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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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].”
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]).”
In the citing paper
“These studies revealed that AI-based conversational chatbots significantly improved EFL learners’ engagement.”
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Background
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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