Volume 28 Number 2, June 2024 Special Issue: Artificial Intelligence for Language Learning
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ItemCall for papers for a special issue on Emotional CALL(University of Hawaii National Foreign Language Resource Center, 2024-06-01)
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ItemAutomated versus peer assessment: Effects of learners' English public speaking(University of Hawaii National Foreign Language Resource Center, 2024-06-01)This quasi-experimental research investigates the employment of a formative assessment platform aided by artificial intelligence in an English public speaking course. The platform integrates deep learning, automatic speech recognition, and automatic writing evaluation. It provides automated assessment and immediate feedback on speakers’ public speaking anxiety and their speaking and writing competence. Fifty-two English public speaking learners were randomly assigned to two groups. The control group (G1) undertook self-, peer, and teacher assessment via the platform, while the experimental group (G2) experienced self-, automated, and teacher assessment. The ANCOVA results revealed that students in G1 perceived significantly higher social engagement than those in G2, which indicates that social interaction between learners during peer assessment cannot be substituted by automated assessment. The chi-square analysis showed students’ different concerns regarding online formative assessment. While students in G1 showed concerns for peers’ qualifications and willingness to provide feedback, students in G2 suggested generating more detailed automated feedback to improve self-learning. No significant differences were found in learners’ English public speaking self-efficacy, engagement, or competence. This indicates that automated assessment can serve as an effective strategy for formative assessment and that AI tools may supplement peers as reliable learning companions in the foreseeable future.
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ItemExploring AI-Generated text in student writing: How does AI help?(University of Hawaii National Foreign Language Resource Center, 2024-06-01)English as a foreign language (EFL) students’ use of artificial intelligence (AI) tools that generate human-like text may enhance students’ written work. However, the extent to which students use AI-generated text to complete a written composition and how AI-generated text influences the overall writing quality remain uncertain. 23 Hong Kong secondary school students wrote stories with AI-writing tools, integrating their own words and AI-generated text into the stories. We analyzed the basic structure, organization, and syntactic complexity of each story and its AI-generated text. Experts scored the quality of each story’s content, language, and organization. By employing multiple linear regression and cluster analyses, we found that both the number of human words and the number of AI-generated words significantly contributed to writing scores. Furthermore, students could be classified into competent and less competent writers based on the variations of students’ usage of AI-generated text compared to their peers. Cluster analyses revealed some benefit of AI-generated text in improving the scores of both high-scoring students’ and low-scoring students’ writing. We suggest differentiated, pedagogical strategies for EFL students to effectively use AI-writing tools and AI-generated text to complete writing tasks.
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ItemTeacher engagement with automated text simplification for differentiated instruction(University of Hawaii National Foreign Language Resource Center, 2024-06-01)Differentiated instruction is much demanded yet quite challenging in face of the growing student diversity in today’s K-12 classrooms. One major challenge is the provision of differentiated materials to students. Automated text simplification (ATS) tools fueled by natural language processing may serve as a useful assistant for teachers. However, little is known about teachers’ contextualized use of ATS over time. This case study traced two teachers’ use of ATS systems over a semester. Drawing upon three semi-structured interviews and teacher-generated materials with ATS, we identified an evolving pattern of teachers’ engagement with ATS systems, a progression from a blind reliance on the tool to a more critical and coordinated use of the tool over time. We further revealed that teachers’ evolving understanding of DI, positioning of the role of ATS systems and human instructors, and interpretation of DI need in specific teaching situations interplayed to shape their particular ways of engagement. Overall, this study contributes to the understanding of teachers’ contextualized use of ATS technology for DI. By revealing the influencing factors, the findings hold significant pedagogical implications to inform the design of ATS tools and the creation of favorable conditions to maximize the potential of ATS tools for DI and language teaching and learning in general.
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ItemThe effects of AI-guided individualized language learning: A meta-analysis(University of Hawaii National Foreign Language Resource Center, 2024-06-01)Artificial intelligence (AI) has considerably advanced the methods for individualizing language learning opportunities, such as assessing learning progress and recommending effective individual instruction. In the present study, we conducted a meta-analysis to synthesize recent empirical findings pertaining to the utilization of AI-guided language learning and collected 61 samples (N = 8,282) from 17 research projects (e.g., Assessment to Instruction [A2i], Duolingo, and Project LISTEN). The results of our meta-analysis confirmed that AI-guided individualized language learning was effective for learners’ language development (d = 1.18, based on 26 within-group samples, N = 2,262) and had an overall positive treatment effect compared to business-as-usual conditions (d = 0.39, based on 35 between-group samples, N = 6,020). Moreover, the results of our moderator analyses for the treatment effect revealed that AI-guided language learning with machine learning and hybrid systems were more impactful than those with rule-based systems, which may be more helpful (compared to the former) in understanding how predictions are made from a pedagogical perspective. Evidence-based implications are provided based on the results of this meta-analysis.
