Understanding Researchers’ AI Readiness in a Higher Education Context: Q Methodology Research
Open accessAbstract
Taking a human-centered socio-cultural perspective, this study explored the manifold individual and structural processes that contribute to researchers’ AI readiness. Forty-three graduate students and faculty at one university in Qatar took part in this Q methodology study. The results represented participants’ collective perspectives on what they considered relevant to their AI readiness. A 5 + 1-factor solution was accepted, illustrating diverse perspectives and no consensus. The factors were termed based on their main foci, as follows, (F-1) how technical skills are acquired, (F-2) when it is all about ethics, (F-3) when technical skills meet ethical considerations, (F-4a and F-4b) when opposites concede, and (F-5) how collaborations reflect AI readiness. The results revealed the diversity of viewpoints among participants, and the interrelations among some factors. This study recommended a holistic approach to enhance AI readiness. It suggested integrating targeted educational initiatives and developing localized ethical frameworks to promote responsible AI use across various research disciplines.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Coaching models pair faculty with instructional designers or AI-literate mentors who offer personalised support in redesigning activities or troubleshooting implementation challenges.
“Coaching models pair faculty with instructional designers or AI-literate mentors who can offer personalised support in redesigning activities or troubleshooting implementation challenges (Chaaban et al., 2024; Sarıkahya et al., 2025).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Chaaban et al., 2024
Cites
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · see citing works
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · see citing works
A comprehensive AI policy education framework for university teaching and learning
Chan, C. K. Y. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00408-3 · Cited by 1,577 · see citing works
Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100127 · Cited by 647 · see citing works
AI and science: what 1,600 researchers think
Van Noorden, R., & Perkel, J. M. (2023) · Nature · doi:10.1038/d41586-023-02980-0 · Cited by 383 · see citing works
Karaca, O., Çalışkan, S. A., & Demir, K. (2021) · BMC Medical Education · doi:10.1186/s12909-021-02546-6 · Cited by 377 · see citing works
Wang, X., Li, L., Tan, S. C., Yang, L., & Lei, J. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107798 · Cited by 336 · see citing works
Using AI to write scholarly publications
Hosseini, M., Rasmussen, L. M., & Resnik, D. B. (2024) · Accountability in Research · doi:10.1080/08989621.2023.2168535 · Cited by 286 · see citing works
ENAI Recommendations on the ethical use of Artificial Intelligence in Education
Foltynek, T., Bjelobaba, S., Glendinning, I., Khan, Z. R., Santos, R., Pavletic, P., & Kravjar, J. (2023) · International Journal for Educational Integrity · doi:10.1007/s40979-023-00133-4 · Cited by 180 · see citing works
How ChatGPT and other AI tools could disrupt scientific publishing
Conroy, G. (2023) · Nature · doi:10.1038/d41586-023-03144-w · Cited by 121 · see citing works
Qadhi, S. M., Alduais, A., Chaaban, Y., & Khraisheh, M. (2024) · Information · doi:10.3390/info15060325 · Cited by 106 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Alduais, A., Qadhi, S., Chaaban, Y., & Khraisheh, M. (2025) · Serials Review · doi:10.1080/00987913.2025.2581429 · Cited by 37 · see citing works
Self-citationJayasinghe, S., Gamage, K. A. A., Yang, D., Cheng, C., Disanayake, C., & Apeji, U. D. (2026) · Education Sciences · doi:10.3390/educsci16010137 · Cited by 23 · see citing works
- Authors
- Chaaban, Y., Qadhi, S., Chen, J., & Du, X.
- Affiliations
- Qatar University (QA)
- Aalborg University (DK)
- Corresponding author
- Chaaban, Youmen
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2024-06-29
- Volume
- 14
- Issue
- 7
- Pages
- 709
- ISSN
- 2227-7102 (ISSN-L)
- OpenAlex
- W4400237624
- Cited by
- 19
- References
- 13
- Citations by year
- 2026: 12 · 2025: 4 · 2024: 2 · 2022: 1
- FWCI
- 6.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Education Sciences (publishedVersion, cc-by)
- VBN Forskningsportal (Aalborg Universitet) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- Qatar Research and Development Innovation Council QRDI (ARG01-0516-230177)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Management Science and Operations Research › Q Methodology Applications
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- AI readiness, researchers, higher education, graduate students, faculty, technical skills, AI ethics, responsible AI use, collaboration, human-centered perspective
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 ›
Are you an author, or do you see a problem with this page? Request a review
