A multidimensional framework for student searching in algorithmically curated information environments

In today’s information landscape, students routinely encounter content that is inaccurate, conflicting, or incomplete. These encounters are shaped by a confluence of personalization algorithms, user behaviors, and media dynamics that structure what information students access and how they interpret it. We address this issue by integrating work from epistemic cognition and multiple document comprehension (MDC) that addresses the sociotechnical and epistemic complexities of digital search and information processing. Whereas MDC provides a foundation for examining integration of conflicting documents, these models have not deeply unpacked how students arrive at such documents. Theories of epistemic cognition, conversely, have advanced understanding of the processes involved in navigating “epistemically unfriendly” environments. We propose a framework that captures the cognitive, metacognitive, and justice-oriented demands of digital search, articulated through dimensions of algorithmic awareness, epistemic reflexivity, integrative sourcing and sensemaking, and justice sensitivity. These dimensions highlight how algorithmic filtering, prior beliefs, search behaviors, and epistemic injustice intersect to shape what information students see, how they make sense of it, and which perspectives they trust. Our aim is to contribute to researchers’ conceptualization of information processing online and ultimately prompt a critical reevaluation of educational practices in response to evolving technological landscapes.