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

Generative AI (GenAI) is an enhanced fragment of Artificial Intelligence (AI). The emergence of GenAI has engendered numerous impacts on many sectors, including the English language learning and education industry. Motivation, one of the key aspects in language learning, has inspired many scholars as a subject of discussion within language education and second language learning and acquisition. This research sought to determine the correlation between GenAI and EFL Indonesian learners’ perceptions of using GenAI to motivate them in writing. An open-ended and close-ended questionnaire was used to collect quantitative and qualitative data from 30 Indonesian EFL undergraduate students through purposive sampling. The other group of 32 Indonesian EFL undergraduate students participated in the pilot study to test the credibility and validity of research instruments. The result suggests a moderate positive correlation between EFL undergraduate motivation in writing and GenAI’s usage. Qualitative data analysis also demonstrated students’ positive perceptions of using GenAI technology in writing. They found it resourceful for boosting motivation, delivering immediate and personalized feedback, and encouraging self-directed learning. These findings indicate that integrating GenAI into language learning classes, particularly writing, can moderately enhance students' motivation and engagement, thus improving their overall writing skills. The primary limitation stemmed from the small scale and targeted group of participants. Future scholars and researchers are encouraged to explore the influence of a specific GenAI-driven app (e.g., Google Gemini), on another language's core skills, such as speaking or listening, throughout different educational environments and student demographics.

Contents: Introduction. Part I: Important Social Contexts in Research on Second Language Teaching and Learning. Introduction. M.E. Brisk, Bilingual Education. M.R. Hawkins, ESL in Elementary Education. P.A. Duff, ESL in Secondary Schools: Programs, Problematics, and Possibilities. D.E. Murray, ESL in Adult Education. S. Carkin, English for Academic Purposes. P. Master, English for Specific Purposes. C. Roberts, English in the Workplace. B. Tomlinson, English as a Foreign Language: Matching Procedures to the Context of Learning. Y. Kachru, Teaching and Learning of World Englishes. Part II: Methods in Second Language Research. Introduction. L. Harklau, Ethnography and Ethnographic Research on Second Language Teaching and Learning. L. van Lier, Case Study. A. Lazaraton, Quantitative Research Methods. D. Nunan, Classroom Research. A. Burns, Action Research. Part III: Applied Linguistics and Second Language Research. Introduction. T. Pica, Second Language Acquisition Research and Applied Linguistics. S.L. McKay, Sociolinguistics and Second Language Learning. J. Zuengler, K.M. Cole, Language Socialization and Second Language Learning. G. Kasper, C. Roever, Pragmatics in Second Language Learning. J.P. Lantolf, Sociocultural and Second Language Learning Research: An Exegesis. N. Markee, Conversation Analysis for Second Language Acquisition. R.B. Kaplan, Contrastive Rhetoric. S. Conrad, Corpus Linguistics and L2 Teaching. Part IV: Second Language Processes and Development. Introduction. G. Ioup, Age in Second Language Development. R. DeKeyser, A. Juffs, Cognitive Considerations in L2 Learning. Z. Han, L. Selinker, Fossilization in L2 Learners. M. Swain, The Output Hypothesis: Theory and Research. E. Tarone, Speaking in a Second Language. M. Rost, L2 Listening. T.G. Wiley, Second Language Literacy and Biliteracy. P. Byrd, Instructed Grammar. D.E. Eskey, Reading in a Second Language. I.S.P. Nation, Teaching and Learning Vocabulary. J.S. Hedgcock, Taking Stock of Research and Pedagogy in L2 Writing. E. Hinkel, Analyses of Second Language Text and What Can Be Learned From Them. Part V: Methods and Curricula in Second Language Teaching. Introduction. S.J. Savignon, Communicative Language Teaching: Strategies and Goals. S. Fotos, Traditional and Grammar Translation Methods for Second Language Teaching. J. Williams, Form-Focused Instruction. M.A. Snow, A Model of Academic Literacy for Integrated Language and Content Instruction. R. Ellis, Instructed Language Learning and Task-Based Teaching. M. Celce-Murcia, E. Olshtain, Discourse-Based Approaches: A New Framework for Second Language Teaching and Learning. C.A. Chapelle, Computer-Assisted Language Learning. N.J. Anderson, L2 Learning Strategies. Part VI: Second Language Testing and Assessment. T. McNamara, Introduction. A.J. Kunnan, Language Assessment From a Wider Context. A. Davies, C. Elder, Validity and Validation in Language Testing. M. Chalhoub-Deville, C. Deville, A Look Back at and Forward to What Language Testers Measure. T. Lumley, A. Brown, Research Methods in Language Testing. D. Douglas, Testing Languages for Specific Purposes. C. Leung, Classroom Teacher Assessment of Second Language Development: Construct as Practice. Part VII: Identity, Culture, and Critical Pedagogy in Second Language Teaching and Learning. Introduction. T. Ricento, Considerations of Identity in L2 Learning. M. Byram, A. Feng, Teaching and Researching Intercultural Competence. S. Canagarajah, Critical Pedagogy in L2 Learning and Teaching. Part VIII: Language Planning and Policy and Language Rights. R.B. Baldauf, Jr., Introduction. R.B. Baldauf, Jr., Language Planning and Policy Research: An Overview. T. van Els, Status Planning for Learning and Teaching. A.J. Liddicoat, Corpus Planning: Syllabus and Materials Development. R.B. Baldauf, Jr., R.B. Kaplan, Language-in-Education Planning. D.E. Ager, Prestige and Image Planning. S. May, Language Planning and Minority Language Rights.

