Search

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. [ABSTRACT FROM AUTHOR]

Researchers have indicated that integrating augmented reality into robot-assisted language learning modules could visually represent the students' learning needs and extend their embodied cognition of comprehension. To examine the potential of robots through extended embodied cognition, this quasi-experimental study was conducted in the English for Medical Purposes classrooms at the Isfahan University of Medical Sciences. In the 2022 academic year, 526 male and female participants, whose first language is not English, were selected. Students were sorted into three English proficiency levels: pre-intermediate (n = 176), intermediate (n = 175), and upper-intermediate (n = 175), then semi-randomly divided into three-member teams, subsequently, randomly assigned to control (n = 370) or experimental (n = 156) groups. Students in the control group learned English for Medical Purposes listening and reading through online classrooms, while those in the experimental group learned the skills in robot (augmented reality)-assisted classrooms. In the control and experimental groups, flipped classrooms were conducted under the supervision of the teachers. The primary data sources included formative assessments of the participants' English for Medical Purposes listening and reading, as well as interviews. The findings showed that the participants in the robot's augmented reality group achieved significantly higher results, when compared with robot-only and control groups, in English for Medical Purposes listening and reading in academia and the healthcare fields. The positive perception participants had of robot's augmented reality was clear, based on interview results. The outcomes are discussed in detail. • Robot-aided modules facilitated English for Medical Purposes listening and reading. • The glossed robot fostered connection with the world. • Robot's AR enhanced extended embodied cognition for comprehension. • Robot's AR fostered variegated channels for comprehension. • Robot's AR facilitated listening and reading in the fields. [ABSTRACT FROM AUTHOR]

While numerous studies of robot-assisted language learning (RALL) for English-as-a-foreign-language (EFL) learners' language skill development have been done, a comprehensive and theoretically-driven meta-analysis on its effects is still in paucity. To fill the gap, drawing on Activity Theory (AT), this study reported a meta-analysis from 47 independent studies out of 29 literature samples involving 1791 EFL learners on RALL for language skill development published during 2004–2023. The results indicated that the overall effect size was g =.69, 95% CI [.49,.90], suggesting that RALL outperforms non-RALL conditions. In addition, educational levels and intervention durations were found to be significant moderators. Based on the results, implications for practice were discussed. [ABSTRACT FROM AUTHOR]

AbstractAlthough the existing research on educational robots has exhibited the assistance for EFL learners’ English skills, the evidence which shows robot-assisted systems’ effect on adult learners’ English read-aloud is still rare. Nevertheless, read-aloud is still treated as a useful approach in English classes for speech pronunciations in particular in Asia. This study aims at the design of a system that uses a robot as a tutor equips automatic speech recognition to diagnose learners’ errors while they are reading aloud the English passages. The learner is able to practice and improve pronunciations via the diagnosis. An experiment designed with a pretest, a posttest, and a delayed posttest was conducted to evaluate the proposed robot-tutoring system. 19 university students in Taiwan enrolled in the experiment, and learned with the system for 90 minutes per round, with a total of 2 rounds of self-learning. The results showed the participants’ accuracy of read-aloud in the delayed posttest was significantly better than those of the pretest and immediate posttest. In addition, when looking into the investigation of the participants’ perceptions of the system, most were impressed by the system, and also agreed the system was a useful and helpful tool to help reading English passages aloud. Therefore, this study provides evidence of the effects of robot-tutoring approach on adults’ English read-aloud in Taiwan.

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.

This study explores how much current mainstream Robot-Assisted Language Learning (RALL) systems produce outcomes compared to human tutors instructing a typical English conversation lesson. To this end, an experiment was conducted with 26 participants divided in RALL (14 participants) and human tutor (12 participants) groups. All participants took a pre-test on the first day, followed by 30 min of study per day for 7 days, and 3 post-tests on the last day. The test results indicated that the RALL group considerably improved lexical/grammatical error rates and fluency of speech compared to that for the human tutor group. The other characteristics, such as rhythm, pronunciation, complexity, and task achievement of speech did not indicate any differences between the groups. The results suggested that exercises with the RALL system enabled participants to commit the learned expressions to memory, whereas those with human tutors emphasized on communication with the participants. This study demonstrated the benefits of using RALL systems that can work well in lessons that human tutors find hard to teach. [ABSTRACT FROM AUTHOR]

