Critical Integration of Generative AI in Higher Education: Cognitive, Pedagogical, and Ethical Perspectives

Authors

  • Dr. Ahmedtelba, Elycheikh

Keywords:

inclusive education, theatre pedagogy, social cohesion, bullying prevention, reading theatre, Content, index, sign, Symbol, expression, duality, icon, attrition, Chetti Malay, Gen Y, Gen Z, language shift, loss, Higher education, Normalization, Student performance, Academic Writing, Generative AI, Cognitive offloading, Critical AI adoption, Naﶥ AI reliance, Learning patterns, Confirmation bias, Scaffolding elimination, Quasi-experimental study, Personalized learning, Metacognition, Academic integrity

Abstract

Generative AI is rapidly transforming higher education by reshaping cognitive processes, learning behaviors, assessment practices, and instructional approaches. This study examines the impact of AI on student learning through a combination of multi-institutional evidence and a quasi-experimental assessment in an undergraduate writing course. Three central dimensions are analyzed: cognitive offloading, critical versus naïve adoption of AI, and emerging learning patterns including normalization, confirmation bias, and the erosion of scaffolding. Findings reveal that AI tools can enhance grammar accuracy, research efficiency, and factual recall, while also posing risks to creativity, critical thinking, independent revision, and metacognitive engagement. The study highlights the importance of structured, critically mediated integration of AI into curricula to maximize learning benefits, uphold academic integrity, and support long-term skill development.

References

E. Ahmedtelba (2025) Rethinking ESL education: Integrating holistic and experiential learning models. 16, 589–600.

Erik Brynjolfsson, Andrew McAfee (2020) The second machine age: Work, progress and prosperity in a time of brilliant technologies.

P. Chou, et al. (2023) Impact of AI tutors in flipped classroom settings: A study at the University of Michigan. 105–120.

James Paul Gee (2020) The dangers of normalizing AI usage in learning. 72(3), 335–347.

Michael Holmes, Cathy Bialik, Charles Fadel (2019) Artificial intelligence in education: Promises and implications for teaching and learning.

L. Johnson, et al. (2022) Student perceptions and usage of AI tools in higher education. 45(1), 89–102.

S. Kumar, et al. (2022) Predictive analytics for student retention: Accuracy and implementation. 9(4), 202–217.

Diane Larsen-Freeman (2021) The impact of translation tools on language acquisition. 49(1), 22–36.

J. Miller, R. Davis (2023) Grammarly and student writing: Benefits and limitations. 28(3), 56–70.

E. F. Risko, S. J. Gilbert (2016) Cognitive offloading. 20(9), 676–688.

Neil Selwyn (2021) Critically evaluating AI in education: Ethical and practical perspectives. 46(1), 42–57.

W. R. Shadish, T. D. Cook, D. T. Campbell (2002) Experimental and quasi-experimental designs for generalized causal inference.

Cass R. Sunstein (2017) #Republic: Divided democracy in the age of social media.

UNESCO (2021) Recommendation on the ethics of artificial intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000380455

K. VanLehn (2022) Intelligent tutoring systems and student learning gains. 57(4), 281–295.

H. White, S. Sabarwal (2014) Quasi-experimental design and methods. https://www.unicef-irc.org/publications/753/

Critical Integration of Generative AI in Higher Education: Cognitive, Pedagogical, and Ethical Perspectives

Downloads

Published

2025-09-22

How to Cite

Critical Integration of Generative AI in Higher Education: Cognitive, Pedagogical, and Ethical Perspectives. (2025). London Journal of Research In Humanities and Social Sciences, 25(13), 1-12. https://journalspress.uk/index.php/LJRHSS/article/view/1601