A Systematic Review on Oral Interactions in Robot-Assisted Language Learning

Abstract

Although educational robots are known for their capability to support language learning, how actual interaction processes lead to positive learning outcomes has not been sufficiently examined. To explore the instructional design and the interaction effects of robot-assisted language learning (RALL) on learner performance, this study systematically reviewed twenty-two empirical studies published between 2010 and 2020. Through an inclusion/exclusion procedure, general research characteristics such as the context, target language, and research design were identified. Further analysis on oral interaction design, including language teaching methods, interactive learning tasks, interaction processes, interactive agents, and interaction effects showed that the communicative or storytelling approach served as the dominant methods complemented by total physical response and audiolingual methods in RALL oral interactions. The review provides insights on how educational robots can facilitate oral interactions in language classrooms, as well as how such learning tasks can be designed to effectively utilize robotic affordances to fulfill functions that used to be provided by human teachers alone. Future research directions point to a focus on meaning-based communication and intelligibility in oral production among language learners in RALL.

Social/dialogical roles of social robots in supporting children’s learning of language and literacy—A review and analysis of innovative roles

“…In this review, we have proposed innovative roles that a social robot can fulfill in an interaction with a child. This extends established roles that enable a social robot to act as a tutor, peer/companion, or novice/learner ( Mubin et al, 2013 ; Ahmad et al, 2021 ; Lin et al, 2022 ) through being less rigid and more fluid in relation to the established role categories (see Figure 1 ). At the same time, these roles offer innovative forms of education for learning language and literacy that further differentiate the existing categories with respect to domain knowledge and social competence (see Figure 1 ).…”

“…They often have a humanoid or animal-like appearance, and this increases the tendency to anthropomorphize them ( Mubin et al, 2013 ), thus, making it more likely that people will speak to them. Lin et al (2022) discussed oral interactions between learners and robots in their recent RALL review. One of their findings was that interactive oral tasks (such as engaging in dialogues with robots) are often used in foreign-language-learning classes—because robots provide learners with the opportunity to engage in dialogues with a “native” speaker—and these activities are aimed more at practicing communicative skills than improving grammatical accuracy.…”

“…Hence, the design of the role that a robot fulfills in a learning process clearly has manifold consequences not only for the robot’s appearance but also for the interaction and the learning process. Despite these far-reaching consequences, and even though the literature offers different forms of designing an interaction with a robot for the purpose of enjoyable ( Lin et al, 2022 ) and successful learning, little is known about whether and how these different roles can be designed systematically, let alone how they differ in shaping a human–robot social relationship ( De Graaf, 2016 ; Tolksdorf et al, 2020 ).…”

Influence of Human–Computer Interaction-Based Intelligent Dancing Robot and Psychological Construct on Choreography

“…As far as dance itself is concerned, dancing robots can broaden the expression of the art of dance. Interestingly, dance uses body language to express emotion and intention, with both artistic and decorative utilities (Lin et al, 2022 ). Currently, robots might not dance as flexibly and aesthetically as human dancers.…”

Robot-Assisted Language Learning: Integrating Artificial Intelligence and Virtual Reality into English Tour Guide Practice

“…The results indicated a significant improvement in oral production due to the elicitation cues in the learning environment. Lin et al [37] also conducted a systematic review of 22 empirical studies published between 2010 and 2020 and, in particular, analyzed oral interaction design, including language teaching methods, interactive learning tasks, and interaction effects. Oral fluency, literacy, and communicative competence, rather than grammatical accuracy, were given prominence regarding the pedagogical implications of RALL instructional design.…”