In recent years, social robots have emerged as a new teaching aid in foreign language (FL) classrooms. Interaction in FL classrooms usually takes place between teachers and learners or among learners. However, this constellation of interactions changes when a robot enters the classroom. The robot’s role in the classroom has been studied previously, however, in this article we examine how initial encounters between a social robot and learners occur, focusing on the teacher’s role during these encounters. Additionally, we examine how children seek help or assurance from their teacher when interacting with the robot. Research data consists of video recorded in FL classrooms in primary schools in Finland in 2019. The primary school learners (N = 22) who participated in this study ranged in age from 10 to 13 years. The results show that during the robot-assisted language learning (RALL) interaction, the teacher had several roles: she validated children’s contributions, guided or mediated the discussion, encouraged the children to speak with the robot, and provided technical support. The results also suggest that the teacher’s role in RALL classrooms, while not necessarily central, is essential to ensure smooth interactions between the robot and learners.

Understanding the obstacles and causes students faced while learning with new technologies is the key to inform effective instructional designs. To achieve this aim, this study conducted a qualitative video analysis on language learners’ observable behaviors when they took part in learning activities supported by the technology of robots and IoT-based tangible objects. Insightful findings and instructional implications emerge from the attempt to explore learners’ learning process in terms of the obstacles learners encountered and the causes of the obstacles. Based on the findings and implications, eight instructional guidelines are proposed for teachers/instructional designers to design effective language learning activities with robots and IoT-based tangible objects. This study contributes to the literature on enhancing learning and teaching by integrating educational robots and IoT-based tangible objects in the field of robot assisted language learning (RALL).

While recent years have witnessed increasing attention to technology-enhanced language learning, investigation regarding how technologies facilitate digital storytelling outcomes among middle school students in English-as-a-foreign-language contexts remains insufficiently explored. Studies concerning the effects of different presentation modes such as anthropomorphized robots versus PowerPoint on digital storytelling outcomes have been insufficient, and specific probes into affective domains such as emotion, grit, and perceptions have been even scarcer. The interplay between these issues, therefore, has yet to be validated. This mixed-methods study, accordingly, addresses these concerns by examining not only the relationship among digital storytelling outcomes, emotions, and grit in different presentation modes (Robot vs. PowerPoint), but also student perceptions. Focusing on 52 ninth-grade middle school students from a middle school in central Taiwan, the results from diverse data sources (pre-/post-tests, an questionnaire about emotions, a grit survey, a perception survey, and reflective journals) identified the benefits of both presentation modes. Nonetheless, the robot-assisted mode was more advantageous in making the learners grittier and led to higher storytelling outcomes as well as more positive affect. Storytelling outcomes were strongly and positively correlated with grit, including perseverance of effort and consistency in interests. Nonetheless, perseverance of effort was the best predictor of storytelling outcomes in the experimental group, suggesting higher perseverance of effort for better storytelling outcomes. In terms of student perceptions, the robot-assisted group using Kebbi Air was more positive about the overall learning experience, as demonstrated by higher responses to motivation, effectiveness, engagement, and satisfaction. [ABSTRACT FROM AUTHOR]

Modern emotional companion robots can support multimodal communicative interaction with humans using speech, gestures, and facial expressions. This paper researches the potential of a social robot in the educational process: in a situation of learning a foreign language vocabulary. We have compared a classic text interface for word-learning with a companion robot, orally supporting the learning process of students (43 people, 34 women, average age 22.5years). It was shown that selection of one of the two methods does not affect the efficiency of word memorization, however, most of the students appreciate the robot as an assistant and indicate is as a preferable, or one of the possible learning tools in their future studies. The obtained results open up further prospects for using emotional companion robots in the educational process. [ABSTRACT FROM AUTHOR]

