Synergizing Software Engineering and AI: A Sustainable Intelligent Model for Automating Higher Education Quality Assurance Auditing
DOI:
https://doi.org/10.59421/joeats.v3i1.2479Keywords:
Artificial intelligence, software architecture design, quality assurance, auditing, natural language processing, deep learningAbstract
Higher education quality assurance is a very important task for improving the quality of education, which needs regular auditing. This auditing process requires a lot of manual work to evaluate the higher education institutions against quality assurance standards, guides, and forms. Therefore, this paper aims to propose a new AI-based model to automate the auditing process and to ensure as accurate results as possible when needed. This model utilizes artificial intelligence and software engineering. The software architecture of the proposed model consists of three layers that systematically interact with each other to achieve the model goal.
References
Xu, Y., et al., Artificial intelligence: A powerful paradigm for scientific research. 2021. 2(4).
Dwivedi, Y.K., et al., Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. 2021. 57: p. 101994.
Ocaña-Fernández, Y., L.A. Valenzuela-Fernández, and L.L.J.J.o.E.P.-P.y.R. Garro-Aburto, Artificial Intelligence and Its Implications in Higher Education. 2019. 7(2): p. 553-568.
Elviwani, M.Z., A. Dilham, and R.J.I.J.S.R.C.S.E.I.T. Buaton, Higher Education Quality Assurance System Based Artificial Intelligence. 2020. 3307: p. 274-279.
Zawacki-Richter, O., et al., Systematic review of research on artificial intelligence applications in higher education–where are the educators? 2019. 16(1): p. 1-27.
Van Vliet, H., H. Van Vliet, and J. Van Vliet, Software engineering: principles and practice. Vol. 13. 2008: John Wiley & Sons Hoboken, NJ.
Javed, Y. and M. Alenezi, A Case Study on Sustainable Quality Assurance in Higher Education. 2023. 15(10): p. 8136.
Noordin, N.A., K. Hussainey, and A.F. Hayek, The Use of Artificial Intelligence and Audit Quality: An Analysis from the Perspectives of External Auditors in the UAE. 2022. 15(8): p. 339.
Yogish, D., T. Manjunath, and R.S. Hegadi. Review on natural language processing trends and techniques using NLTK. in Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part III 2. 2019. Springer.
Abd El-Haleem, A.M., et al., A Generic AI-Based Technique for Assessing Student Performance in Conducting Online Virtual and Remote Controlled Laboratories. 2022. 10: p. 128046-128065.
I, P.D., European Middle Market Report 2H 2015. 20215.
I, P.D., U.S. Middle market report Q4 2015. Technical report. 2015.
Alasmari, J. S. . (2023). The Dynamics of Verbal and Non-Verbal Linguistic Communication in The Saudi Sports Community. Arts for Linguistic & Literary Studies, 5(4), 539–569. https://doi.org/10.53286/arts.v5i4.1676
Ahmed, M. R. A. (2025). Accreditation and Quality Assurance: Exploring Impact and Assessing Institutional Change in the US and Saudi Arabian Higher Education Institutions. Arts for Linguistic & Literary Studies, 7(1), 626–639. https://doi.org/10.53286/arts.v7i1.2419
Al-Ghobesi, A. A. H. (2025). Risks of Relying on Artificial Intelligence in Learning Arabic Language Sciences Through the Meta Application. Arts for Linguistic & Literary Studies, 7(1), 396–419. https://doi.org/10.53286/arts.v7i1.2420
Omer, N. I. M. (2024). Maintaining Meaningful Human Interaction in AI-Enhanced Language Learning Environments: A Systematic Review. Arts for Linguistic & Literary Studies, 6(3), 533–552. https://doi.org/10.53286/arts.v6i3.2083
