LLMs as Academic Reading Companions: Extending HCI Through Synthetic Personae

Understand

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.