Generative AI Tools in Higher Education: Benefits, Limitations, and Ethical Risks

Yuliia Saienko; Viktor Kyrychenko; Olena Brona; Alla Malii; Viktor Pasichnyk

doi:10.63931/ijchr.v7iSI1.2.517

Authors

Yuliia Saienko Bohdan Khmelnytsky Melitopol State Pedagogical University, Zaporizhzhia, Ukraine https://orcid.org/0000-0001-9174-7684
Viktor Kyrychenko National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine https://orcid.org/0009-0001-0575-8684
Olena Brona Ivan Franko National University of Lviv, Lviv, Ukraine. https://orcid.org/0000-0003-3853-334X
Alla Malii Educational and Scientific Institute of Philology, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine https://orcid.org/0000-0003-1240-4902
Viktor Pasichnyk Interregional Academy of Personnel Management, Kyiv, Ukraine https://orcid.org/0009-0001-5695-305X

DOI:

https://doi.org/10.63931/ijchr.v7iSI1.2.517

Keywords:

neural networks, university environment, terminology, English academic writing, concept, academic integrity

Abstract

In recent years, neural networks have experienced rapid development and are increasingly implemented across various areas of human activity, including education, which necessitates research into their effectiveness for didactic purposes. This article investigates the opportunities and challenges associated with using generative AI in higher education, particularly for professional English language training among natural science students. The study employs theoretical general-logical methods (analysis, synthesis, generalization, induction, deduction) and empirical methods (pedagogical observation, experiment, description, survey). Its theoretical and methodological foundation is based on cognitive psychology, cognitive pedagogy, and AI theory. The article critically analyzes the literature, highlights the advantages and limitations of implementing generative AI in educational processes, and examines the didactic potential of neural networks such as ChatGPT, Copilot, and AI Tutor. A pedagogical experiment was conducted, exploring AI’s roles as teacher, editor, and foreign-language interlocutor. The study identifies key pedagogical capabilities of ChatGPT in fostering students’ cognitive and linguistic development, including critical thinking, creativity, cognitive interest, motivation, reflexivity, and language skill acquisition. Results demonstrate AI’s effectiveness in compiling thematic glossaries, contextualizing terminology, testing vocabulary, and conducting dialogues, which enhance cognitive engagement. Final assessments revealed that students in the experimental group achieved higher proficiency in geological English terminology, scientific text comprehension, and listening skills.

References

[1] Agaoglu, M. (2016). Predicting instructor performance using data mining techniques in higher education. IEEE Access, 4, 2379–2387. https://doi.org/10.1109/ACCESS.2016.2568756 DOI: https://doi.org/10.1109/ACCESS.2016.2568756

[2] Al-Shanfari, L., Abdullah, S., Fstnassi, T., & Al-Kharusi, S. (2023). Instructors’ perceptions of intelligent tutoring systems and their implications for studying computer programming in Omani higher education institutions. International Journal of Membrane Science and Technology, 10, 947–967. https://doi.org/10.15379/ijmst.v10i2.1395 DOI: https://doi.org/10.15379/ijmst.v10i2.1395

[3] Aluko, R. O., Adenuga, O. A., Kukoyi, P. O., Soyingbe, A. A., & Oyedeji, J. O. (2016). Predicting the academic success of architecture students by pre-enrolment requirement: Using machine-learning techniques. Construction Economics and Building, 16(4), 86–98. https://doi.org/10.5130/AJCEB.v16i4.5184 DOI: https://doi.org/10.5130/AJCEB.v16i4.5184

[4] Aluthman, E. S. (2016). The effect of using automated essay evaluation on ESL undergraduate students’ writing skill. International Journal of English Linguistics, 6(5), 54–67. https://doi.org/10.5539/ijel.v6n5p54 DOI: https://doi.org/10.5539/ijel.v6n5p54

[5] Baillifard, A., Gabella, M., Lavenex, P. B., & Martarelli, C. S. (2025). Implementing learning principles with a personal AI tutor: A case study. Education and Information Technologies, 30, 297–312. https://doi.org/10.1007/s10639-024-12888-5 DOI: https://doi.org/10.1007/s10639-024-12888-5

[6] Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf

[7] Barker, T. (2011). An automated individual feedback and marking system: An empirical study. Electronic Journal of E-Learning, 9(1), 1–14. https://www.learntechlib.org/p/52053/