Task-based language teaching has attracted the attention of second language learning, which was proposed and later developed by second language researchers and educators in reaction to already existing other teacher-dominated, formoriented methods.Since a better understanding of the theories of task-based language teaching is a prerequisite to a better understanding teachers' perceptions and how these perceptions affect their teaching, this paper attempts to deal with the issue of task-based language teaching in the classrooms and second language education.

This study investigates the adoption and impact of Generative Artificial Intelligence (GenAI) tools on English as a Foreign Language (EFL) learners, focusing on self-efficacy, motivation, and predicted exam performance. Using a mixed-methods approach, the research combines survey data (N = 595) and interviews (N = 8) from Chinese university students preparing for the College English Test 4. Quantitative analysis shows that positive attitudes, perceived usefulness, and ease of use predict GenAI usage frequency, while intrinsic motivation negatively correlates with use. GenAI use frequency did not significantly affect predicted test scores or self-efficacy levels. Younger students used GenAI more frequently. Qualitative data reveals that students primarily use GenAI as supplementary learning resources, which, while beneficial, do not substantially enhance self-efficacy or predicted exam performance. The study founds a knowledge gap in the effectiveness of these tools in EFL contexts. It suggests strategies for optimizing GenAI in EFL learning, including blending with traditional methods, providing user training, balancing technology with traditional approaches, ensuring equal access, and customizing GenAI for education. The findings imply a need for educators to supervise GenAI use and for institutions to facilitate effective integration. Future research should examine long-term and cultural effects of GenAI use in education.

Despite the global integration of generative artificial intelligence (GenAI) tools in higher education, limited research exists on how demographic factors such as gender and academic level shape their adoption and usage, particularly in language learning contexts outside Western settings. This study aimed to fill this gap by examining the usage patterns, satisfaction levels, and acceptance factors of GenAI tools among English major students in Indonesian higher education. Employing a mixed-methods approach, the research collected data from 277 students using surveys and structured interviews to gauge both quantitative and qualitative aspects of GenAI tool utilization. The results indicate ChatGPT, Google Translate, and Grammarly as the most utilized tools for writing assistance, language learning, and research tasks, with consistent satisfaction across demographics. Performance expectancy emerged as the most influential acceptance factor, followed by effort expectancy and facilitating conditions, while social influence played a moderate role. Qualitative findings reveal students rely on GenAI for grammar refinement, translation accuracy, content exploration, and idea generation, reflecting critical and reflective engagement. Nonetheless, concerns about overreliance and ethical implications accentuate the need for balanced integration. These findings inform tailored educational strategies, emphasizing ethical use and fostering critical thinking in GenAI adoption for English language education.

