Rethinking Higher Education Pedagogy for Generation Z: Strategies, Challenges, and Theoretical Perspectives on Artificial Intelligence Integration
DOI:
https://doi.org/10.66635/9a8bbs13Keywords:
Artificial Intelligence, Generation Z, Higher Education, Personalized Learning, Self-Regulated Learning, Technology AcceptanceAbstract
University students belonging to Generation Z have brought to higher education learning habits, attention patterns, and expectations formed almost exclusively in an environment where interactions with digital and algorithmically mediated technologies prevail. Simultaneously, advanced generative and adaptive artificial intelligence (AI) tools are no longer limited to peripheral applications within the field of educational technology but have become integral in helping students search for information, develop writing skills, receive constructive feedback, and manage their learning behavior. This paper offers a conceptual and systematic literature review of the topic of how AI could be effectively used in higher education in order to improve learning of Generation Z students. The study analyses peer-reviewed research articles found in Scopus- and Web of Science-indexed Journals over the period of 2015-2026 and focuses on the ways how personalized and adaptive learning systems, intelligent tutoring systems, and generative AI tools like ChatGPT influence students' motivation, engagement, and self-regulated learning. In order to do so, the review employs four theoretical frameworks: Prensky's framework of digital natives, Self-Determination Theory, Self-Regulated Learning Theory, and the Unified Theory of Acceptance and Use of Technology (UTAUT2). The paper discusses the existing pedagogical approaches such as flipped and hybrid classrooms, use of AI in formative assessment, gamification and project-based learning, and prompt literacy, as well as major problems associated with the application of AI in the learning process (algorithmic bias, academic integrity, dependency on AI, data privacy issues, and inequitable access). In the conclusion, AI has the highest potential in education when it is used to complement rather than to replace human instruction, and when universities combine technology use with training in critical thinking, digital ethics, and self-control. Implications for curriculum design, teacher development, and policy-making are discussed along with suggestions for future research.
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