Stewarding Quantum Intelligence and RALL in Evolving EFL Communication through Maritime Teaching Materials for Sustainability

Abstract

This study examines how Quantum Intelligence (QI) and Robot-Assisted Language Learning (RALL) can be used to improve English as a Foreign Language (EFL) communication skills using maritime-themed teaching materials for sustainability education. The purpose of this study is to create and test a new learning model based on need-based learning that uses intelligent tutoring systems (ITS), AI-powered feedback, and maritime information to help students strengthen their communication skills while improving their understanding of cultural and environmental issues. This research is important today because there is a growing need for culturally responsive and AI-integrated teaching that is aligned with the UN Sustainable Development Goals (SDGs), specifically SDG 4 (Quality Education), SDG 13 (Climate Action), and SDG 14 (Life Below Water). Twenty-four junior high school students were purposively selected respondents with characteristics of being proficient in AI tools and basic EFL who participated in this study. They completed a 20-item Likert scale questionnaire regarding their needs responses in RALL-based teaching, which included various activities, including AI engagement, environmental discussions, poster drawing, storytelling, and simulating marine life. The results indicated that students’ enthusiasm and ability to communicate in EFL had increased significantly, with good reliability scores (Cronbach’s α = .963). Students reported that they were genuinely interested in using the AI tutor to learn about things like sailing, seafood, maritime rituals, and coastal life. This study adds to the body of knowledge about sustainable EFL teaching using AI-enhanced QI and culturally relevant content to engage students in conversations about ocean health in English. This study has implications for how future curricula are created by demonstrating how intelligent systems can help students learn about the environment, language, and engagement through personalised, human-like AI interactions.