[8] Bartolomé, A., Castañeda, L., & Adell, J. (2018). Personalisation in educational technology: The absence of underlying pedagogies. International Journal of Educational Technology in Higher Education, 15(14). https://doi.org/10.1186/s41239-018-0095-0 DOI: https://doi.org/10.1186/s41239-018-0095-0

[9] Camacho-Sánchez, R., Serna Bardavío, J., Rillo-Albert, A., & Lavega-Burgués, P. (2023). Enhancing motivation and academic performance through gamified digital game-based learning methodology using the ARCS model. Interactive Learning Environments, 32(10), 1–18. https://doi.org/10.1080/10494820.2023.2294762 DOI: https://doi.org/10.1080/10494820.2023.2294762

[10] Capon-Sieber, V., Köhler, C., Alp Christ, A., Helbling, J., & Praetorius, A. K. (2022). The role of relatedness in the motivation and vitality of university students in online classes during social distancing. Frontiers in Psychology, 12, 702323. https://doi.org/10.3389/fpsyg.2021.702323 DOI: https://doi.org/10.3389/fpsyg.2021.702323

[11] Casamayor, A., Amandi, A., & Campo, M. (2009). Intelligent assistance for teachers in collaborative e-learning environments. Computers & Education, 53(4), 1147–1154. https://doi.org/10.1016/j.compedu.2009.05.025 DOI: https://doi.org/10.1016/j.compedu.2009.05.025

[12] Castañeda, L., & Selwyn, N. (2018). More than tools? Making sense of the ongoing digitizations of higher education. International Journal of Educational Technology in Higher Education, 15(22). https://doi.org/10.1186/s41239-018-0109-y DOI: https://doi.org/10.1186/s41239-018-0109-y

[13] Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20. https://doi.org/10.1186/s41239-023-00408-3 DOI: https://doi.org/10.1186/s41239-023-00408-3

[14] Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, 43. https://doi.org/10.1186/s41239-023-00411-8 DOI: https://doi.org/10.1186/s41239-023-00411-8

[15] Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8 DOI: https://doi.org/10.1186/s41239-023-00392-8

[16] Duzhin, F., & Gustafsson, A. (2018). Machine learning-based app for self-evaluation of teacher-specific instructional style and tools. Education Sciences, 8(1). https://doi.org/10.3390/educsci8010007 DOI: https://doi.org/10.3390/educsci8010007

[17] Exintaris, B., Karunaratne, N., & Yuriev, E. (2023). Metacognition and critical thinking: Using ChatGPT-generated responses as prompts for critique in a problem-solving workshop (SMARTCHEMPer). Journal of Chemical Education, 100, 2972–2980. https://pubs.acs.org/doi/10.1021/acs.jchemed.3c00481 DOI: https://doi.org/10.1021/acs.jchemed.3c00481

[18] Garcia-Gorrostieta, J. M., Lopez-Lopez, A., & Gonzalez-Lopez, S. (2018). Automatic argument assessment of final project reports of computer engineering students. Computer Applications in Engineering Education, 26(5), 1217–1226. https://doi.org/10.1002/cae.21996 DOI: https://doi.org/10.1002/cae.21996

[19] Haugeland, J. (1985). Artificial intelligence: The very idea. MIT Press. https://philpapers.org/rec/HAUAIT

[20] Hew, K. F., Lan, M., Tang, Y., Jia, C., & Lo, C. K. (2019). Where is the “theory” within the field of educational technology research? British Journal of Educational Technology, 50(3), 956–971. https://doi.org/10.1111/bjet.12770 DOI: https://doi.org/10.1111/bjet.12770

[21] Hinojo-Lucena, F.-J., Aznar-Díaz, I., Cáceres-Reche, M.-P., & Romero-Rodríguez, J.-M. (2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 51. https://doi.org/10.3390/educsci9010051 DOI: https://doi.org/10.3390/educsci9010051

[22] Holmes, W., Iniesto, F., Anastopoulou, S., & Boticario, J. G. (2023). Stakeholder perspectives on the ethics of AI in distance-based higher education. International Review of Research in Open and Distributed Learning, 24, 96–117. https://doi.org/10.19173/irrodl.v24i2.6089 DOI: https://doi.org/10.19173/irrodl.v24i2.6089

