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Educational Innovation of Using Artificial Intelligence in University Education: A Comprehensive Student Survey

Year 2024, Volume: 10 Issue: 2, 55 - 65
https://doi.org/10.25233/ijlel.1528746

Abstract

This paper investigates the adoption and potential integration of artificial intelligence (AI) within higher education, examining its impact on educators and learners through detailed perspectives gathered from university students. It provides an extensive literature review outlining the dynamics, characteristics, and the application of AI in the educational sector. The primary research included a meticulously designed survey distributed among active students to assess their current experiences, perceived benefits, and concerns having AI-driven materials and tools in educational environments. Based on the learners’ responses a generally positive attitude towards the use of AI was revealed among the university students. They expressed a strong belief in their ability to learn with and utilize AI tools effectively, acknowledging the significant advantages AI can offer in enhancing educational experiences and providing personalized academic support. This optimistic view is, however, tempered by significant concerns, particularly regarding ethical issues and the potential shift away from traditional pedagogical methods. The data also showed that the participants highly valued the effectiveness and accessibility provided by the AI-enhanced instructional materials and teaching methods. Despite this, there remained a substantial degree of apprehension surrounding the ethical implications and safety of AI applications in education. This paper makes a significant contribution to the field of educational technology by providing primary research on AI-related challenges and considerations. It highlights the critical importance of maintaining a balanced approach that prioritizes technological innovation alongside ethical considerations and human-centered practices in the development and integration of AI into higher education, advocating for responsible use of technology.

Supporting Institution

Óbuda University, Keleti Károly Faculty of Business and Management

References

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Year 2024, Volume: 10 Issue: 2, 55 - 65
https://doi.org/10.25233/ijlel.1528746

