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REVIEW article

Front. Psychol.
Sec. Educational Psychology
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1471370

A Systematic Review on Robot-Assisted Language Learning for Adults

Provisionally accepted
Qi Deng Qi Deng 1Changzeng Fu Changzeng Fu 1*Midori Ban Midori Ban 2Takamasa Iio Takamasa Iio 3
  • 1 Northeastern University, Shenyang, China
  • 2 Osaka University, Suita, Ōsaka, Japan
  • 3 Doshisha University, Kyoto, Kyōto, Japan

The final, formatted version of the article will be published soon.

    In the 21st-century era of globalization, language proficiency is a pivotal connector across cultures, with artificial intelligence (AI) revolutionizing educational paradigms through Robot-Assisted Language Learning (RALL). This systematic review examines the role of RALL in adult second language acquisition, focusing on its pedagogical strategies and learner engagement.Unlike the previous systematic reviews that explore the multifaceted roles of robots in language learning, including as teachers, tutors, assistants, and peer learners, we identify explicit and implicit instructional strategies within RALL, highlighting the unique learning landscape of adult learners characterized by self-regulation and self-direction. We assess the latest advancements in RALL for adult learners through three research questions, compare the effectiveness of explicit versus implicit instructions, and investigate affective factors enhancing RALL performance.Our review contributes a comprehensive status analysis, in-depth exploration of interaction modes, and insights for future research directions, providing a roadmap for academic research and practical guidance for educators and robot developers. This study aims to optimize RALL strategies to better meet the needs of adult learners, fostering a more efficient and engaging language learning experience.

    Keywords: Robot-assisted language learning, Adult Education, AI in Education, Language Learning Effectiveness, human-robot interaction

    Received: 27 Jul 2024; Accepted: 14 Oct 2024.

    Copyright: © 2024 Deng, Fu, Ban and Iio. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Changzeng Fu, Northeastern University, Shenyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.