Drawing on Bandura’s (1986) ‘triadic reciprocal’ framework of Social Cognitive Theory and incorporating Pekrun’s (2006) Control-Value Theory, this study examines the cognitive–affective mechanisms shaping EFL learners’ experiences in informal generative artificial intelligence (GenAI) environments. The research models the interactions among digital literacy, three emotions, and motivated learning behavior. The sample comprised a GenAI user group (n = 546) and a Non-AI group (n = 563), treating GenAI usage as the moderating environmental factor. Partial Least Squares Structural Equation Modeling (PLS-SEM) and multigroup path analysis were used to test the model. The results indicate that: (1) The ‘digital literacy – emotions – motivated learning behavior’ pathway was confirmed in the full sample, with enjoyment acting as the sole significant mediator among the emotions. (2) A multigroup path analysis identified significant path differences between the groups, except for boredom. Notably, the model demonstrated stronger predictive power for the GenAI group, where digital literacy positively predicted technology anxiety; in contrast, digital literacy reduced anxiety in the Non-AI group. These findings suggest that while GenAI enhances digital literacy and positive affect, it simultaneously introduces specific anxiety challenges. This study extends Social Cognitive Theory to GenAI contexts and offers pedagogical implications for facilitating second language learning.

The rapid development of AI (Artificial Intelligence) is enhancing the potential to accelerate a paradigm shift in foreign language teaching and learning. Instructional design must shift beyond knowledge learning and skills training to include more significant opportunities for practical application in authentic contexts. To do so, it is necessary to investigate the pedagogical possibilities of generative AI through specific educational practices. Thus this study intends to explore the potential of using generative AI to improve the efficiency and effectiveness of PBL (Problem/Project Based Learning) which integrates foreign language, information media, and professional education. This study suggests a PBL course design integrating Chinese language, tourism, and multimedia creation learning outcomes at a Japanese university, as a preliminary phase to compare and examine the teaching and learning processes between traditional methods and proactive AI-enabled methods. Through qualitative exploration of integrating generative AI into traditional instructional design, this paper considers how to organize the elements of teaching and learning for a renewed approach with greater efficiency and quality of learning outcomes.

Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study aims to examine the knowledge, level of satisfaction and perceptions concerning the integration of conversational AI in language learning among future educators. In this mixed method research based on convenience sampling, 176 undergraduates from two educational settings, Spain (n = 115) and Poland (n = 61), interacted autonomously with three conversational agents (Replika, Kuki, Wysa) over a four-week period. A learning module about Artificial Intelligence and language learning was specifically designed for this research, including an ad hoc model named the Chatbot–Human Interaction Satisfaction Model (CHISM), which was used by teacher candidates to evaluate different linguistic and technological features of the three conversational agents. Quantitative and qualitative data were gathered through a pre-post-survey based on the CHISM and the TAM2 (technology acceptance) models and a template analysis (TA), and analyzed through IBM SPSS 22 and QDA Miner software. The analysis yielded positive results regarding perceptions concerning the integration of conversational agents in language learning, particularly in relation to perceived ease of use (PeU) and attitudes (AT), but the scores for behavioral intention (BI) were more moderate. The findings also unveiled some gender-related differences regarding participants’ satisfaction with chatbot design and topics of interaction.

The integration of technology has profoundly influenced language teaching and learning practices. Emerging technologies such as generative artificial intelligence (GenAI) are poised to shift educational paradigms even further. This research examines the use of AI-aided learning tools from the perspective of language teachers. The study engaged teachers from different tiers of schools, both secondary and tertiary institutions, in Hong Kong as participants. These participants evaluated and provided feedback on their overall views on their use of GenAI-aided language teaching and learning. Through a qualitative grounded theory approach to the data set of semi-structured interviews, this study aims to understand language teachers' views of the application of AI tools in classrooms, as well as the overall teaching and learning experience, with consideration to the tiers of schools in which the teachers were employed. Preliminary findings suggested varied opinions on the practicality and impact of AI tools in language education, highlighting both potential benefits and challenges. These insights are crucial for developing innovative teaching strategies that align with contemporary educational needs as well as the continual call for advancement in teacher education. This study contributes to the ongoing discourse on GenAI development in language education, offering valuable implications for curriculum design and instructional practices in secondary education.

