Cogent Education

Student Information Access and Processing: Implications for Teacher Practice

Cogent Education is pleased to welcome you to submit your research to the Article Collection "Student Information Access and Processing: Implications for Teacher Practice".

This Article Collection will explore how changing ways of accessing information are shaping student thinking, learning, and processing — and what that means for teacher practice. Responsible engagement with information is an increasingly essential skill, as the pathways students use to find and evaluate it grow more complex. How students access information has changed dramatically over the past two decades — from libraries and print texts to search engines, social media, and now AI-generated content — yet there is still much to understand about how these shifting pathways shape the way students think, learn, and process information, and what that means for how teachers teach. This Article Collection takes up that gap. As AI-based technologies increasingly mediate how students encounter and evaluate information, new complexities emerge, including unintended algorithmic bias, deepfakes and other synthetic content, widening gaps in media literacy. This Collection will address how different modes of information access shape student cognition and learning processes, how educators can build students' capacity to critically evaluate what they encounter, the pedagogical implications of AI-mediated information access, and how teacher practice must adapt as students engage with information in fundamentally different ways than in previous generations.

This shift matters because how students access information is no longer incidental to learning — it is shaping the cognitive processes underneath it: how they attend, synthesize, evaluate credibility, and construct knowledge. Students now arrive in classrooms having already formed habits of information-seeking through search algorithms, social feeds, and AI tools, often before a teacher has any say in the matter. This changes what teaching itself requires. Educators can no longer assume a shared starting point for how students find, interpret, or trust information; they must actively teach the skills to navigate it well. The stakes are rising: AI-generated content, algorithmic curation, and synthetic media (including deepfakes) make it harder for students to distinguish reliable information from convincing fabrication. Additionally, the proliferation of misinformation, disinformation, and denial threaten how instructional is delivered and learning occurs. Without a clearer understanding of how these access patterns shape student thinking, teacher practice risks falling further behind the actual conditions under which students are learning.

Subtopics may include, but are not limited to:

1. How search, social media, and AI-generated content shape student attention, synthesis, and knowledge construction
2. Developing student media and information literacy in an AI-mediated landscape
3. Instructional strategies for helping students critically evaluate the credibility and provenance of information they encounter, dealing with the proliferation of misinformation, disinformation, and denial of facts
4. The pedagogical implications of algorithmic bias, synthetic media, and deepfakes for classroom practice
5. How educators can adapt instructional design as students' information-processing habits change
6. Equity considerations in access to information and the tools to evaluate it
7. How teacher preparation and professional development must evolve to address these shifts.

We are especially interested in work that connects shifts in how students access and process information to concrete implications for teaching practice, rather than treating either in isolation.

Meet the Guest Advisors

Dr. Ellen B. Mandinach, Senior Research Scientist Emeritus of WestEd, is an international known scholar data-driven decision making in education. She has developed the constructs data literacy for teachers and culturally responsive data literacy. Her most recent focus has been on data ethics; that is, the responsible use of data, particularly with the proliferation of AI in education. She holds a Ph.D .in educational psychology from Stanford University.

Dr. Debbie Kim, co-founder and Head of Research at PAVE Education Consulting, employs institutional and organizational theory to study how educational entities locate, understand, share, and use evidence to make decisions. Before founding PAVE, she was a Senior Research Scientist at NORC at the University of Chicago, where she led mixed-methods studies for the National Science Foundation, federal agencies, and major foundations.

The Guest Advisors declare no conflict of interest regarding this work.