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

2026-04-20

AI Summary

This study addresses the lack of low-cost, personalized “learning-by-teaching” support in traditional vocabulary acquisition, where existing systems often rely on rigid question templates that fail to adapt dynamically to learners’ needs. To overcome this limitation, the work proposes a novel framework that leverages a large language model (LLM) as a simulated tutee, generating context-aware questions in real time to prompt learners to explain and elaborate on target vocabulary. This approach enables scalable, cost-effective personalization by encouraging deeper cognitive engagement during instruction. In a pilot study with ten participants, the method significantly improved vocabulary retention at both three-day and seven-day intervals. Furthermore, the findings uncover key individual learner traits that modulate instructional effectiveness, offering insights for adaptive educational design.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Learning by Teaching
• vocabulary acquisition
• question generation
• Large Language Models
• personalized learning

Innovation

• Learning by Teaching
• Large Language Models
• vocabulary acquisition
• adaptive questioning
• personalized learning