Supporting Students' Reading and Cognition with AI
Yue Fu, Alexis Hiniker
TL;DR
The paper investigates how AI reading tools shape students' reading processes and cognitive engagement in undergraduate coursework. It uses a mixed-methods approach, collecting AI prompt logs, responses, reflections, and discussion questions from 124 reading entries across 19 students over three weeks, and codes prompts using Bloom's taxonomy. Findings show that most prompts target understanding, with mid-session cycles showing higher-order thinking (analyzing, evaluating), but over weeks engagement trends toward passive reading, underscoring a tension between efficiency and deep cognition. The authors propose design implications, including scaffolds for recall, proactive higher-order prompts, and human-in-the-loop customization to balance user autonomy with cognitive engagement, contributing to the broader discourse on AI-supported academic reading.
Abstract
With the rapid adoption of AI tools in learning contexts, it is vital to understand how these systems shape users' reading processes and cognitive engagement. We collected and analyzed text from 124 sessions with AI tools, in which students used these tools to support them as they read assigned readings for an undergraduate course. We categorized participants' prompts to AI according to Bloom's Taxonomy of educational objectives -- Remembering, Understanding, Applying, Analyzing, Evaluating. Our results show that ``Analyzing'' and ``Evaluating'' are more prevalent in users' second and third prompts within a single usage session, suggesting a shift toward higher-order thinking. However, in reviewing users' engagement with AI tools over several weeks, we found that users converge toward passive reading engagement over time. Based on these results, we propose design implications for future AI reading-support systems, including structured scaffolds for lower-level cognitive tasks (e.g., recalling terms) and proactive prompts that encourage higher-order thinking (e.g., analyzing, applying, evaluating). Additionally, we advocate for adaptive, human-in-the-loop features that allow students and instructors to tailor their reading experiences with AI, balancing efficiency with enriched cognitive engagement. Our paper expands the dialogue on integrating AI into academic reading, highlighting both its potential benefits and challenges.