[23] Hooshyar, D., Ahmad, R., Yousefi, M., Yusop, F., & Horng, S. (2015). A flowchart-based intelligent tutoring system for improving problem-solving skills of novice programmers. Journal of Computer Assisted Learning, 31(4), 345–361. https://doi.org/10.1111/jcal.12099 DOI: https://doi.org/10.1111/jcal.12099

[24] Howard, J. L., Bureau, J. S., Guay, F., Chong, J. X., & Ryan, R. M. (2021). Student motivation and associated outcomes: A meta-analysis from self-determination theory. Perspectives on Psychological Science, 16(6), 1300–1323. https://doi.org/10.1177/1745691620966789 DOI: https://doi.org/10.1177/1745691620966789

[25] Kalliamvakou, E. (2022). Research: Quantifying GitHub Copilot’s impact on developer productivity and happiness. GitHub Blog. https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness

[26] Kirwan, A. (2023). ChatGPT and university teaching, learning and assessment: Some initial reflections on teaching academic integrity in the age of large language models. Irish Educational Studies, 43(4), 1389–1406. https://doi.org/10.1080/03323315.2023.2284901 DOI: https://doi.org/10.1080/03323315.2023.2284901

[27] Kuramitsu, K., Obara, Y., Sato, M., & Obara, M. (2023). KOGI: A seamless integration of ChatGPT into Jupyter environments for programming education. In Proceedings of the 2023 ACM SIGPLAN International Symposium on SPLASH-E (SPLASH-E 2023) (pp. 50–59). Association for Computing Machinery. https://doi.org/10.1145/3622780.3623648 DOI: https://doi.org/10.1145/3622780.3623648

[28] Malang, B. (2025). The impact on using Chat-Generative Pre-Trained Transformer on students. International Journal on Culture, History, and Religion, 7(1), 373–392. https://doi.org/10.63931/ijchr.v7i1.85 DOI: https://doi.org/10.63931/ijchr.v7i1.85

[29] Sallam, M., Elsayed, W., Al-Shorbagy, M., Barakat, M., El Khatib, S., Ghach, W., Alwan, N., Hallit, S., & Malaeb, D. (2024). ChatGPT usage and attitudes are driven by perceptions of usefulness, ease of use, risks, and psycho-social impact: A study among university students in the UAE. Frontiers in Education, 9, 1414758. https://doi.org/10.3389/feduc.2024.1414758 DOI: https://doi.org/10.3389/feduc.2024.1414758

[30] Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1). https://doi.org/10.37074/jalt.2023.6.1.17 DOI: https://doi.org/10.37074/jalt.2023.6.1.17

[31] Tung, L. (2023). ChatGPT’s next big challenge: Helping Microsoft to challenge Google Search. ZDNET. https://www.zdnet.com/article/chatgpts-next-big-challenge-helping-microsoft-to-challenge-google-search/

[32] Wang, X., & Feng, Y. (2024). An experimental study of ChatGPT-assisted improvement of Chinese college students’ English reading skills: A case study of Dear Life. In Proceedings of the 15th International Conference on Education Technology and Computers (ICETC ’23). https://doi.org/10.1145/3629296.3629300 DOI: https://doi.org/10.1145/3629296.3629300

[33] Weale, S. (2025). UK universities warned to “stress-test” assessments as 92% of students use AI. The Guardian. https://www.theguardian.com/education/2025/feb/26/uk-universities-warned-to-stress-test-assessments-as-92-of-students-use-ai

[34] Zaiarna, I., Zhyhadlo, O., & Dunaievska, O. (2024). ChatGPT in foreign language teaching and assessment: Exploring EFL instructors’ experience. Information Technologies and Learning Tools, 102(4), 176–191. https://doi.org/10.33407/itlt.v102i4.5716 DOI: https://doi.org/10.33407/itlt.v102i4.5716

[35] Zawacki-Richter, O., Martín, 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(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0 DOI: https://doi.org/10.1186/s41239-019-0171-0

[36] Zeb, A., Goh, G. G. G., Javaid, M., Khan, M. N., Khan, A. U., & Gul, S. (2023). The interplay between supervisor support and job performance: Implications of social exchange and social learning theories. Journal of Applied Research in Higher Education, 15(2), 429–448. https://doi.org/10.1108/JARHE-04-2021-0143 DOI: https://doi.org/10.1108/JARHE-04-2021-0143