Cited by 2Artificial Intelligence in EducationEducational Technology and PedagogyEducational Innovations and Challenges

Effectiveness of AI in Enhancing Quality Higher Education: A Survey Study

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-, Dr. M. Krishnakumar, -, K.Balasubramanian·2024·International Journal For Multidisciplinary Research

Abstract

The integration of artificial intelligence (AI) in higher education has gained significant attention recently, with the potential to transform the learning experience and enhance educational quality. This study investigates the effectiveness of AI in enhancing the quality of higher education from the perspective of stakeholders, including students, faculty, and administrators. A survey method was employed to gather data on the perceived impact of AI on various aspects of higher education, such as teaching and learning, assessment, and administrative processes. The study examined the influence of factors such as gender, age, locality, and type of management on the perceptions of AI's effectiveness. The findings provide valuable insights into the current state of AI adoption in higher education and offer recommendations for optimizing its implementation to improve educational quality.

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Crossref · 10.36948/ijfmr.2024.v06i04.23833

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Research works printed but not linked: 6

  1. Brill, T. M., & Galloway, C. (2007). Integrating AI into education: A meta-study. Journal of Educational Technology Systems, 36(1), 5-27. https://doi.org/10.2190/ET.36.1.b · DOI
  2. Almutiry, O., Forsythe, H., & Wake, J. D. (2022). Artificial intelligence and digital education: An exploratory review of K-12 and higher education contexts. Education and Information Technologies, 27(1), 1079-1104. https://doi.org/10.1007/s10639-021-10632-w · DOI
  3. Chassignol, M. (2021). Artificial intelligence in higher education: A literature review. AI and Ethics, 1(4), 441-455. https://doi.org/10.1007/s43681-021-00065-5 · DOI
  4. Popenici, S. A. D., & Kerr, S. (2022). Exploring the impact of artificial intelligence on higher education: A systematic literature review. Educational Research Review, 36, 100455. https://doi.org/10.1016/j.edurev.2022.100455 · DOI · Paper page
  5. Zawacki-Richter, O., Baer, M., Kramer, V., Krämer, N. C., Kiili, K., & Vlachopoulos, D. (2022). Artificial intelligence and higher education: A bibliometric analysis. International Review of Education, 68(2), 243-270. https://doi.org/10.1007/s11159-022-09950-2 · DOI
  6. Zhai, X. (2021). Artificial intelligence in education: From myth to reality. In K. G. Li, T. L. Wong, S. K. S. Cheung, J. Lam, & K. K. Ng (Eds.), Technology in education: Pedagogic practices and the new normal (pp. 21-41). Springer. https://doi.org/10.1007/978-981-16-1005-3_2 · DOI

Authors
-, D. M. K., & -, K. B.
Affiliations
Amrita Vishwa Vidyapeetham (IN)
Venue
International Journal For Multidisciplinary Research
Published
2024-07-03
Volume
6
Issue
4
ISSN
2582-2160 (ISSN-L)
OpenAlex
W4400389821
Cited by
2
References
1
Citations by year
2025: 2
FWCI
0.75
Type
journal article
Language
en
Open access
Licence
cc-by-sa
Locations
International Journal For Multidisciplinary Research (publishedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
  • Physical Sciences › Computer Science › Information Systems › Educational Innovations and Challenges
Keywords
artificial intelligence in higher education, educational quality, stakeholder perspectives, artificial intelligence adoption, teaching-learning process, assessment, administrative processes, gender differences, age differences, institutional type

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