The rapid advancement of generative artificial intelligence (GenAI) is reshaping the job landscape by altering firms' labor demands and task requirements across occupations. This study investigates the impact of the launch of GenAI on firms' demand for specific tasks within occupations, focusing on how ChatGPT has influenced the relative desirability of different task types. Building on the task-based approach by Acemoglu and Autor (2011), we classify tasks into routine and non-routine, cognitive and non-cognitive, as well as offshorable and non-offshorable categories. Leveraging the public release of ChatGPT as an exogenous shock, our empirical strategy applies a continuous difference-in-differences approach to estimate the task reallocation effects in job postings across firms with different levels of exposure to ChatGPT. Our findings reveal that GenAI drives firms’ demand for talents engaged in both non-routine cognitive and non-routine manual tasks. Similarly, GenAI accentuates the continued importance of routine-based tasks. In addition, our study also shows that GenAI reduces the offshorability of jobs, shifting the focus toward tasks that require local execution. This study is among the first to provide empirical evidence on how GenAI is altering job content and thus provides profound implications for policymakers and business stakeholders, prompting the need for strategic adjustments and the alignment with the evolving nature of job tasks in the (Gen)AI era.

History is littered with unfulfilled promises that emerging technologies – from radios to televisions, and from computers to mobile phones – would completely transform teaching and learning. Now the same promises are being made of generative artificial intelligence (AI). This presentation argues that we should not be focusing on educational revolution, but instead on educational evolution. Education is a complex social, cultural, and political endeavour, serving multiple purposes and multiple stakeholders, and technology is just one of many elements in this large ecosystem. Focusing on the context of language teaching and learning, this presentation discusses what has changed technologically, and suggests what could and should change educationally. It shows that ChatGPT and a range of other generative AI tools can contribute to language and literacy development in a number of ways, but that we need to be wary of their pedagogical, social, and environmental risks. Educators must develop the AI literacy necessary to take a more nuanced view of generative AI, and we must help our students to do the same. This paper is based on a keynote presentation delivered at the English Australia Conference in Perth, Australia, on 12 September 2024, with some elaborations for the written version alongside minor updates to reflect more recent developments and publications.

The role and implementation of Generative Artificial Intelligence (GenAI) ‎tools have encouraged ‎researchers to delve into this multifaceted area of study. ‎This thematic literature ‎review aims ‎to study the role of GenAI in education, ‎exploring the ‎challenges and ‎benefits in the context of language learning and teaching. ‎This comprehensive literature ‎review is ‎centered on thematic analysis where ‎scholarly publications were selected and ‎analyzed ‎based on predetermined ‎themes including characteristics, challenges, benefits, ‎current ‎practices, and ‎future directions of GenAI in education with a particular ‎emphasis on ‎‎language learning and teaching. The study presents the common ‎characteristics of GenAI ‎pertaining to the aspects of education. The study ‎also highlights the common challenges ‎investigated by researchers ‎such as ‎academic integrity, especially for written assignments, ‎accuracy, overreliance, ‎transparency, privacy‎, and the potential disruption of the existing ‎assessment, ‎which could diminish the perceived importance of acquiring knowledge‎ ‎‎encountered by the major stakeholders in education. The benefits of GenAI ‎include ‎providing personalized education, increasing productivity, providing ‎instant feedback, ‎creating teaching materials efficiently, and improving ‎various language aspects. Given these underlying factors, stakeholders are ‎recommended to integrate and implement GenAI in ‎their education with a ‎conscious consideration of the posed challenges and the benefits to ‎maximize ‎learning and teaching effectively. ‎

