Empirical studies of writing and generative AI: Introduction to the special issue

This special issue of the Journal of Writing Research brings together seven empirical studies of the relationship between writing and generative AI, examining what can be systematically observed and measured about the functioning of generative AI in educational and professional writing contexts. Collectively, the studies demonstrate the necessity and value of methodological pluralism for investigating a complex, rapidly evolving phenomenon. In their contributions, the researchers use experimental comparisons, mixed-methods intervention designs, corpus-based analyses, computational linguistic techniques, and qualitative interpretive approaches. Taken together, these methods enable lines of inquiry that no single approach could sustain: comparisons of AI and human performance in professional writing tasks; analyses of how writers at different ages and levels of expertise engage AI tools; examinations of how assessment systems register and respond to AI-generated prose; and investigations of how human readers interpret texts with ambiguous authorship. By foregrounding both the affordances and limitations of different methodological traditions, the articles present a multifaceted approach to the study of writing and generative AI.