Developing postgraduate students’ competencies in generative artificial intelligence for ethical integration into academic practices: A participatory action research
Open accessAbstract
Generative artificial intelligence (GenAI) has been integrated into everyday life, including education. Educational institutions cannot prevent their members from using this technology; however, they can develop awareness and competencies to apply GenAI ethically. This study aims to develop postgraduate students’ competencies in GenAI so that they can integrate it ethically into their academic practices. Employing a qualitative participatory action research (PAR) approach and drawing on Kolb’s Experiential Learning Theory (ELT), data were collected through focus groups, interviews, observations, student-generated materials, Telegram communications, and symposium data. Thematic analysis revealed that students developed several competencies that can be categorised into four key themes: technical and AI literacy, ethical and legal, critical thinking and analytical, and lifelong learning and interpersonal. The findings also showed how the PAR approach developed the students’ GenAI competencies. The discussion emphasises the importance of developing these competencies. Finally, implications and recommendations are provided to inform future higher education practices and research.
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Digital abundance does not automatically translate into informed judgment.
“Empirical research consistently shows that digital abundance does not automatically translate into informed judgment; rather, students must actively develop competencies to evaluate, verify, and ethically use information in academically responsible ways (Aladsani, 2025; Kunz et al., 2024; Molerov et al., 2020; Surducan and Valadas, 2025).”
From Information competence and critical thinking in digital environments evidence from university students in Kazakhstan (2026) · cites this paper as Aladsani, 2025
Students must actively develop competencies to evaluate, verify, and ethically use information in academically responsible ways.
“Empirical research consistently shows that digital abundance does not automatically translate into informed judgment; rather, students must actively develop competencies to evaluate, verify, and ethically use information in academically responsible ways (Aladsani, 2025; Kunz et al., 2024; Molerov et al., 2020; Surducan and Valadas, 2025).”
From Information competence and critical thinking in digital environments evidence from university students in Kazakhstan (2026) · cites this paper as Aladsani, 2025
Oral examinations create opportunities for real-time interaction and spontaneous reasoning, making them resistant to AI impersonation.
“Oral examinations, meanwhile, create opportunities for real-time interaction and spontaneous reasoning, making them resistant to AI impersonation (Aladsani, 2025; Niloy et al., 2024; Ou et al., 2024).”
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 Aladsani, 2025
The Data collection methods section describes interviews, presentations, and assignment discussions, not oral examinations. Searches for oral examinations, spontaneous reasoning, and AI impersonation found no passage in our copy assessing whether oral examinations resist AI impersonation. No page numbers were available. (LLM, conf=0.76)
Experimental — accuracy not yet measuredPortfolio-based assessments encourage students to document their learning journey across multiple stages, often requiring metacognitive reflection and evidence of process rather than just final products.
“Portfolio-based assessments encourage students to document their learning journey across multiple stages, often requiring metacognitive reflection and evidence of process rather than just final products (Aladsani, 2025; An et al., 2025; Jin 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 Aladsani, 2025
The Data collection methods section describes weekly reflection papers, and the First cycle section describes students reflecting on what they learned. But our copy does not discuss portfolio-based assessments or say that they require evidence of learning across stages rather than only final products (searched: portfolio, portfolio-based assessments, evidence of process). (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Aladsani, H. K.
- Affiliations
- King Faisal University (SA)
- Corresponding author
- Aladsani, Hibah Khalid
- Venue
- Interactive Learning Environments
- Publisher
- Informa UK Limited
- Published
- 2025-04-07
- Volume
- 33
- Issue
- 10
- Pages
- 5747-5765
- ISSN
- 1049-4820 (ISSN-L)
- 1744-5191
- OpenAlex
- W4409210388
- Semantic Scholar
- 0753ee029537b4748c9c906cf03e2ef893c8bc7f
- Cited by
- 33
- References
- 25
- Citations by year
- 2026: 29 · 2025: 4
- FWCI
- 33.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Interactive Learning Environments (publishedVersion, cc-by-nc-nd)
- Funders
- Deanship of Scientific Research at King Faisal University (KFU242963)
- 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 › Information Systems › Digital literacy in education
- Keywords
- generative AI, graduate students, AI competencies, ethical use of AI, AI literacy, Kolb's experiential learning theory, participatory action research, higher education, critical thinking, lifelong learning, legal awareness
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