LLMs as Academic Reading Companions: Extending HCI Through Synthetic Personae

TL;DR

This paper explores the use of large language models as academic reading companions, demonstrating their potential to improve comprehension and engagement in graduate students, while highlighting ethical considerations and the need for responsible AI design.

Contribution

It presents an early study integrating an LLM-based assistant into education, providing practical insights and emphasizing responsible development of synthetic personae for learning support.

Findings

Improved reading comprehension with LLM assistance

Increased student engagement using AI companions

Identified ethical considerations and risks of overreliance

Abstract

This position paper argues that large language models (LLMs) constitute promising yet underutilized academic reading companions capable of enhancing learning. We detail an exploratory study examining Claude from Anthropic, an LLM-based interactive assistant that helps students comprehend complex qualitative literature content. The study compares quantitative survey data and qualitative interviews assessing outcomes between a control group and an experimental group leveraging Claude over a semester across two graduate courses. Initial findings demonstrate tangible improvements in reading comprehension and engagement among participants using the AI agent versus unsupported independent study. However, there is potential for overreliance and ethical considerations that warrant continued investigation. By documenting an early integration of an LLM reading companion into an educational context, this work contributes pragmatic insights to guide development of synthetic personae supporting learning. Broader impacts compel policy and industry actions to uphold responsible design in order to maximize benefits of AI integration while prioritizing student wellbeing.