3 research outputs found

    A systematic review on cross-culture, humor and empathy dimensions in conversational chatbots: The case of second language acquisition

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    The advancement of information and communication technologies has led to an increasing use of conversational chatbots in the learning and teaching sector, especially for the second language (L2) acquisition. In the field of second language acquisition, the use of AI chatbots has been explored, mainly studying pedagogical approaches. However, there is a limited study in the development of empathetic strategies for dealing with learners’ emotional discomfort, the impact of humor and the consideration of learners’ cultural backgrounds. Thus, this study reviews the existing studies on AI second language (L2) chatbots to investigate the development of empathetic strategies for enhancing learners’ learning outcomes. To achieve the aim of this study, prior studies from 2012 and 2022 of several popular databases, including Web of Science, ProQuest, IEEE and ScienceDirect are collected and analyzed. This study found that three dimensions such as cultural, empathetic and humorous dimensions have a positive influence on the application of AI L2 chatbots for enhancing learners’ learning outcomes. This study also found that the development of an AI chatbot in L2 education has plenty of room for improvement. Several recommendations are made for enhancing the use of AI L2 chatbots which include integrating cross-cultural empathetic responses in conversational L2 chatbots, identifying how learners perceive and react to the learning content, and investigating the effects of cross-culture humor on learners’ language proficiency

    A Systematic Review on Artificial Intelligence Dialogue Systems for Enhancing English as Foreign Language Students’ Interactional Competence in the University

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    Previous studies demonstrate that the use of artificial intelligence (AI) dialogue systems for English as a Foreign Language (EFL) education has effectively improved university students' reading, writing, and listening abilities. However, there are limited systematic reviews focused on the evidence-based interactional competence of EFL university students. This study aims to examine the use of AI dialogue systems to enhance EFL university students’ interactional competence. Through the PRISMA process, this study identified 28 articles published between January 2013 and August 2022 in journals and conferences from the most popular databases, including Google Scholar, ProQuest, IEEE, ScienceDirect, and Web of Science. The systematic review identified six dimensions and 25 sub-dimensions that influence the application of AI dialogue systems for EFL learning. The six dimensions include technological integration, task designs, students’ engagement, learning objectives, technological limitations, and the novelty effect. Gaps are identified that (1) components of debate and problem-solving skills in EF acquisition in university education seemed to be overlooked in the AI dialogue system design, and (2) the importance of embedding culture, humor and empathy functions were not taken into consideration in the AI dialogue system. This study finds that the development and implementation of an AI dialogue system in EFL is still in its infancy stage. Future research should emphasize meaning-based communication, intelligibility in language competency, debate, and problem-solving skills in university education

    Work-in-progress - Embedding cross-cultural humorous and empathetic functions to facilitate language acquisition

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    Despite the growing attention paid to conversational chatbots, there is a lack of research in either offering culturally related empathetic strategies when English as additional language learners encounter emotional discomfort or simulating a sense of humour in language acquisition. This paper aims to propose a multivariate normal distribution model for cross-cultural class, BERT for humour detection and response, and NRC VAD to compute empathetic intensity values. The novel algorithm will contribute to significant and long-term outcomes of language learners when exposed to new cultural, cognitive and psychological experiences
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