Empowering Vocabulary Learning Through Teaching AI: Using LLMs as a Student to Perform Learning by Teaching in Vocabulary Acquisition

TL;DR

This study introduces a system utilizing Large Language Models to generate dynamic questions for Learning by Teaching in vocabulary learning, demonstrating improved retention and personalized benefits.

Contribution

The paper presents a novel LLM-based question generation system for LbT, addressing limitations of rigid templates and high costs, and explores learner traits for optimized learning.

Findings

Improved memory retention at 3 and 7 days compared to traditional methods.

Identified learner traits linked to better learning outcomes.

Demonstrated scalability and cost-effectiveness of the system.

Abstract

"Learning by Teaching (LbT)" helps learners deepen their understanding by explaining concepts to others, with questions playing a vital role in identifying knowledge gaps and reinforcing comprehension. However, existing systems for generating such questions often rely on rigid templates and are expensive to build. To overcome these limitations, we developed a system using Large Language Models (LLMs) to create dynamic, contextually relevant questions for LbT. In our English vocabulary learning study, we examined which learner characteristics best leverage the system's benefits. Our results showed improved memory retention over traditional methods at three and seven days of testing, with ten participants. Additionally, we identified traits linked to better learning outcomes, highlighting the potential for tailored approaches. These findings support the development of scalable, cost-effective solutions to enhance LbT methods across various fields.