A Survey of Robot-Assisted Language Learning (RALL) <s> Language Learning Theories <s> At the beginning of the second millennium, many university students in Taiwan that are enrolled in Freshman English are still being taught with teacher-centered methods. This study, which was inspired by the author’s studies and research in the theoretical arguments of the Natural Approach and Psychological Method put forth by American educator Stephen Krashen, was undertaken for the purpose of evaluating the practical applicability of communicative methodologies in a Taiwan classroom setting. This study especially focuses on the connection between students’ interest, self-esteem, attitude, motivation, and the use of various student-centered communicative activities designed to enhance confidence and language acquisition. Through the use of both quantitative and qualitative research methods focusing on learner perceptions of their overall learning experience, the author proves the effectiveness of pedagogies based on Krashen’s theory of affective filter. <s> BIB001 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Language Learning Theories <s> The study reported in this article investigated (a) whether classroom language anxiety affects learners’ ability to improve accuracy in their use of English articles when provided with corrective feedback in the form of recasts and (b) whether language anxiety influences the extent to which learners modify output following recasts. Four groups were formed based on their responses to a questionnaire measuring language anxiety: high-anxiety learners who received recasts (N = 13), low-anxiety learners who received recasts (N = 11), high-anxiety learners who received no recasts (N = 11), and low-anxiety learners who received no recasts (N = 10). For the two recast groups, two treatment sessions directed at article errors took place, which were audio-taped. Transcriptions were then coded for the frequency of recasts and modified output. All groups completed a pretest, and immediate and delayed posttests. Results show that in the speeded dictation and writing immediate posttests, the low-anxiety recast group outperformed the high-anxiety recast group and the low-anxiety control group, but there was no significant difference in the performance of both high-anxiety recast and control groups. Moreover, the less anxious learners who benefited from recasts were found to produce higher levels of modified output. In other words, recasts were most effective for low-anxiety learners who produced high levels of modified output. These findings suggest that language anxiety is a factor influencing not only whether recasts lead to modified output but also whether they promote learning. <s> BIB002
Using robots in language learning aligns with practical guidance on employing a number of theories of language learning. Three prominent theories of language learning-innatist (a.k.a. nativist), behavioral, and interactionist-hold different implications for teaching; however, these strategies are best combined in practical language instruction  . Per Nor and Rashid (2018) behavioral theory suggests that positive and negative reinforcement are needed for language learning, along with corrective feedback. Innatist theory suggests the need for comprehensible input and an environment that reduces anxiety as much as possible. Interactionist theory suggests that language is best learned when interacting with social others and with the goal of social interaction, and that teaching should be within one's zone of proximal development (ZPD), what one can achieve with guidance  . The addition of robots is most obviously connected to this last theory. By using an embodied agent that is more readily interpreted as a social other that can be communicated with, robots should be better than other technology for supporting language learning. Strategies for further supporting learning using robots align with practical implications from both innatist and behavioral theory; that is, the importance of feedback and personalization, the benefits of which are also supported by RALL specific research highlighted previously. As evidenced by research thus far, the clearest benefit of using robots over other technology and in addition to human instruction is that they lower the affective filter. Based on Krashen's affective filter hypothesis and literature validating the role of affect in language learning BIB001 BIB002 , it is probable that robots' ability to lower the affective filter (if more than a novelty effect) will also allow for the processing of more comprehensible input and thus promote learning gains.