Introduction: Backchannels, i.e., short interjections by an interlocutor to indicate attention, understanding or agreement regarding utterances by another conversation participant, are fundamental in human-human interaction. Lack of backchannels or if they have unexpected timing or formulation may influence the conversation negatively, as misinterpretations regarding attention, understanding or agreement may occur. However, several studies over the years have shown that there may be cultural differences in how backchannels are provided and perceived and that these differences may affect intercultural conversations. Culturally aware robots must hence be endowed with the capability to detect and adapt to the way these conversational markers are used across different cultures. Traditionally, culture has been defined in terms of nationality, but this is more and more considered to be a stereotypic simplification. We therefore investigate several socio-cultural factors, such as the participants’ gender, age, first language, extroversion and familiarity with robots, that may be relevant for the perception of backchannels.Methods: We first cover existing research on cultural influence on backchannel formulation and perception in human-human interaction and on backchannel implementation in Human-Robot Interaction. We then present an experiment on second language spoken practice, in which we investigate how backchannels from the social robot Furhat influence interaction (investigated through speaking time ratios and ethnomethodology and multimodal conversation analysis) and impression of the robot (measured by post-session ratings). The experiment, made in a triad word game setting, is focused on if activity-adaptive robot backchannels may redistribute the participants’ speaking time ratio, and/or if the participants’ assessment of the robot is influenced by the backchannel strategy. The goal is to explore how robot backchannels should be adapted to different language learners to encourage their participation while being perceived as socio-culturally appropriate.Results: We find that a strategy that displays more backchannels towards a less active speaker may substantially decrease the difference in speaking time between the two speakers, that different socio-cultural groups respond differently to the robot’s backchannel strategy and that they also perceive the robot differently after the session.Discussion: We conclude that the robot may need different backchanneling strategies towards speakers from different socio-cultural groups in order to encourage them to speak and have a positive perception of the robot.

Abstract This study focuses on dialogue breakdowns that can occur in robot-assisted language learning (RALL). Our aim is to analyse how children use gaze to resolve these breakdowns, that is, interruptions in the interaction caused by the robot’s inability to understand the children and react appropriately. Our corpus consists of 18 video filmed L2 learning situations where 36 primary school children talk pairwise with an educational robot for the first time. Our participants are 10–13 years old mono- and bilingual children from Swedish speaking schools in Finland learning L2 English. After detecting the breakdowns from the data, we use a multimodal analysis to identify most typical gaze patterns during these sequences. Our results show that when breakdowns occur, children’s first gaze is directed most frequently towards the robot, but after that, they shift their gaze most often towards the teacher. This result suggests that the children try first to resolve the breakdowns with the robot, but when the problem persists, they also use gaze to seek assistance from other participants present in the learning situation. We interpret this finding to show that children attempt to treat social robots as human-like conversational partners in RALL, but that they turn to other human participants if the robot does not follow the expected interactional behavior. [ABSTRACT FROM AUTHOR]

Despite attention by educators and researchers to the advancement of robotics research in education, little experimental research has been conducted on the use of robots in language education for young children. This paper reports on a study aimed at providing an improved English language development assessment questionnaire specifically for use in a project to bring autonomous social robots into English as a foreign language (EFL) classes for native-Korean-speaking children aged 3-5 years old. In order to examine the validity of questionnaire items, a focus group of 10 professors with expertise in child EFL learning was sent the questionnaire, which includes 127 items to be rated on a 5-point Likert scale and two open-ended questions. They were asked to judge whether each item was appropriate for children of the target age group, and whether any items were not appropriate to assess child L2 learners' English language development. The findings inform the selection of appropriate items for evaluating language development stages based on children's speaking performance in a robot-assisted language learning condition. The study's results provide a baseline for an English language assessment questionnaire for EFL teachers to evaluate 3-5-year-old children's language development in English speaking in the robot-assisted language learning (RALL) condition. [ABSTRACT FROM AUTHOR]

This action research created an application system using robots as a tool for training English-language tour guides. It combined artificial intelligence (AI) and virtual reality (VR) technologies to develop content for tours and a 3D VR environment using the AI Unity plug-in for programming. Students learned to orally interact with the robot and act as a guide to various destinations. The qualitative methods included observation, interviews, and self-reporting of learning outcomes. Two students voluntarily participated in the study. The intervention lasted for ten weeks. The results indicated the teaching effectiveness of robot-assisted language learning (RALL). The students acknowledged the value of RALL and had positive attitudes toward it. The contextualized VR learning environment increased their motivation and engagement in learning, and students perceived that RALL could help develop autonomy, enhance interaction, and provide an active learning experience. The implications of the study are that RALL has potential and that it provides an alternative learning opportunity for students. [ABSTRACT FROM AUTHOR]

