Chatbot learning partners: Connecting learning experiences, interest and competence
By Luke K. Fryer, Kaori Nakao, Andrew Thompson. Computers in Human Behavior, 2019
410 citations recorded by the source corpus.
Abstract Conversation practice, while paramount for all language learners, can be difficult to get enough of and very expensive. In this mobile age, chatbots are an obvious means of filling this gap, but have yet to realize their potential as practice partners. The current study was undertaken to examine why chatbots are not yet a substantial instrument for language learning engagement/practice, and to provide direction for future practice and chatbot development. To this end, building on a recent experimental study examining chatbot novelty effects, students undertook a pair of conversation activities: human and human-chatbot (via speech-to-text software). Immediately following the practice conversations, students' interest in the two partners was surveyed and open-ended textual feedback was collected. With these data sources and prior standardised test results, regression and content analysis of the data was undertaken. Findings indicated: 1) prior interest in human conversation partners was the best single predictor of future interest in chatbot conversations; 2) prior language competency was more strongly linked to interest in chatbot than human conversations; 3) that the qualitative experience of having “learned more” with the chatbot was strongly connected to task interest, even when reporting communication difficulties. Implications for practicing languages with currently available chatbots, for chatbots and related educational technology as sources of student interest and directions for chatbots future development are discussed.
Subfields: Artificial Intelligence, Social Psychology
View Paper: https://www.semanticscholar.org/paper/70724314ca597ffca142cc6ab67f496605d79161
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Citations in the corpus
- Teachers’ attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics (2021, background): “These educational tasks include the facilitation and adaptation of content, such as knowing the difference between two concepts or practicing a language by chatting with a mobile phone (Fryer, Nakao, and Thompson 2019; Lu et al. 2006).”
- Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance (2020, background): “However, like any new technological innovation there is a need to examine the novelty effect as interest and value perception may decrease over time (Fryer et al., 2019; Rodr (cid:2) ıguez-Aflecht et al., 2018).”
- Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot (2023, neutral): “Their role within classroom learning, particularly in language education, is tangential (Fryer et al., 2019; Shah et al., 2016; Yin et al., 2021).”
- What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education (2023, background): “In this context, Fryer et al. (2019) mentioned that students’ competencies in using chatbots affect their future experiences and motivation when interacting with conversational agents.”
- Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT (2023, background): “…general-purpose chatbots that can engage in daily, simple conversation with users via question-and-answer dialogue structures (e.g., Dizon, 2020; Fryer et al., 2019), (2) specific-purpose chatbots with more complex structures designed by commercial developers for language learning (e.g., Wang…”