Abstract

References

  • Ahmad, S. F., Alam, M. M., Rahmat, Mohd. K., Mubarik, M. S., & Hyder, S. I. (2022). Academic and Administrative Role of Artificial Intelligence in Education. Sustainability, 14(3), 1101. https://doi.org/10.3390/su14031101
  • Al-Zahrani, A. M. (2024). From traditionalism to algorithms: embracing artificial intelligence for effective university teaching and learning. TECHNOLOGY, 2(2), 103.
  • Alqahtani, A. Y., & Rajkhan, A. A. (2020). E-Learning Critical Success Factors during the COVID-19 Pandemic: A Comprehensive Analysis of E-Learning Managerial Perspectives. Education Sciences, 10(9), 216. https://doi.org/10.3390/educsci10090216
  • Anantrasirichai, N., & Bull, D. (2022). Artificial intelligence in the creative industries: A review. Artificial Intelligence Review, 55(1), 589–656. https://doi.org/10.1007/s10462-021-10039-7
  • Bajaj, R., & Sharma, V. (2018). Smart Education with artificial intelligence based determination of learning styles. Procedia Computer Science, 132, 834–842. https://doi.org/10.1016/j.procs.2018.05.095
  • Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17(1), 42, s41239-020-00218–x. https://doi.org/10.1186/s41239-020-00218-x
  • Bonezzi, A., & Ostinelli, M. (2021). Can algorithms legitimize discrimination? Journal of Experimental Psychology: Applied, 27(2), 447–459. https://doi.org/10.1037/xap0000294
  • Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z
  • Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
  • Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The Promises and Challenges of Artificial Intelligence for Teachers: A Systematic Review of Research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y
  • 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(1), 38. https://doi.org/10.1186/s41239-023-00408-3
  • Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233
  • Chen, C., Fu, J., & Lyu, L. (2023). A Pathway Towards Responsible AI Generated Content (Version 3). arXiv. https://doi.org/10.48550/ARXIV.2303.01325
  • Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
  • Corea, F. (2019). Applied Artificial Intelligence: Where AI Can Be Used In Business. Springer International Publishing. https://doi.org/10.1007/978-3-319-77252-3
  • Curzon, J., Kosa, T. A., Akalu, R., & El-Khatib, K. (2021). Privacy and Artificial Intelligence. IEEE Transactions on Artificial Intelligence, 2(2), 96–108. https://doi.org/10.1109/TAI.2021.3088084
  • Dečman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in Human Behavior, 49, 272–281. https://doi.org/10.1016/j.chb.2015.03.022
  • European Commission. Joint Research Centre. (2020). AI Watch, historical evolution of artificial intelligence: Analysis of the three main paradigm shifts in AI. Publications Office. https://data.europa.eu/doi/10.2760/801580
  • Fernández Herrero, J., Gómez Donoso, F., & Roig Vila, R. (2023). The first steps for adapting an artificial intelligence emotion expression recognition software for emotional management in the educational context. British Journal of Educational Technology, 54(6), 1939–1963. https://doi.org/10.1111/bjet.13326
  • Gao, P., Li, J., & Liu, S. (2021). An Introduction to Key Technology in Artificial Intelligence and big Data Driven e-Learning and e-Education. Mobile Networks and Applications, 26(5), 2123–2126. https://doi.org/10.1007/s11036-021-01777-7
  • González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial Intelligence for Student Assessment: A Systematic Review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/app11125467
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press.
  • Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education. Mathematical Problems in Engineering, 2022, 1–19. https://doi.org/10.1155/2022/5215722
  • Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684
  • Ingavelez-Guerra, P., Robles-Bykbaev, V. E., Perez-Munoz, A., Hilera-Gonzalez, J., & Oton-Tortosa, S. (2022). Automatic Adaptation of Open Educational Resources: An Approach From a Multilevel Methodology Based on Students’ Preferences, Educational Special Needs, Artificial Intelligence and Accessibility Metadata. IEEE Access, 10, 9703–9716. https://doi.org/10.1109/ACCESS.2021.3139537
  • Jain, G. P., Gurupur, V. P., Schroeder, J. L., & Faulkenberry, E. D. (2014). Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student’s Understanding of a Topic. IEEE Transactions on Learning Technologies, 7(3), 267–279. https://doi.org/10.1109/TLT.2014.2330297
  • Jones, K. M. L. (2019). Learning analytics and higher education: A proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. International Journal of Educational Technology in Higher Education, 16(1), 24. https://doi.org/10.1186/s41239-019-0155-0
  • Jurs, P., Kulberga, I., Zupa, U., Titrek, O., & Špehte, E. (2023). Efficient Management of School and Teachers’ Professional Development – challenges and Development Perspectives. Pegem Journal of Education and Instruction, Vol. 13, No. 2, 2023, 112-118
  • Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M. D., Păun, D., & Mihoreanu, L. (2021). Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions. Sustainability, 13(18), 10424. https://doi.org/10.3390/su131810424
  • Lameras, P., & Arnab, S. (2021). Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education. Information, 13(1), 14. https://doi.org/10.3390/info13010014
  • Li, J., Xiao, W., & Zhang, C. (2023). Data security crisis in universities: Identification of key factors affecting data breach incidents. Humanities and Social Sciences Communications, 10(1), 270. https://doi.org/10.1057/s41599-023-01757-0
  • Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, 23(1), 18. https://doi.org/10.3390/e23010018
  • Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J. H., Ogata, H., Baltes, J., Guerra, R., Li, P., & Tsai, C.-C. (2020). Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Frontiers in Psychology, 11, 580820. https://doi.org/10.3389/fpsyg.2020.580820
  • Maghsudi, S., Lan, A., Xu, J., & Van Der Schaar, M. (2021). Personalized Education in the Artificial Intelligence Era: What to Expect Next. IEEE Signal Processing Magazine, 38(3), 37–50. https://doi.org/10.1109/MSP.2021.3055032
  • Mehigan, T. (2020). Towards Intelligent Education: Developments in Artificial Intelligence for Accessibility and Inclusion for all Students. 539–547. https://doi.org/10.21125/iceri.2020.0169
  • Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
  • Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020
  • Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. https://doi.org/10.1007/s10639-022-10925-9
  • Owoc, M. L., Sawicka, A., & Weichbroth, P. (2021). Artificial Intelligence Technologies in Education: Benefits, Challenges and Strategies of Implementation. In M. L. Owoc & M. Pondel (Eds.), Artificial Intelligence for Knowledge Management (Vol. 599, pp. 37–58). Springer International Publishing. https://doi.org/10.1007/978-3-030-85001-2_4
  • Paiva, R., & Bittencourt, I. I. (2020). Helping Teachers Help Their Students: A Human-AI Hybrid Approach. In I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millán (Eds.), Artificial Intelligence in Education (Vol. 12163, pp. 448–459). Springer International Publishing. https://doi.org/10.1007/978-3-030-52237-7_36
  • Panjwani-Charania, S. & Zhai, X., AI for Students with Learning Disabilities: A Systematic Review (October 30, 2023). Panjwani-Charani, S. & Zhai, X. (in press). AI for Students with Learning Disabilities: A Systematic Review. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xx-xx). Oxford, UK: Oxford University Press. , Available at SSRN: https://ssrn.com/abstract=4617715
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There are 57 citations in total.

Details

Primary Language English
Subjects Educational Technology and Computing
Journal Section Research Article
Authors

Attila Balogh 0009-0001-1078-6874

Early Pub Date December 9, 2024
Publication Date
Submission Date August 5, 2024
Acceptance Date December 5, 2024
Published in Issue Year 2024 Volume: 10 Issue: 2

Cite

APA Balogh, A. (2024). Educational Innovation of Using Artificial Intelligence in University Education: A Comprehensive Student Survey. International Journal on Lifelong Education and Leadership, 10(2), 55-65. https://doi.org/10.25233/ijlel.1528746