The large majority of previous work on human-robot conversations in a second language has been performed with a human wizard-of-Oz. The reasons are that automatic speech recognition of non-native conversational speech is considered to be unreliable and that the dialogue management task of selecting robot utterances that are adequate at a given turn is complex in social conversations. This study therefore investigates if robot-led conversation practice in a second language with pairs of adult learners could potentially be managed by an autonomous robot. We first investigate how correct and understandable transcriptions of second language learner utterances are when made by a state-of-the-art speech recogniser. We find both a relatively high word error rate (41%) and that a substantial share (42%) of the utterances are judged to be incomprehensible or only partially understandable by a human reader. We then evaluate how adequate the robot utterance selection is, when performed manually based on the speech recognition transcriptions or autonomously using (a) predefined sequences of robot utterances, (b) a general state-of-the-art language model that selects utterances based on learner input or the preceding robot utterance, or (c) a custom-made statistical method that is trained on observations of the wizard's choices in previous conversations. It is shown that adequate or at least acceptable robot utterances are selected by the human wizard in most cases (96%), even though the ASR transcriptions have a high word error rate. Further, the custom-made statistical method performs as well as manual selection of robot utterances based on ASR transcriptions. It was also found that the interaction strategy that the robot employed, which differed regarding how much the robot maintained the initiative in the conversation and if the focus of the conversation was on the robot or the learners, had marginal effects on the word error rate and understandability of the transcriptions but larger effects on the adequateness of the utterance selection. Autonomous robot-led conversations may hence work better with some robot interaction strategies. [ABSTRACT FROM AUTHOR]

This meta-analysis included 27 empirical studies to explore the effectiveness of robot-assisted language learning (RALL) as well as the moderating effects of a number of variables. The comparison of 64 effect sizes from 2,637 participants indicated significant variability between the independent samples. The overall results showed that the combined linguistic and affective gain of RALL was significant. The results of moderator analysis showed that most moderators, including place of the study, type of robot, publication type, instructional level of the learners, design of the study, time points of assessment, learning setting, second language (L2) proficiency level, type of assessment, language domain, and the non-RALL condition had no significant effects on learning outcomes. However, the moderating effect of the participants’ age was significant as RALL was found to be more effective for elementary school students (7–12 years old). The findings provide insights into the design of effective robots for second language acquisition (SLA) that support learners with certain characteristics. [ABSTRACT FROM AUTHOR]

Providing first language (L1) translations in L2 vocabulary interventions may be beneficial for L2 vocabulary learning. However, in linguistically diverse L2 classrooms, teachers cannot provide L1 translations to all children. Social robots do offer such opportunities, as they can be programmed to speak any combination of languages. This study investigates whether providing L1 translations in a robot‐assisted L2 vocabulary training facilitates children's learning. Participants were Turkish‐Dutch kindergartners (n = 67) who were taught six Dutch (L2) words for which they knew the L1 (Turkish), but not the L2 Dutch form. Half of these words were taught by a Turkish‐Dutch bilingual robot, alongside their Turkish translations; the other half by a monolingual Dutch robot. Children also completed Dutch and Turkish receptive vocabulary tests. Results of generalized linear regression models indicated better performance in the Dutch‐only condition than in the Turkish‐Dutch condition. Children with well‐developed Turkish and Dutch vocabulary knowledge outperformed children with less well‐developed vocabulary knowledge. The majority of children preferred working with the bilingual robot, but children's preference did not affect word learning. Thus, contrary to our prediction, we found no evidence for a facilitating effect of providing L1 translations through a robot on bilingual children's L2 word learning. Lay Description: What is already known about this topic: Turkish‐Dutch children often lag behind their monolingual Dutch peers in terms of Dutch (L2) language skills, which means that supporting these children's L2 development is highly relevant.Children may boost their L2 vocabulary learning using their Turkish (L1) vocabulary knowledge.Social robots that can speak a vast variety of languages can be used to provide L1 input in linguistically diverse classrooms. What this paper adds: Using an experimental design with a social robot, we investigated whether children learned more L2 words when L1 translations were provided by a bilingual Turkish‐Dutch robot.Children learned more L2 target words when the robot spoke only L2, compared to when the robot also provided L1 translations.>70% of the children preferred to play again with the bilingual robot, as compared to the monolingual robot. Implications for practice and/or policy: More research is needed to investigate how children's L1 can help L2 vocabulary learning, perhaps a more L1‐inclusive school context.Technological developments (e.g., in automatic speech recognition) are needed to further improve the interaction between robot and child. [ABSTRACT FROM AUTHOR]