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ItemEffects of learner uptake following automatic corrective recast from Artificial Intelligence chatbots on the learning of English caused-motion construction(University of Hawaii National Foreign Language Resource Center, 2024-06-01)This study investigated the instructional effects of learner uptake following automatic corrective recast from artificial intelligence (AI) chatbots on the learning of the English caused-motion construction. 69 novice-level EFL learners in a Korean high school were recruited to investigate the instructional effects of corrective recast from AI chatbots on the learning of the English caused-motion construction. Results from the elicited writing tasks (EWT) revealed that statistically significant gains were observed in both immediate and delayed posttests for the production of the English caused-motion construction by experimental group participants. Also, the relationship between learner uptake from AI chatbots’ corrective recast and the learning of the English caused-motion construction were analyzed. The results demonstrated that learners’ successful repair from AI chatbots’ corrective recast was positively correlated with the learning gains in the two EWT posttests. The study concludes by highlighting the significance of noticeability in AI chatbots’ corrective feedback for foreign language learning.
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ItemTeaching foreign language with conversational AI: Teacher-student-AI interaction(University of Hawaii National Foreign Language Resource Center, 2024-06-01)This study investigated the usage of conversational artificial intelligence (CAI) to support learners in foreign language classrooms. It employed Google Assistant and focused on the interactions between the teacher, learners, and CAI, as well as the teacher’s collaboration with CAI. Using social network and content analyses of two 50-minute language classes and group interviews, this study revealed that the teacher and CAI played a significant role during classroom interactions. The teacher employed various talk moves to facilitate interactions between the students and CAI. There were several instances of collaboration between the teacher and CAI during classroom facilitation. This study highlights the implications of the collaboration between human teachers and CAI in classrooms for teaching foreign languages and suggests avenues for future research.
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ItemUsing chatbots to support EFL listening decoding skills in a fully online environment(University of Hawaii National Foreign Language Resource Center, 2024-06-01)Aural decoding skill is an important contributor to successful EFL listening comprehension. This paper first described a preliminary study involving a 12-week undergraduate flipped decoding course, based on the flipped SEF-ARCS decoding model. Although the decoding model (N = 44) was significantly more effective in supporting students’ decoding performance than a conventional decoding course (N = 36), two main challenges were reported: teacher’s excessive workload, and high requirement for the individual teacher’s decoding skills. To address these challenges, we developed a chatbot based on the self-determination theory and social presence theory to serve as a 24/7 conversational agent, and adapted the flipped decoding course to a fully online chatbot-supported learning course to reduce the dependence on the teacher. Although results revealed that the chatbot-supported fully online group (N = 46) and the flipped group (N = 43) performed equally well in decoding test, the chatbot-supported fully online approach was more effective in supporting students’ behavioral and emotional engagement than the flipped learning approach. Students’ perceptions of the chatbot-supported decoding activities were also explored. This study provides a useful pedagogical model involving the innovative use of chatbot to develop undergraduate EFL aural decoding skills in a fully online environment.
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ItemOpenings and closings in human-human versus human-spoken dialogue system conversations(University of Hawaii National Foreign Language Resource Center, 2024-06-01)Although conversation openings and closings are ritualized speech acts (House & Kádár, 2023), they do require interactional work (Schegloff, 1986). Thus, they are important elements of interactional competence (Roever, 2022) and have been studied extensively in L2 interactions, including various types of technology-mediated communication contexts (e.g., Abe & Roever, 2019; 2020). However, to our knowledge, no research on openings and closings has been conducted with newer technologies such as spoken dialogue systems (SDS). To address this gap, this study compares conversation openings and closings across two modalities: a role-play with a human interlocutor versus with a fully automated agent. We analyzed interactional data from 47 tertiary-level learners of English. A quantitative (e.g., number of turns) and a qualitative, discursive analysis rendered several key findings: 1) learners were more transactionally oriented in SDS modality, but tended to engage in relational discourse with a human interlocutor; 2) humans adapted to the emergent discourse in both modalities; 3) despite training, the human interlocutor was inconsistent in displaying transactional versus interactional patterns with different participants, while the SDS followed the same dialogue structure in each interaction. Findings will be discussed in terms of specific affordances of the two modalities for interactional competence.
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ItemDistributed agency in second language learning and teaching through generative AI(University of Hawaii National Foreign Language Resource Center, 2024-06-01)Generative AI offers significant opportunities for language learning. Tools like ChatGPT provide second language practice through chats in written or voice formats, with the learner specifying through prompts conversational parameters. AI can be instructed to give corrective feedback and create practice exercises. Using AI, instructors can build learning and assessment materials in a variety of media. Generative AI provides affordances for both autonomous and instructed learning. In addition, AI is poised to enhance dramatically the usefulness of immersive technologies. For both learners and teachers, it is important to understand the limitations of AI systems that arise from their statistical model of human language, which constrains their capacity for dealing with sociocultural aspects of language use. Additionally, there are ethical concerns over how AI systems are created and deployed, as well as practical constraints in their use, especially for less privileged populations. Nevertheless, the power and versatility of AI tools are likely to turn them into constant companions in many people’s lives, creating a close connection that goes beyond simple tool use. Ecological theories such as sociomaterialism are helpful in examining the shared agency that develops through close user-AI interactions, as are the perspectives on human-tool relationships from Indigenous cultures.