Artificial Intelligence in Higher Education: Enhancing Learning Systems and Transforming Educational Paradigms
Open accessAbstract
This study ventures into the expanding role of artificial intelligence (AI) in higher education and its potential to revolutionize the learning experience. Through a qualitative approach, we investigate how advanced AI-driven educational tools can enhance learning systems through tailored learning experiences, AI-powered adaptive tutoring platforms, immersive digital learning environments, and AI-assisted assessment and feedback systems. From a theoretical perspective, we utilize the constructivist theory of learning as a study framework to explain the role of technology in AI-facilitated personalized and collaborative knowledge construction. This study was executed in three stages. First, we present a brief overview of AI and its recent impact on the education system, including its ethical considerations and implementation hurdles. In the second stage, we propose the potential implications for learning contexts through the constructivist lens, the process of AI-supported cognitive development, and the use of AI tools for optimizing educational processes through AI and transformation in educational paradigms and study work. The findings suggest that institutions can unlock new opportunities for efficiency, accessibility, and personalized learning by adopting AI technology, but they also highlight the need to address ethical considerations and implementation hurdles. This study also contributes to future studies by examining the impact of AI on student outcomes, teacher roles, and institutional practices in education, as well as exploring ethical and equity concerns in AI-driven educational settings.
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The omission of formal quality appraisal reflects a broader trend in recent AI-in-education syntheses, where conclusions have sometimes been distorted due to over-reliance on descriptive synthesis without evaluating study credibility.
“This omission reflects a broader trend in recent AI-in-education syntheses, where conclusions have sometimes been distorted due to over-reliance on descriptive synthesis without evaluating study credibility.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [37]
The Methodology and Theoretical Framework describes this paper as a qualitative descriptive study, and §2.1 says it reviews blogs, interviews, policy documents, and literature. Neither section evaluates a broader trend of omitted quality appraisal in AI-in-education syntheses or says that descriptive synthesis without credibility assessment has distorted conclusions. Searches for quality appraisal, study credibility, and synthesis bias found no passage making that connection; the copy provides no page numbers. (LLM, conf=0.76)
Experimental — accuracy not yet measuredFailure to consider study quality can lead to overstated findings or the misinterpretation of AI's effectiveness.
“As recent critiques have noted, failure to consider study quality can lead to overstated findings or the misinterpretation of AI's effectiveness [37].”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [37]
The Introduction discusses the need for sound pedagogical foundations, and section 2.1 describes a review of literature and other sources. Neither addresses whether failing to assess study quality overstates findings or misrepresents AI's effectiveness. Searches for study quality, methodological rigor, and overstated findings found no passage making that point; page numbers were not available in our copy. (LLM, conf=0.74)
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Research works printed but not linked: 11
- H. Niemi, "AI in learning: Preparing grounds for future learning," Journal of Pacific Rim Psychology, vol. 15, 2021. https://doi.org/10.1177/18344909211038105 · DOI
- D. Papadopoulos and M. M. Hossain, "Education in the age of analytics: maximizing student success through big data-driven personalized learning," Emerging Trends in Machine Intelligence and Big Data, vol. 15, no. 9, pp. 20-36, 2023. http://orientreview. com/index.php/etmibd-journal/article/view/19
- S. Grubaugh, G. Levitt, and D. Deever, "Harnessing AI to power constructivist learning: An evolution in educational methodologies," EIKI Journal of Effective Teaching Methods, vol. 1, no. 3, 2023. https://doi.org/10.59652/jetm.v1i3.43 · DOI
- D. H. Schunk, "Learning theories an educational perspective," Pearson Education, Inc. Boston, 2012.
- O. R. Arogundade, "Structuring knowledge bases with AI and machine learning," Information and Knowledge Management, vol. 13, no. 1, pp. 27-35, 2023. https://iiste.org/ Journals/index.php/IKM/article/view/60279
- A. Gull, M. Imran, M. Yiunas, and M. Afzaal, "A descriptive study of challenges faced by english language teachers in integrating information and communication technology (ICT) tools at elementary level in Pakistan," International Journal of Advanced Science and Technology, vol. 29, no. 8s, pp. 290-305, 2020. http://sersc.org/journals/index.php/IJAST/ article/view/10504
- E. Hannan and S. Liu, "AI: New source of competitiveness in higher education," Competitiveness Review, vol. 33 no. 2, pp. 265-279, 2023. https://doi.org/10.1108/ CR-03-2021-0045 · DOI · Paper page
- H. Furey and F. Martin, "AI education matters: A modular approach to AI ethics educa- tion," AI Matters, vol. 4, no. 4, pp. 13-15, 2019. https://doi.org/10.1145/3299758.3299764 · DOI
- R. Fadli et al., "Effectiveness of mobile virtual laboratory based on project-based learning to build constructivism thinking," International Journal of Interactive Mobile Technologies, vol. 34, no. 2, pp. 814-824, 2024. https://doi.org/10.11591/ijeecs.v34.i2.pp814-824 · DOI
- M. Afzaal, S. Ahmad, M. Imran, and D. Xiangtaoc, "Artificial intelligence, context, and meaning making in language: A rationalization approach," International Journal of Future Generation Communication and Networking, vol. 13, no. 3, pp. 115-122, 2020. https:// sersc.org/journals/index.php/IJFGCN/article/view/17094
- M. Jabeen, "Exploring ChatGPT's role in creative writing: A short review," International Review of Literary Studies, vol. 5, no. 2, pp. 32-34, 2023. https://irlsjournal.com/index. php/Irls/article/view/51
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Influential
- Authors
- Imran, M., Almusharraf, N., Abdellatif, M. S., & Abbasova, M. Y.
- Affiliations
- Prince Sultan University (SA)
- Prince Sattam Bin Abdulaziz University (SA)
- Khazar University (AZ)
- Venue
- International Journal of Interactive Mobile Technologies (iJIM)
- Published
- 2024-09-24
- Volume
- 18
- Issue
- 18
- Pages
- 34-48
- ISSN
- 1865-7923 (ISSN-L)
- OpenAlex
- W4402765726
- Cited by
- 51
- References
- 0
- Citations by year
- 2026: 23 · 2025: 27 · 2024: 1
- FWCI
- 19.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Interactive Mobile Technologies (iJIM) (publishedVersion, cc-by)
- Khazar University Institutional Repository (Khazar University) (submittedVersion)
- Funders
- Prince Sattam bin Abdulaziz University (PSAU/ 2024 / R / 1445)
- Prince Sultan University
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- artificial intelligence in education, higher education, adaptive tutoring, personalized learning, constructivist learning theory, AI-assisted assessment, feedback systems, immersive learning environments, AI ethics in education, implementation barriers, cognitive development, educational equity, teacher role
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