arXiv Open Access 2025

GenQuest: An LLM-based Text Adventure Game for Language Learners

Qiao Wang Adnan Labib Robert Swier Michael Hofmeyr Zheng Yuan
Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

Penulis (5)

Q

Qiao Wang

A

Adnan Labib

R

Robert Swier

M

Michael Hofmeyr

Z

Zheng Yuan

Format Sitasi

Wang, Q., Labib, A., Swier, R., Hofmeyr, M., Yuan, Z. (2025). GenQuest: An LLM-based Text Adventure Game for Language Learners. https://arxiv.org/abs/2510.04498

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2025
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