Unveiling the Potential: Experts' Perspectives on Artificial Intelligence Integration in Higher Education
This article investigates artificial intelligence (AI) implementation in higher education (HE) from experts' perspectives. It emphasises the view .
- Pub. date: October 15, 2024
- Online Pub. date: May 25, 2024
- Pages: 1477-1492
- 56 Downloads
- 173 Views
- 0 Citations
This article investigates artificial intelligence (AI) implementation in higher education (HE) from experts' perspectives. It emphasises the view of AI's involvement in administrative activities in higher education, experts' opinions concerning the influence of the incorporation of AI on learning and teaching, and experts' views on applying AI specifically to assessment, academic integrity, and ethical considerations. The study used a qualitative method based on an unstructured qualitative interview with open-ended questions. The participants were thirteen individuals currently involved with higher education institutions and had various talents related to AI and education. Findings stress that implementing AI technology in administrative roles within higher education institutions is essential since it cuts costs, addresses problems efficiently and effectively, and saves time. The findings also revealed that AI plays a vital role in learning and teaching by speeding up the learning process, engaging learners and tutors, and personalising learning depending on the learner's needs within an entirely intelligent environment. AI can produce an accurate, objective, and suitable level of assessment. AI aids students in developing a stronger sense of integrity in their academic work by guiding them through AI-powered applications. AI must adhere to ethical laws and policies, ensuring its potential negative aspects are not overlooked or left unchecked.
Keywords: AI and education administration, AI and education ethics, AI education experts, AI in higher education.
References
Ahajjam, T., Moutaib, M., Aissa, H., Azrour, M., Farhaoui, Y., & Fattah, M. (2022). Predicting students' final performance using artificial neural networks. Big Data Mining and Analytics, 5(4), 294-301. https://doi.org/10.26599/bdma.2021.9020030
Allen, B., McGough, A. S., & Devlin, M. (2021). Toward a framework for teaching artificial intelligence to a higher education audience. ACM Transactions on Computing Education, 22(2), Article 15. https://doi.org/10.1145/3485062
Antoniou, J., & Tringides, O. (2023). Effects of data overload on user quality of experience. Springer. https://doi.org/10.1007/978-3-031-06870-6
Bucea-Manea-Țoniş, R., Kuleto, V., Gudei, S. C. D., Lianu, C., Lianu, C., Ilić, M. P., & Păun, D. (2022). Artificial intelligence potential in higher education institutions enhanced the learning environment in Romania and Serbia. Sustainability, 14(10), Article 5842. https://doi.org/10.3390/su14105842
Çelik, İ. (2023). Towards intelligent-TPACK: An empirical study on teachers' professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, Article 107468. https://doi.org/10.1016/j.chb.2022.107468
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, Article 43. https://doi.org/10.1186/s41239-023-00411-8
Chauhan, R. S. (2022). Unstructured interviews: Are they all that bad? Human Resource Development International, 25(4), 474-487. https://doi.org/10.1080/13678868.2019.1603019
Elkhatat, A. M. (2022). Practical randomly selected question exam design to address replicated and sequential questions in online examinations. International Journal for Educational Integrity, 18, Article 8. https://doi.org/10.1007/s40979-022-00103-2
Golafshani, N. (2003). Understanding reliability and validity in qualitative research. The Qualitative Report, 8(4) 597-606. https://doi.org/10.46743/2160-3715/2003.1870
İçen, M. (2022). The future of education utilizing artificial intelligence in Turkey. Humanities and Social Sciences Communications, 9, Article 268. https://doi.org/10.1057/s41599-022-01284-4
James, M., Baptista, A. M. T., Barnabas, D., Sadza, A., Smith, S., Usmani, O., & John, C. (2022). Collaborative case-based learning with programmatic team-based assessment: a novel methodology for developing advanced skills in early-years medical students. BMC Medical Education, 22, Article 81. https://doi.org/10.1186/s12909-022-03111-5
