The DOI matches its Crossref record; 68 of the 72 references it cites were found in a registry or an index.
Adding communicative and affective strategies to an embodied conversational agent to enhance second language learners' willingness to communicate
Open accessAbstract
This paper describes an embodied conversational agent enhanced with specific conversational strategies aiming to foster learners’ readiness towards communication in a second language (L2). Willingness to communicate (WTC) in a second language is believed to have a direct and sustained influence on learners’ actual usage frequency of the target language. To help overcome the lack of suitable environments for increasing L2 learners’ WTC, our approach is to build embodied conversational agents that can help learners surmount their apprehension towards communication in L2. Here, we focus on the dialogue management aspects of our approach and propose a model based on a set of communication strategies (CS) and affective backchannels (AB) to foster such agents’ ability to carry on natural and WTC-friendly conversations with learners. We examined learners’ expected WTC after interacting with one of the following versions of the system: an agent featuring both CS and AB; an agent featuring only CS; and an agent featuring only AB. The results suggested that combining CS and AB empowers the conversational agent and leads to higher expected WTC among L2 learners. We also found that even the AB-only version of the system had the potential to enhance WTC to some extent. These findings are evidence of the feasibility of enhancing L2 learners’ engagement towards communication using a computer-based environment coupled with appropriate conversational strategies.
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“Chatbots also acted as learning partners by chatting and interacting with learners through voice ([26,28,52,47,53]) and text ([27,41]).”
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“Furthermore, conversational agents such as MOCA ([26,45]) enhanced learner engagement in EFL collaborative tasks and significantly improved their learning performance [26] but needed a moderate level of motivation [45].”
From Investigating the integration of artificial intelligence in English as foreign language classes for enhancing learners’ affective factors: A systematic review (2024) · cites this paper as [26]
Experimental — accuracy not yet measuredIn the citing paper
“Moreover, AI-based conversational agents enhanced learners’ motivation to communicate in the target language [26].”
In the citing paper
“Furthermore, conversational agents such as MOCA ([26,45]) enhanced learner engagement in EFL collaborative tasks and significantly improved their learning performance [26] but needed a moderate level of motivation [45].”
In the citing paper
“Interactive tools such as chatbots kept learners motivated and engaged, while allowing them to learn EFL in an exciting and relaxing atmosphere ([26,41,42,45]).”
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“These studies revealed that AI-based conversational chatbots significantly improved EFL learners’ engagement.”
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THEORETICAL BASES OF COMMUNICATIVE APPROACHES TO SECOND LANGUAGE TEACHING AND TESTING
CANALE, M., & SWAIN, M. (1980) · Applied Linguistics · doi:10.1093/applin/i.1.1 · Cited by 5,044
Theoretical Bases of Communicative Approaches to Second Language Teaching and Testing
CANALE, M. (1980) · Applied Linguistics · doi:10.1093/applin/1.1.1 · Cited by 4,021
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“Researchers (Nass et al. 1994; Bickmore and Picard 2005; Park and Catrambone 2007) have amply demonstrated that the human-computer relationship is fundamentally social, suggesting that the insights and lessons learned from face-to-face interactions and the above-mentioned language learning theories might also be appli…”
Background
Research works printed but not linked: 6
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Cited by
Sorted by citations
Huang, W., Hew, K. F., & Fryer, L. K. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12610 · Cited by 830
Chaves, A. P., & Gerosa, M. A. (2021) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1841438 · Cited by 572
Zhai, C., & Wibowo, S. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100134 · Cited by 425
Jeon, J., Lee, S., & Choe, H. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104898 · Cited by 309
Artificial intelligence technologies and applications for language learning and teaching
Son, J., Ružić, N. K., & Philpott, A. (2023) · Journal of China Computer-Assisted Language Learning · doi:10.1515/jccall-2023-0015 · Cited by 367
Metadata
- Authors
- Ayedoun, E., Hayashi, Y., & Seta, K.
- Affiliations
- Osaka Prefecture University (JP)
- Corresponding author
- Ayedoun, Emmanuel
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 29
- Issue
- 1
- Pages
- 29-57
- ISSN
- 1560-4292 (ISSN-L)
- OpenAlex
- W2887720395
- Cited by
- 128
- References
- 72
- Citations by year
- 2026: 13 · 2025: 30 · 2024: 25 · 2023: 23 · 2022: 17 · 2021: 13 · 2020: 6 · 2019: 1
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Artificial Intelligence in Education (publishedVersion, cc-by)
- Funders
- Japan Society for the Promotion of Science (18H03345, 18J22445)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Keywords
- embodied conversational agents, willingness to communicate, second language acquisition, communication strategies, dialogue management, computer-assisted language learning, student engagement
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