As generative artificial intelligence (AI) tools and large language models (LLMs)-powered applications develop rapidly in the era of algorithms, it should be integrated thoughtfully to enhance English as a Foreign Language (EFL) teaching and learning without replacing learners’ critical thinking (CT). This study systematically analyzes the impact of generative AI tools and LLMs on language learners’ CT in EFL education using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to identify, evaluate, and synthesize relevant studies from 2022 to 2025. A thorough review of 15 selected studies focuses on generative AI tools and LLMs’ dual nature, research methods, main focuses, theory and models, limitations and challenges, and future directions in the field based on Web of Science (WoS), SCOPUS, ERIC, ProQuest, and Google Scholar. The findings identified generative AI tools and LLMs possessed both the potential to nurture and the risk of hindering CT in EFL education. 66.67% of studies reported generative AI tools and LLMs’ positive role in CT, while 33.33% of studies reported its negative role in CT. Furthermore, 3 types of research methods, 3 key themes of research focus, and 4 groups of theoretical perspectives were examined. However, 4 kinds of limitations in this field remain, including research scope, user dependency, generative AI reliability, and pedagogical integration. Future research can focus on assessing long-term effects, broadening research scope, promoting responsible AI use, and refining pedagogical strategies. Finally, Limitations, implications and future direction of this study were discussed.

Integration of computers into Foreign Language (fl) learning has become the allbut-inevitable direction for the future, given the (a) considerable benefits this affords for students of English as a Foreign Language (efl), (b) characteristics of today’s learners as Generation Z (González-Lloret & Ortega, 2014), and (c) widespread use of the internet in the twenty-first century. This situation has spurred a transformation of ComputerAssisted Language Learning (call) into Web-Facilitated Language Learning (wfll) as an alternative paradigm for efl teachers and learners. Furthermore, Task-Based Language Teaching (tblt) is likely to serve as a pedagogical framework in designing the web for the purpose of fl learning. The primary objectives of the present study were therefore to (a) develop a teacher-designed learning website, namely I Love Indonesia, and (b) gather information from high school learners of English in Indonesia with different attitudes towards call (positive/moderate/negative) in correlation with how they perceive wfll (agree/disagree) and perform web-based activities. Descriptive Statistics, if Function in Excel, Correlation Analysis, and Independent-samples T-test were employed in the study. Finally, the study findings demonstrate that (a) the majority of learners participating in the study have positive attitudes towards call, (b) all focused participants have positive perceptions of wfll and (c) learners with positive attitudes are likely to have more positive perceptions (to agree) than moderate and negative attitude learners (ones who disagree) on the use of the website for language learning. In addition, the website seems to benefit efl learners in some specific areas. Consideration should be given to working with 158 The jalt call Journal 2018: Regular Papers a greater number of learners over a longer period of time when conducting further studies on the effectiveness of the website for efl learners in order to be able to shed some light on the development of their language skills.

Generative artificial intelligence (GenAI) has emerged as a powerful tool in the Informal Digital Learning of English (IDLE) environment, offering personalized, interactive, and innovative language learning experiences that may enhance learning engagement. However, the influencing path of perceptions and acceptance of GenAI on the learning engagement of English‐as‐a‐foreign‐language (EFL) students has not been sufficiently explored. Specifically, little is known about how cognitive and motivational factors jointly mediate this relationship. Self‐efficacy in language learning has been found to play a critical role in regulating motivation through resilience, perseverance, and goal determination, thereby influencing the quality and quantity of effort that students invest in engaging with GenAI‐assisted IDLE. This study investigated the effects of GenAI perceptions and acceptance on IDLE engagement among 369 university EFL learners. Using structural equation modeling (SEM), the study analyzed both the direct effects of perceptions and acceptance of GenAI on learning engagement and the serial mediating roles of self‐efficacy and motivation. The results showed that (1) GenAI perceptions and acceptance exerted a significant and positive direct effect on learners’ engagement; (2) motivation significantly mediated the relationship between GenAI perceptions and acceptance and engagement; and (3) self‐efficacy indirectly enhanced engagement by increasing motivation, forming a chain mediation pathway. These findings provide insights into how self‐efficacy and motivation jointly mediate the relationship between GenAI perceptions and acceptance and learning engagement and offer guidance for the implementation of GenAI‐enhanced tools and personalized learning strategies in fostering student autonomy and optimizing engagement in IDLE environments.