Multiple texts, culture, marginalization, and generative AI: An introduction to the special issue

In this Introduction to the Special Issue, our aims are three-fold. First, we provide the motivation for and explain the timing of this Special Issue. We also introduce three dimensions of analysis to justify why we asked authors to consider issues of culture, marginalization, and generative AI in relation to the literature on learning from multiple texts. These dimensions include that the analysis of culture, marginalization, generative AI, and multiple texts all require the critical consideration of language and source, reflect processes of homogenization and perpetuation, and demand individuals’ sense-making of and within broader social experience. Second, we analyze the Special Issue contributions to identify three unifying themes in the critiques introduced about the literature on learning from multiple texts. These critiques include the multiple texts literature’s (a) neglect of affect, (b) over-reliance on researcher-determined and constrained document sets, and (c) insufficient consideration of the sociotechnical context shaping students’ multiple text use and students’ own emerging conceptions of this context. Third, we introduce the idea of texts models as an epistemic tool that can be used to address some of these concerns by structuring students’ reflections on the affective, epistemic, technical, and social justice dimensions of their interactions with information.