GenQuest: An LLM-based Text Adventure Game for Language Learners
This addresses language acquisition for EFL learners through an immersive, game-based approach, though it appears incremental in applying existing LLM technology to a specific educational context.
The paper tackles second language learning by developing GenQuest, an LLM-based text adventure game that generates interactive stories for English learners, with a pilot study showing promising vocabulary gains and positive user feedback.
GenQuest is a generative text adventure game that leverages Large Language Models (LLMs) to facilitate second language learning through immersive, interactive storytelling. The system engages English as a Foreign Language (EFL) learners in a collaborative "choose-your-own-adventure" style narrative, dynamically generated in response to learner choices. Game mechanics such as branching decision points and story milestones are incorporated to maintain narrative coherence while allowing learner-driven plot development. Key pedagogical features include content generation tailored to each learner's proficiency level, and a vocabulary assistant that provides in-context explanations of learner-queried text strings, ranging from words and phrases to sentences. Findings from a pilot study with university EFL students in China indicate promising vocabulary gains and positive user perceptions. Also discussed are suggestions from participants regarding the narrative length and quality, and the request for multi-modal content such as illustrations.