga-Press, 388-414. https://doi.org/10.36059/978-966-397-241-1-15
    24. Sullivan, M., Kelly, A. & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and learning. Journal of Applied Learning and Teaching, 6(1), 31-40. https://doi.org/10.37074/jalt.2023.6.1.17
    25. Verma, M. (2023). The Digital Circular Economy: ChatGPT and the future of STEM Education and Research. International Journal of Trend in Scientific Research and Development, 7(3), 178-182. https://www.researchgate.net/ Accessed on August 22, 2024.
    26. Wu, Y. T., & Anderson, O. R. (2015). Technology-enhanced stem (science, technology, engineering, and mathematics) education. Journal of Computers in Education, 2, 245-249. https://doi.org/10.1007/s40692-015-0041-2
    27. Zhylin, M., Tormanova, A., Poplavska, Y., Chernov, M. M., & Druz, O. (2024). Study of digital approach in education and psychology among selected users in Ukraine. Multidisciplinary Reviews, 6, 2023spe007. https://doi.org/10.31893/multirev.2023spe007
    28. Zinchenko, V., Mielkov, Y., Nych, T., Abasov, M., & Trynyak, M. (2023). Human Thinking in the Age of Generative AI: Values of Openness and Higher Education for the Future. In 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 1-6. https://doi.org/10.1109/ICECET58911.2023.10389449
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2024 The Authors

How to cite

Streletska, N., Ulishchenko, A., Klieba, A., Vlasiuk, I., & Genkal, S. (2024). Integrating artificial intelligence into STEM education: Navigating academic integrity. Multidisciplinary Reviews, 8, 2024spe069. https://doi.org/10.31893/multirev.2024spe069
Crossref
Scopus
Google Scholar
Europe PMC
  • Article viewed - 477
  • PDF downloaded - 195