Supporting Students' Reading and Cognition with AI
Project Overview
The document explores the integration of generative AI tools in education, specifically their effects on students' reading processes and cognitive engagement. A study involving undergraduate students revealed that while AI tools can significantly enhance higher-order thinking and comprehension, they also foster a reliance on AI for simpler tasks, which may result in passive engagement over time. This reliance raises concerns about students' critical thinking skills and active participation in learning. To address these challenges, the document recommends designing AI tools that incorporate scaffolding for both lower and higher-order cognitive tasks, alongside promoting a human-in-the-loop approach for customization. This approach aims to strike a balance between leveraging AI's capabilities and ensuring that students remain actively engaged in their learning processes, ultimately enhancing educational outcomes.
Key Applications
AI tools for reading support (e.g., ChatGPT, Gemini, Perplexity, Claude)
Context: Undergraduate course where students use AI tools to assist with reading academic literature, business reports, and news articles.
Implementation: Students logged their prompts and responses while using AI tools to assist with required readings. The study analyzed these logs to assess cognitive engagement.
Outcomes: Positive student experiences with increased reading efficiency, better comprehension, and encouragement of critical thinking, though reliance on AI may lead to passive engagement.
Challenges: Over-reliance on AI tools may encourage superficial reading and limit deeper cognitive engagement.
Implementation Barriers
Cognitive Engagement Barrier
Students tend to default to lower-order thinking tasks over time, leading to passive reading engagement.
Proposed Solutions: Implement scaffolding for lower-level cognitive tasks and proactive prompts encouraging higher-order thinking.
Motivational Barrier
Students may start with high motivation but this diminishes over time, impacting their engagement with AI tools.
Proposed Solutions: Design AI systems that adapt to students' evolving goals and provide customizable support.
Project Team
Yue Fu
Researcher
Alexis Hiniker
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Yue Fu, Alexis Hiniker
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai