Generative AI in Practice: How AI Tools are Transforming Teaching and Learning in Higher Education
The emergence of generative artificial intelligence (GenAI) tools has transformed the technological landscape of higher education rapidly. Tools capable of generating text, code, images, and other media are now widely accessible and embedded increasingly in the everyday academic workflows of students and educators. Early discussions have focused on academic integrity, assessment security, and institutional policy. Beyond these initial reactions, however, a growing body of practice is emerging in which generative AI is actively integrated into teaching, learning, and academic work. Students are using these tools to support programming, writing, and study practices, while educators are experimenting with new approaches to feedback, course design, and assessment.
Despite this rapid uptake, research remains fragmented and often technically focused. Key questions remain underexplored: how does routine GenAI use affect learning processes and student agency? What happens to knowledge construction and authorship when AI mediates academic work? And what do these shifts mean for how we design and theorise teaching and learning? Understanding how GenAI is used in practice is therefore essential for informing pedagogical development and institutional strategy in higher education.
The goal of this work is to examine how generative AI tools are being used in practice across higher education and how these uses are shaping teaching, learning, and academic work. While discussions of artificial intelligence in education often emphasize theoretical implications or institutional responses, there remains a clear need for empirical and practice-oriented research that moves beyond policy reaction toward a deeper understanding of what is actually changing, and why it matters. In particular, the field needs research that can illuminate the tensions GenAI adoption generates: between augmentation and dependency, between efficiency and the erosion of effortful learning, and between institutional oversight and student agency.
This collection brings together research that investigates the real-world adoption of generative AI technologies in higher education contexts. Contributions may explore how students employ generative AI tools to support learning, how educators incorporate such tools into pedagogical design, and how disciplinary practices are evolving in response to these technologies. We particularly welcome contributions that situate their findings within a theoretical or conceptual framework, and that draw out implications beyond the immediate context studied.
As these tools become more embedded in educational practice, the integration of AI raises important questions about academic integrity, assessment practices, equity, and student agency. By highlighting concrete examples of practice alongside critical reflection, this research seeks to develop a clearer understanding of how generative AI tools are reshaping higher education and the opportunities and challenges that arise from their use. The collection aims to support informed, ethically responsible, and pedagogically sound integration of generative AI technologies within teaching and learning.
This work focuses on the practical use of generative AI tools within higher education and welcomes contributions that examine how these tools are integrated into teaching, learning, assessment, and academic workflows across disciplines.
Of particular interest are studies exploring:
– student use of generative AI for learning, programming, writing, or study support;
– educator use of generative AI in teaching preparation, feedback, and instructional design;
– disciplinary differences in adoption and practice; case studies of classroom- or curriculum-level integration;
– institutional approaches to supporting or guiding tool use;
– and the development of AI literacy and responsible use practices among students and staff.
While emphasising practice-based research, this collection also welcomes critical engagement with issues such as academic integrity, equity, human–AI interaction, and faculty development. Across all contribution types, we encourage authors to move beyond description toward analysis, interpreting what observed practices reveal about learning, pedagogy, or the broader implications of AI integration. We invite original research articles, case studies, methodological contributions, conceptual papers, and systematic reviews that advance scholarly understanding of how generative AI is shaping higher education practice.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
- Data Report
- Editorial
- FAIR² Data
- FAIR² DATA Direct Submission
- General Commentary
- Hypothesis and Theory
- Methods
- Mini Review
- Opinion
- … View all formats
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Keywords: large language models, higher education, AI for education, AI literacy
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
