Artificial Intelligence in Higher Education: Modelling the Antecedents of Artificial Intelligence Usage and Effects on 21st Century Employability Skills among Postgraduate Students in Ghana
Open accessAbstract
The utilisation of Artificial intelligence (AI) in higher education has been a contemporary issue attracting varied perspectives. However, the nuances among the antecedents of AI usage among postgraduate students and the acquisition of 21st-century employability skills are under-researched. This study sought to fill the chasm by adopting a concurrent integrated mixed method to collect data using a questionnaire with closed-ended and open-ended items from 294 postgraduate students in Ghana. Descriptive statistics and partial least squares structural equation modelling (PLS-SEM) techniques were employed for the quantitative data analyses while thematic pattern matching was used to analyse the qualitative data gathered. Results from the descriptive analysis revealed that the most prevalent AI platforms used by postgraduate students were ChartGPT and Quillbot. The two most important uses of such AI tools were just used for learning in general; and searching for literature. The PLS-SEM paths modelling showed that antecedents of postgraduate students' AI usage were benefits, challenges and platform availability. Finally, both AI usage and AI challenges were significant predictors of postgraduate students’ acquisition of 21stcentury employability skills. However, the relationship between AI challenges and the acquisition of 21st-century employability skills was fully mediated by AI usage. Based on the results, recommendations were made for policy and practice of AI usage in higher education.
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Non-native or students with language difficulties can take advantage of functions making materials available in various languages or presented in a more simplified way [52,57].
“Non-native or students with language difficulties can also take advantage of the new technologies' functions which make the materials available in various languages or presented in a more simplified way [52,57].”
From Impact of Artificial Intelligence and Virtual Reality on Educational Inclusion: A Systematic Review of Technologies Supporting Students with Disabilities (2024) · cites this paper as [57]
Segbenya et al. [57] dealt with the issue of overreliance on assistive technologies, showcasing students' lack of critical thinking to process and manage new information due to the simplicity of accessing huge numbers of information in a short period.
“Segbenya et al. [57] in their research dealt with the issue of overreliance on these assistive technologies, showcasing the students' lack of critical thinking to process and manage new information, due to the simplicity of accessing and reaching huge numbers of information in a short period of time.”
From Impact of Artificial Intelligence and Virtual Reality on Educational Inclusion: A Systematic Review of Technologies Supporting Students with Disabilities (2024) · cites this paper as [57]
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- Authors
- Segbenya, M., Bervell, B., Frimpong-Manso, E., Otoo, I. C., Andzie, T. A., & Achina, S.
- Affiliations
- University of Cape Coast (GH)
- Corresponding author
- Segbenya, Moses
- Venue
- Computers and Education Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 5
- Pages
- 100188
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4389396699
- Cited by
- 52
- References
- 43
- Citations by year
- 2026: 17 · 2025: 27 · 2024: 8
- FWCI
- 12.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Higher Education and Employability
- Keywords
- artificial intelligence in higher education, graduate students, AI challenges, benefits of artificial intelligence, QuillBot, AI tools in learning, literature searching, Ghana
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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