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Applying Generative Artificial Intelligence to Task-Based Language Teaching and Learning: A Systematic Review and Meta-Analysis

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Title: Applying Generative Artificial Intelligence to Task-Based Language Teaching and Learning: A Systematic Review and Meta-Analysis
Language: English
Authors: Yan Li, Rustam Shadiev (ORCID 0000-0001-5571-1158), Thomas K. F. Chiu (ORCID 0000-0003-2887-5477)
Source: TechTrends: Linking Research and Practice to Improve Learning. 2026 70(1):150-163.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 14
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Information Analyses
Descriptors: Artificial Intelligence, Technology Uses in Education, Task Analysis, Teaching Methods, Second Language Instruction, Second Language Learning, Meta Analysis, Digital Literacy, Planning, Teacher Education
DOI: 10.1007/s11528-025-01140-7
ISSN: 8756-3894
1559-7075
Abstract: Generative artificial intelligence (GenAI) has received increasing attention from language researchers and educators due to its capability to influence student language performance. Very limited previous meta-analysis studies have examined instructional factors in their analysis, while it deserves great attention. Task-based language teaching (TBLT) is a widely applied language instructional approach. Hence, this systematic review and meta-analysis study adopts TBLT to investigate how these instructional factors (e.g., task-based variables, learner variables) impact student language learning in GenAI contexts. Using the PRISMA guidelines, we collected literature and included 25 empirical studies involving 2,431 participants. The study confirmed the positive influence of GenAI on language learning and identified three moderators--student AI literacy, task planning, and task assessors--that potentially moderate its effectiveness. This finding likely relates to the GenAI tools employed; specifically, ChatGPT--not primarily designed for language learning--was used most frequently. This general-purpose GenAI may require students to understand how AI works, how to plan their tasks, and the necessity of involving teachers as assessors. We suggest teachers should get more involved in language learning with GenAI, and more language learning-specific GenAI should be developed. These suggestions help institutions and teachers to plan their instructions in the AI era.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1505833
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Yan+Li%22">Yan Li</searchLink><br /><searchLink fieldCode="AR" term="%22Rustam+Shadiev%22">Rustam Shadiev</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5571-1158">0000-0001-5571-1158</externalLink>)<br /><searchLink fieldCode="AR" term="%22Thomas+K%2E+F%2E+Chiu%22">Thomas K. F. Chiu</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-2887-5477">0000-0003-2887-5477</externalLink>)
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Generative artificial intelligence (GenAI) has received increasing attention from language researchers and educators due to its capability to influence student language performance. Very limited previous meta-analysis studies have examined instructional factors in their analysis, while it deserves great attention. Task-based language teaching (TBLT) is a widely applied language instructional approach. Hence, this systematic review and meta-analysis study adopts TBLT to investigate how these instructional factors (e.g., task-based variables, learner variables) impact student language learning in GenAI contexts. Using the PRISMA guidelines, we collected literature and included 25 empirical studies involving 2,431 participants. The study confirmed the positive influence of GenAI on language learning and identified three moderators--student AI literacy, task planning, and task assessors--that potentially moderate its effectiveness. This finding likely relates to the GenAI tools employed; specifically, ChatGPT--not primarily designed for language learning--was used most frequently. This general-purpose GenAI may require students to understand how AI works, how to plan their tasks, and the necessity of involving teachers as assessors. We suggest teachers should get more involved in language learning with GenAI, and more language learning-specific GenAI should be developed. These suggestions help institutions and teachers to plan their instructions in the AI era.
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