The integration of artificial intelligence (Al) and machine learning (ML) tools in education has been a growing trend in recent years, with a particular focus on the use of large language models for generative AI, such as ChatGPT. While the potential use of generative AI as a teaching and learning resource has been acknowledged, there is a lack of research exploring how it may influence pedagogy, learning, and authorship. This special issue calls for new research to better understand the impact of generative AI on these areas and the ways it may shape the future of education.
Education content producible by generative AI includes text, essays, and poetry, as well as visual images, computer code, music, and design. An irresistible student tool, generative AI has the speed, scale, ease of use, and accessibility to disrupt education, raising several issues. There are concerns about how such forms of machine learning will influence human learning, transform the meaning of authorship, and upend common pedagogies such as the student essay (Rudolph et al., 2023; Williamson et al., 2023; Zembylas, 2023). Student and faculty reliance on generative AI for academic work raises questions of plagiarism, the possible eradication of a writerly voice and writing style, original thought and novel critique, as well as poses epistemic questions about the nature of learning and knowledge. These are important issues to consider. However, at present, generative AI is an undertheorized area. The aim of this special issue is to spark theoretical development and theoretically-informed empirical research about how generative AI may shape socio-technical practices in education.
We invite theoretical papers and theoretically-informed empirical studies that explore emerging practices and offer new imaginings of generative AI in education. Papers may use a variety of methodological approaches including feminist, critical, new materialist, interpretive, qualitative, rhetorical, quantitative, or experimental.
Topics may include, and are not limited to:
- Critical pedagogy and generative AI
- Ways in which generative AI further complicates notions of authenticity–of authorship, ideas, ownership, and truth
- Creative and productive uses of generative AI and how can they be harnessed in education
- Ethical, political, and epistemological issues of generative AI in education
- Socio-technical explorations of generative AI and equity, power, inclusion, diversity, identity, marginalization, (dis)ability, ethnicity, gender, race, class, community, sustainability, etc.
- Development of methodologies to critically assess generative AI in education
- Alternative imaginaries for the development of future generative AI tools for education
