Generative AI in Higher Education: Teachers’ and Students’ Perspectives on Support, Replacement, and Digital Literacy
Open accessAbstract
Artificial intelligence (AI) is increasingly shaping diverse sectors, including education, sparking debates about its potential to transform pedagogical practices and redefine the role of educators. This study explores the perceptions and applications of generative AI in Moroccan higher education to better understand its implications for teaching and learning. A mixed-methods approach was adopted, combining quantitative data from 130 teachers and 156 students with qualitative insights. Quantitative findings reveal significant differences: students demonstrate greater openness to adopting AI, appreciating its capacity to provide instant feedback, enhance creativity, and improve academic performance. In contrast, teachers express reservations, particularly regarding AI’s potential to undermine critical soft skills such as collaboration, problem-solving, and critical thinking. Qualitative analyses confirm these trends, highlighting that, while AI is perceived as a valuable complementary tool, it cannot replace the essential human role of educators in providing personalized guidance and addressing students’ emotional and cognitive needs. Both groups agree on the necessity of enhanced digital literacy to ensure ethical and effective AI integration. These findings underscore the opportunities of generative AI, such as personalized learning and efficiency, while addressing limitations like ethical concerns and over-reliance, offering actionable insights for policymakers, educators,…
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?
A notable divergence between faculty and student perspectives was noted, with students exhibiting greater openness and positive attitudes toward AI integration.
“A notable divergence between faculty and student perspectives was noted, with students exhibiting greater openness and positive attitudes toward AI integration (Haroud & Saqri, 2025).”
From An integrated AI literacy framework for higher‑education teachers: a critical review and conceptual synthesis (2026) · cites this paper as Haroud & Saqri, 2025
There were disparate reports on whether AI was perceived as a threat to teacher autonomy because it lacks affective and creative capacities, compared to arguments that AI will not replace human teachers but rather provide time efficiencies for other tasks.
“There were disparate reports on whether AI was perceived as a threat to teacher autonomy because it lacks affective and creative capacities, compared to arguments that AI will not replace human teachers but rather provide time efficiencies for other tasks (Chan & Tsi, 2024;Haroud & Saqri, 2025;Khalil & Alsenaidi, 2024).”
From An integrated AI literacy framework for higher‑education teachers: a critical review and conceptual synthesis (2026) · cites this paper as Haroud & Saqri, 2025
A significant anxiety existed regarding cognitive offloading and epistemic authority.
“The research indicated that a significant anxiety existed regarding cognitive offloading and epistemic authority (Chan & Tsi, 2024;Haroud & Saqri, 2025).”
From An integrated AI literacy framework for higher‑education teachers: a critical review and conceptual synthesis (2026) · cites this paper as Haroud & Saqri, 2025
Educators are introducing AI ethics education to address bias, transparency, and accountability in algorithmic decision-making.
“educators are introducing AI ethics education to address issues such as bias, transparency, and accountability in algorithmic decision-making (Bikanga Ada, 2024; Haroud & Saqri, 2025; Valdivieso & González, 2025; Z. Wang 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 Haroud & Saqri, 2025
The literature strongly supports the shift toward authentic assessments-tasks that are complex, context-rich, and reflective of real-world scenarios-as a way to make AI-generated responses less relevant or useful.
“The literature strongly supports the shift toward authentic assessments, tasks that are complex, context-rich, and reflective of real-world scenarios as a way to make AI-generated responses less relevant or useful (Chan & Zhou, 2023; Haroud & Saqri, 2025; Ozfidan 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 Haroud & Saqri, 2025
Cites
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · cited 2× · see citing works
“Ethical concerns have emerged surrounding tools like ChatGPT (ChatGPT-4), which may produce inaccurate or inappropriate outputs and unintentionally encourage practices such as plagia-rism (Chen et al., 2020).”
What is AI Literacy? Competencies and Design Considerations
Long, D., & Magerko, B. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376727 · Cited by 3,241 · cited 1× · see citing works
“According to Long and Magerko (2020), these competencies include the ability to interpret AI-generated content, recognize algorithmic biases, and assess the reliability of AI outputs.”
Will generative AI replace teachers in higher education? A study of teacher and student perceptions
Chan, C. K. Y., & Tsi, L. H. Y. (2024) · Studies in Educational Evaluation · doi:10.1016/j.stueduc.2024.101395 · Cited by 387 · cited 1× · see citing works
“Similarly, Chan and Tsi (2024) characterize students as “active problem solvers” and “independent learners” (Marshall & Wolanskyj-Spinner, 2020), enabling them to leverage generative AI tools to enhance their autonomy and customize their educational experiences.”
Flagged first, then by citations
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
Soleimani, S., Farrokhnia, M., van Dijk, A., & Noroozi, O. (2025) · Innovations in Education and Teaching International · doi:10.1080/14703297.2025.2530767 · Cited by 8 · see citing works
Williams, A. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2698137 · Cited by 2 · see citing works
Threadgill, B., Paudel, S., Bista, K., Gaulee, U., Prime, G., Momoh, V., Anderson, P. C., Gole, R., Sharma, S., Mandal, U., et al. (2026) · Journal of Interdisciplinary Studies in Education · doi:10.32674/w76bnk04 · Cited by 1 · see citing works
“Students in multiple studies expressed anxiety that heavy reliance on AI might undermine respect for teachers, especially if AI is perceived as a more reliable source of knowledge (Haroud & Saqri, 2025).”
- Authors
- Haroud, S., & Saqri, N.
- Affiliations
- University of Hassan II Casablanca (MA)
- Corresponding author
- Haroud, Samia
- ORCID
- Haroud, Samia
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2025-03-21
- Volume
- 15
- Issue
- 4
- Pages
- 396
- ISSN
- 2227-7102 (ISSN-L)
- OpenAlex
- W4408718435
- Cited by
- 101
- References
- 3
- Citations by year
- 2026: 69 · 2025: 32
- FWCI
- 105.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Education Sciences (publishedVersion, cc-by)
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- generative AI, higher education, teacher perceptions, student perceptions, digital literacy, AI in teaching and learning, critical thinking, personalized learning, AI ethics, Moroccan higher education
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
