Special Issue: The Integration of Generative AI in Teaching and Learning

The integration of GenAI into learning and teaching is still in its nascent stages, but its future could unfold in various ways depending on technological advancements, educational policies and societal acceptance. Whether through widespread integration, selective use, enhanced human-AI collaboration, resistance or innovative models, the trajectory will depend on how education systems, policymakers, educators and society navigate this transformative technology’s benefits and challenges. As we advance, the goal should be to harness GenAI’s potential to enrich education while maintaining the human touch that is central to the learning experience. We must not stand by but experiment, pilot and share results as rapidly as possible to influence the way forward.

As the educational landscape continues to evolve, the integration of GenAI has emerged as a potential pivotal force, transforming how students learn and educators teach. This development presents different visions of how GenAI could shape the future of teaching and learning, ranging from widespread integration to selective use and innovative experimentation. Each of these potential paths reflects degrees of change and offers a glimpse into the possible futures of GenAI in education.

One scenario envisions GenAI becoming a ubiquitous presence in education, seamlessly integrated into curricula and widely accepted by both educators and students. GenAI works together with human teachers, fundamentally reshaping the teaching landscape. Co-teaching models see GenAI complementing human expertise, enhancing the learning experience through its ability to process vast amounts of data but offer personalised insights. Human educators retain their critical role, focusing on the emotional and interactive dimensions of teaching, whereas GenAI manages the technical aspects of teaching and assessment. GenAI-powered learning management systems (LMS) could also play a central role, not only facilitating course delivery but also offering real-time, adaptive learning paths tailored to individual students’ needs. Routine tasks, such as grading and administrative processes, would be automated, allowing teachers more time to focus on individual student engagement.

GenAI could be used to provide virtual tutors and teaching assistants, offering personalised support to students outside of class hours. GenAI-powered systems would adapt to individual learning needs, providing targeted help and alleviating some of the administrative burden on educators by managing student queries, marking and feedback. The use of GenAI could enhance collaboration tools for both students and teachers, ensuring more streamlined communication and workflow. GenAI could provide educators with data-driven insights on student performance, enabling teachers to tailor their teaching strategies based on empirical evidence and evolving group and learning dynamics.

Alternatively, a less ambitious approach could see GenAI integrated selectively into the education system, focusing on areas where it can deliver the most value while leaving the traditional roles of human educators largely intact. GenAI would complement rather than replace existing teaching methods. GenAI tools could be employed to provide supplemental instruction in subjects where students require additional support. This might involve the generation of exercises, quizzes and tailored explanations to reinforce classroom learning. In higher education, GenAI could assist in academic research by filtering through vast quantities of literature, suggesting relevant studies and even generating preliminary reports. This would not only save researchers significant time but also improve the quality of scholarly output.

Furthermore, GenAI could play a transformative role in enhancing accessibility within education. For students with disabilities, GenAI-driven tools such as real-time translation services or speech-to-text functionalities could create more inclusive learning environments. Beyond classroom learning, GenAI could also help to streamline administrative processes, making institutions more efficient by automating tasks like scheduling, enrolment management and other bureaucratic functions. Such uses would not necessarily alter the essence of human teaching but could help to alleviate the pressures on educators, allowing them to focus more on their students.

A more radical future for GenAI in education envisions universities and other educational institutions becoming centres of innovation, experimenting with GenAI in ways that could fundamentally redefine education. GenAI would be fully embraced as a creative and dynamic force in teaching and learning. Entire courses or degree programmes could be designed, delivered and continuously updated by GenAI systems, which would ensure that content remains at the cutting edge of knowledge and pedagogy. These GenAI-driven curricula would adapt in real-time to advancements in various fields, offering students a constantly evolving and relevant education.

Alongside GenAI-driven curricula, immersive learning environments powered by GenAI, combined with virtual and augmented reality technologies, could revolutionise how students engage with learning materials, knowledge and skills. Virtual labs, historical reenactments and real-time simulations would create interactive and immersive spaces that bring theoretical concepts to life in ways that traditional teaching methods cannot. These environments would allow students to experiment, explore and learn in a more engaging, hands-on way, potentially fostering deeper understanding and retention of knowledge.

Beyond individual institutions, GenAI could enable the creation of global education networks, allowing students and educators from different parts of the world to collaborate seamlessly across borders. These networks would foster an interconnected academic community, facilitating the sharing of resources, expertise and insights on a global scale and finally realising the hopes of open sharing. Through such collaboration, GenAI could promote greater access to education, ensuring that students, regardless of geographical location, can benefit from the best learning experiences and opportunities available.

As we consider these potential futures for AI in education, GenAI holds vast promise. Whether through comprehensive integration, selective use or bold experimentation, AI offers opportunities to enhance learning, streamline operations and push the boundaries of what is possible in education. However, the successful adoption of GenAI will require thoughtful navigation of ethical concerns, the preservation of human oversight and a commitment to ensuring that GenAI augments, rather than replaces, the human elements of teaching and learning. By carefully managing this transition, educational institutions can harness the power of GenAI to create a more personalised, inclusive and innovative future for education.

As we look ahead, one thing is clear: GenAI technologies are here to stay. This inevitability compels decisionmakers, educators and faculty members to critically evaluate how these technologies are integrated into teaching and learning processes – which potential scenarios to aim at. The journey to explore the role of GenAI in education underscores the profound influence technology can wield in shaping educational experiences. While GenAI offers exciting opportunities for enhancing learning, it also presents significant challenges that demand careful consideration.

This special issue seeks to contribute to the ongoing discourse by providing a platform for researchers to share their insights on GenAI’s pedagogical, technological and organisational dimensions. This collection of research aspires to deepen our understanding of GenAI’s role in education and offer impactful contributions to the field. We can identify common themes that arise from this collection of research. For example, how can GenAI be used to enhance and personalise the learning experience? Another theme focuses on institutional, educator and student perspectives; the last is GenAI’s pedagogical application for assessment and evaluation. These themes collectively suggest a growing interest in understanding and implementing GenAI technologies in various aspects of education, from learning artefacts to personalised learning experiences, while considering the perspectives of different stakeholders in the educational process.

Editor-in-Chief – Wim Van Petegem, KU Leuven, Belgium

Guest Editor-in-Chief – Gila Kurtz, Holon Institute of Technology (HIT), Israel

Guest Co-Editors:

Gilly Salmon, Education Alchemists Ltd., UK

Ilona Buchem, Berlin University of Applied Sciences, Germany

Marco Kaltz, Heidelberg University of Education, Germany, and the Open University of the Netherlands Ryan Watkins, George Washington University, USA