Four different interaction styles for the social robot Furhat acting as a host in spoken conversation practice with two simultaneous language learners have been developed, based on interaction styles of human moderators of language cafés. We first investigated, through a survey and recorded sessions of three-party language café style conversations, how the interaction styles of human moderators are influenced by different factors (e.g., the participants language level and familiarity). Using this knowledge, four distinct interaction styles were developed for the robot: sequentially asking one participant questions at the time (Interviewer); the robot speaking about itself, robots and Sweden or asking quiz questions about Sweden (Narrator); attempting to make the participants talk with each other (Facilitator); and trying to establish a three-party robot–learner–learner interaction with equal participation (Interlocutor). A user study with 32 participants, conversing in pairs with the robot, was carried out to investigate how the post-session ratings of the robot's behavior along different dimensions (e.g., the robot's conversational skills and friendliness, the value of practice) are influenced by the robot's interaction style and participant variables (e.g., level in the target language, gender, origin). The general findings were that Interviewer received the highest mean rating, but that different factors influenced the ratings substantially, indicating that the preference of individual participants needs to be anticipated in order to improve learner satisfaction with the practice. We conclude with a list of recommendations for robot-hosted conversation practice in a second language. [ABSTRACT FROM AUTHOR]

This action research created an application system using robots as a tool for training English-language tour guides. It combined artificial intelligence (AI) and virtual reality (VR) technologies to develop content for tours and a 3D VR environment using the AI Unity plug-in for programming. Students learned to orally interact with the robot and act as a guide to various destinations. The qualitative methods included observation, interviews, and self-reporting of learning outcomes. Two students voluntarily participated in the study. The intervention lasted for ten weeks. The results indicated the teaching effectiveness of robot-assisted language learning (RALL). The students acknowledged the value of RALL and had positive attitudes toward it. The contextualized VR learning environment increased their motivation and engagement in learning, and students perceived that RALL could help develop autonomy, enhance interaction, and provide an active learning experience. The implications of the study are that RALL has potential and that it provides an alternative learning opportunity for students.

This study aims to design an authoring system of robots and IoT-based toys for creating a scenario-based interactive learning environment for young English as a Foreign Language learners. This study adopts a design-based research approach to investigate the pedagogical needs, the critical features and usability of such a authoring system. Twelve adult participants, including EFL teachers, parents, non-technical, and technical professionals, voluntarily participated in this study to co-develop and evaluated the authoring system through iterations of needs collection, system design, system testing and refinements. An authoring system, consisting of IoT-based toys, a robot and a script editor, was finally developed taking four cycles of iterative improvements in 18 weeks. Eleven pedagogical needs concerning children's language and cognitive development, better children-robot interaction and practical issues were identified. The modifications made across different versions of the authoring system were presented in detail. The results also show that the issues of effectiveness, robustness and efficiency were the most frequently identified, and that the system had achieved good usability after four rounds of design and refinements. Design principles and guidelines also merged as a result providing insights into the effective design of Robot Assisted Language Learning in particular and Technology-Enhanced Language Learning in general. [ABSTRACT FROM AUTHOR]

Although robots’ social behaviors are known for their capacity to facilitate learner–robot interaction for language learning, their application and effect have not been adequately explored. This study reviewed 59 empirical articles to examine the contexts and application of various social behaviors of robots for language learning, and conducted a meta-analysis of 18 study samples to evaluate the effect of robots’ social supportive behaviors on language learning achievement. Results indicate that robots’ social behaviors have mostly been applied in the studies with K–12 students, for learning vocabulary in English, including small sample sizes of below 80 participants, and lasting for one session. Second, various verbal and non-verbal behaviors of robots have been identified and applied, showing mixed results on language learning achievement. Third, robots’ social supportive behaviors have produced a positive effect on language learning achievement compared to neutral behaviors (g = 0.269). Finally, detailed suggestions for future research are discussed. [ABSTRACT FROM AUTHOR]

Robot-assisted language learning (RALL) is a promising application when employing social robots to help both children and adults acquire a language and is an increasingly widely studied area of child–robot interaction. By introducing prosodic entrainment, i.e., converging the robot’s pitch with that of the learner, the present study aimed to provide new insights into RALL as a facilitative method for interactive tutoring. It is hypothesized that pitch-level entrainment by a Nao robot during a word learning task in a foreign language will result in increased learning in school-aged children. The results indicate that entrainment has no significant effect on participants’ learning, contra the hypothesis. Research on the implementation of entrainment in the context of RALL is new. This study highlights constraints in currently available technologies for voice generation and methodological limitations that should be taken into account in future research.