Johnson, K. A., Bennett, J. E., Georgiou, N., Bradshaw, J. L., Chiu, E., Cunnington, R., & Iansek, R. (2000). Bimanual co-ordination in Huntington's disease. Experimental Brain Research, 134, 483-489. https://doi.org/10.1007/s002210000485
Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2022). Trustworthy artificial intelligence: a review. ACM Computing Surveys, 55(2), Article 39. https://doi.org/10.1145/3491209
Kirubarajan, A., Young, D., Khan, S., Crasto, N., Sobel, M., & Sussman, D. (2022). Artificial intelligence and surgical education: A systematic scoping review of interventions. Journal of Surgical Education, 79(2), 500-515. https://doi.org/10.1016/j.jsurg.2021.09.012
Lee, P.-J., & Wu, T.-Y. (2022). Mining relations between personality traits and learning styles. Information Processing and Management, 59(5), Article 103045. https://doi.org/10.1016/j.ipm.2022.103045
Lünich, M., Keller, B., & Marcinkowski, F. (2023). Fairness of academic performance prediction for the distribution of student support measures: Differences in perceived fairness of distributive justice norms. Technology, Knowledge, and Learning. Advance online publication. https://doi.org/10.1007/s10758-023-09698-y
Lyons, J. B., Hamdan, I. A., & Vo, T. Q. (2023). Explanations and trust: What happens to trust when a robot partner does something unexpected? Computers in Human Behavior, 138, Article 107473. https://doi.org/10.1016/j.chb.2022.107473
McLennan, S., Fiske, A., Tigard, D., Müller, R., Haddadin, S., & Buyx, A. (2022). Embedded ethics: a proposal for integrating ethics into the development of medical AI. BMC Medical Ethics, 23, Article 6. https://doi.org/10.1186/s12910-022-00746-3
Nguyen, T. T., Le, A. D., Hoang, H. T., & Nguyen, T. (2021). NEU-chatbot: Chatbot for admission to National Economics University. Computers and Education: Artificial Intelligence, 2, Article 100036. https://doi.org/10.1016/j.caeai.2021.100036
Ram, R. S., Kumar, M. V., Al-Shami, T. M., Masud, M., Aljuaid, H., & Abouhawwash, M. (2023). Deep fake detection using computer vision-based deep neural network with pairwise learning. Intelligent Automation and Soft Computing, 35(2), 2449-2462. https://doi.org/10.32604/iasc.2023.030486
Salas‐Pilco, S. Z., & Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: a systematic review. International Journal of Educational Technology in Higher Education, 19, Article 21. https://doi.org/10.1186/s41239-022-00326-w
Sarraf, D., Vasiliu, V., Imberman, B., & Lindeman, B. (2021). Use artificial intelligence for gender bias analysis in letters of recommendation for general surgery residency candidates. The American Journal of Surgery, 222(6), 1051-1059. https://doi.org/10.1016/j.amjsurg.2021.09.034
Schroeder, K. T., Hubertz, M., Van Campenhout, R., & Johnson, B. G. (2022). Teaching and learning with AI-generated courseware: Lessons from the classroom. Online Learning, 26(3), 73-87. https://doi.org/10.24059/olj.v26i3.3370
Slimi, Z., & Villarejo Carballido, B. (2023a). Navigating the ethical challenges of artificial intelligence in higher education: An analysis of seven global AI ethics policies. TEM Journal, 12(2), 590‑602. https://doi.org/10.18421/tem122-02
Slimi, Z., & Villarejo Carballido, B. (2023b). Systematic review: AI impacts higher education learning, teaching, and career opportunities. TEM Journal, 12(3), 1627-1637. https://doi.org/10.18421/tem123-44
Streefkerk, R. (2023, June 22). Qualitative vs quantitative research | differences, examples & methods. Scribbr. https://www.scribbr.com/methodology/qualitative-quantitative-research/
Tan, S. H. S., Thibault, G., Chew, A. C. Y., & Rajalingam, P. (2022). Enabling open‐ended questions in team‐based learning using automated marking: Impact on student achievement, learning and engagement. Journal of Computer Assisted Learning, 38(5), 1347-1359. https://doi.org/10.1111/jcal.12680
Verhoef, A. H., Fourie, M., Van Rensburg, Z. J., Louw, H., & Erasmus, M. (2022). Enhancing academic integrity through a community of practice at the North-West University, South Africa. International Journal for Educational Integrity, 18, Article 18. https://doi.org/10.1007/s40979-022-00115-y
Wang, H., & Zhang, Y. (2022). The effects of personality traits and attitudes towards the rule on academic dishonesty among university students. Scientific Reports, 12, Article 14181. https://doi.org/10.1038/s41598-022-18394-3