Related papers: Supporting Students' Reading and Cognition with AI

Supporting Students' Reading and Cognition with AI

URL: http://arxiv.org/abs/2504.13900v1
Date: Mon, 07 Apr 2025 17:51:27 GMT
Title: Supporting Students' Reading and Cognition with AI
Authors: Yue Fu, Alexis Hiniker,
Abstract summary: We analyzed text from 124 sessions with AI tools to understand users' reading processes and cognitive engagement.<n>We propose design implications for future AI reading-support systems, including structured scaffolds for lower-level cognitive tasks.<n>We advocate for adaptive, human-in-the-loop features that allow students and instructors to tailor their reading experiences with AI.
Score: 12.029238454394445
License: http://creativecommons.org/licenses/by-sa/4.0/
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.

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