In recent years, robots have increasingly been implemented as tutors in both first- and second-language education. The field of robot-assisted language learning (RALL) is developing rapidly. Studies have been published targeting different languages, age groups, and aspects of language and using different robots and methodologies. The present review presents an overview of the results obtained so far in RALL research and discusses the current possibilities and limitations of using social robots for first- and second-language learning. Thirty-three studies in which vocabulary, reading skills, speaking skills, grammar, and sign language were taught are discussed. Beside insights into learning gains attained in RALL situations, these studies raise more general issues regarding students' motivation and robots' social behavior in learning situations. This review concludes with directions for future research on the use of social robots in language education. [ABSTRACT FROM AUTHOR]

Anthropomorphized robots are regarded as beneficial tools in education due to their capabilities of improving teaching effectiveness and learning motivation. Therefore, one major trend of research, known as Robot-Assisted Language Learning (RALL), is trying to develop robots to support teaching and learning English as a foreign language (EFL). As more practical studies on materials development have been called for, this paper details the methods of adopting the proposed RALL framework to design robot-assisted instructional materials for elementary students studying in an EFL national curriculum in Taiwan. Quantitative and qualitative experiments were conducted in this research to investigate the learning performance and motivation of the designed robot-assisted instructional materials. Two groups of participants from an elementary school in Taiwan enrolled in this experiment. The post-test results revealed that the experimental group outperformed the control group, particularly in terms of the improvement of listening and reading skills. Furthermore, the motivation survey of the instructional materials also showed that the experimental group's learning motivation was positively influenced. Therefore, our research demonstrated that the instructional materials developed by our RALL framework provide a great opportunity to foster elementary students' English language learning. [ABSTRACT FROM AUTHOR]

Introduction: Backchannels, i.e., short interjections by an interlocutor to indicate attention, understanding or agreement regarding utterances by another conversation participant, are fundamental in human-human interaction. Lack of backchannels or if they have unexpected timing or formulation may influence the conversation negatively, as misinterpretations regarding attention, understanding or agreement may occur. However, several studies over the years have shown that there may be cultural differences in how backchannels are provided and perceived and that these differences may affect intercultural conversations. Culturally aware robots must hence be endowed with the capability to detect and adapt to the way these conversational markers are used across different cultures. Traditionally, culture has been defined in terms of nationality, but this is more and more considered to be a stereotypic simplification. We therefore investigate several socio-cultural factors, such as the participants' gender, age, first language, extroversion and familiarity with robots, that may be relevant for the perception of backchannels. Methods: We first cover existing research on cultural influence on backchannel formulation and perception in human-human interaction and on backchannel implementation in Human-Robot Interaction. We then present an experiment on second language spoken practice, in which we investigate how backchannels from the social robot Furhat influence interaction (investigated through speaking time ratios and ethnomethodology and multimodal conversation analysis) and impression of the robot (measured by post-session ratings). The experiment, made in a triad word game setting, is focused on if activity-adaptive robot backchannels may redistribute the participants' speaking time ratio, and/or if the participants' assessment of the robot is influenced by the backchannel strategy. The goal is to explore how robot backchannels should be adapted to different language learners to encourage their participation while being perceived as socio-culturally appropriate. Results: We find that a strategy that displays more backchannels towards a less active speaker may substantially decrease the difference in speaking time between the two speakers, that different socio-cultural groups respond differently to the robot's backchannel strategy and that they also perceive the robot differently after the session. Discussion: We conclude that the robot may need different backchanneling strategies towards speakers from different socio-cultural groups in order to encourage them to speak and have a positive perception of the robot., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Engwall, Cumbal and Majlesi.)

This study introduces the educational assistant robots that we developed for foreign language learning and explores the effectiveness of robot-assisted language learning (RALL) which is in its early stages. To achieve this purpose, a course was designed in which students have meaningful interactions with intelligent robots in an immersive environment. A total of 24 elementary students, ranging in age from ten to twelve, were enrolled in English lessons. A pre-test/post-test design was used to investigate the cognitive effects of the RALL approach on the students’ oral skills. No significant difference in the listening skill was found, but the speaking skills improved with a large effect size at the significance level of 0.01. Descriptive statistics and the pre-test/post-test design were used to investigate the affective effects of RALL approach. The result showed that RALL promoted and improved students’ satisfaction, interest, confidence, and motivation at the significance level of 0.01. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]