Integrating generative AI in university teaching and learning: A model for balanced guidelines
Open accessAbstract
The study proposes a balanced approach and flexible guidelines for incorporating generative artificial intelligence (AI) into university-level teaching and learning processes at both the university-departmental level and within individual academic autonomy. Building on Chan’s (2023) AI Ecological Education Policy Framework, the guidelines offer a suggestive frame of reference for faculty and students to integrate generative AI into their coursework. Furthermore, feedback from 118 students and 14 academics at a teacher education institution in the Philippines underscores the guidelines' potential benefits, concerns, usefulness, and necessity in their academic undertakings. While the policy may not cover every detail exhaustively, it seeks to provide practical and context-sensitive recommendations for ethical, honest, responsible, and fair use of AI in course development, implementation, and student engagement. Consequently, other higher education institutions in general, and academics in particular, may adopt and/or modify the guidelines to suit their positions, goals, needs, and directions.
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Communities of practice create spaces for peer-to-peer learning, interdisciplinary dialogue, and co-construction of best practices.
“communities of practice create spaces for peer-to-peer learning, interdisciplinary dialogue, and the co-construction of best practices (Barus et al., 2025; Bearman & Ajjawi, 2023; Cacho, 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 Cacho, 2024
The GUIDEPOSTS section proposes a multidisciplinary body that runs upskilling activities and develops AI guidelines in consultation with stakeholders; the Methodology section describes faculty discussion and feedback on those guidelines. These passages support collaborative, cross-disciplinary guideline development, but they do not describe communities of practice or establish peer-to-peer learning and co-construction of best practices as their functions. No page numbers were available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredIncreased reliance on GenAI can lead to a decline in critical thinking, as users may unconsciously trade deep cognitive engagement for convenience and speed in acquiring information.
“Increased reliance on GenAI can lead to a decline in critical thinking, as users may unconsciously trade deep cognitive engagement for convenience and speed in acquiring information.”
From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Cacho, 2024
In Students’ Feedback (page unavailable), the paper reports a student’s concern that AI may induce mental dependence and impair critical thinking, and describes reliance that could diminish students’ critical-thinking skills. Its theme table (page unavailable) also identifies intellectual laziness and dependence. The copy does not establish the more specific explanation that users unconsciously sacrifice deep engagement for convenience and speed in acquiring information. (LLM, conf=0.83)
Experimental — accuracy not yet measuredFaculty-student collaboration reflects a shift towards co-constructive educational practices, where students are active participants in shaping AI integration.
“Faculty-student collaboration reflects a shift towards co-constructive educational practices, where students are not merely recipients but active participants in shaping how AI tools are integrated into their learning environments (Cacho, 2024; Gupta & Jaiswal, 2024; Jayasinghe et al., 2025; Mariyam & Karthika, 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 Cacho, 2024
If students become overly reliant on AI, their critical thinking and originality may erode.
“If students become overly reliant on AI, their critical thinking and originality may erode.”
From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Cacho, 2024
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Research works printed but not linked: 1
- Cacho, R. M. (2014). TPCK assessment of pre-service teachers toward enhancing teacher educators' modeling. Asian Journal of Education and E-Learning, 2(5). https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=12a5cf504b5004881f6 cca7ccc2dab155ae1643e
Cited by
Sorted by citations
Jayasinghe, 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
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- Authors
- Cacho, R.
- Affiliations
- Philippine Normal University (PH)
- Corresponding author
- Cacho, Reynald
- ORCID
- Cacho, Reynald
- Venue
- Online Learning
- Publisher
- The Online Learning Consortium
- Published
- 2024-09-01
- Volume
- 28
- Issue
- 3
- ISSN
- 1092-8235 (ISSN-L)
- 2472-5730
- 2472-5749
- OpenAlex
- W4402118366
- Cited by
- 59
- References
- 40
- Citations by year
- 2026: 28 · 2025: 30 · 2024: 1
- FWCI
- 22.25
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Online Learning (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- generative AI, artificial intelligence in higher education, higher education teaching and learning, AI guidelines, responsible AI use, academic autonomy, teacher professional development, student engagement, ethical AI use, course development
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