All Journals

Language Teaching Research Quarterly

European Journal of Studies in Management and Business

International Journal of Behavior Studies in Organizations

Studies in Educational Management

New Challenges in Accounting and Finance

Language Testing in Focus: An International Journal

Marketing and Branding Research

Individual Differences in Language Education: An International Journal 

Feedback Research in Second Language 

Management Issues in Healthcare System

Language Teacher Education Research

Artificial Intelligence Knowledge Development

<span style="font-size: 10.0pt; font-family: 'Cambria',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Calibri; letter-spacing: -.15pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-bidi-font-style: italic;">Although the findings of teaching method studies conducted to date have offered insights into the positive influence of novel teaching strategies on improving students&rsquo; academic performance, there is no conclusive evidence addressing the effect of integrated teaching methods on pupil&rsquo;s academic self-efficacy, academic engagement, and academic performance. This study compares the differential effects of employing an integrated teaching method on enhancing primary school students&rsquo; academic attitudes. To set the scene, 40 6th-grade students were randomly assigned to two groups: an experimental group trained using the integrated teaching method (n = 20) and a control group that stuck to the traditional teaching method (n = 20). The participating groups took Jinks and Morgan&rsquo;s academic self-efficacy, Fredricks and Blumenfield&rsquo;s academic engagement, and teacher-made academic performance pretests. Having completed 8 sessions of integrated teaching, students took the aforementioned tests as posttests. The data analysis demonstrated the effectiveness of the integrated teaching method in increasing the variables, as the participants in the control group outperformed them. The findings of the study act as a driving force for potential educational implications which are discussed in detail.

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Original Research 

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Effect of Integrated Teaching Method on Elementary Students’ Academic Self-Efficacy, Academic Engagement, and Academic Performance

As an L2 learner and teacher, you have often asked yourself why some students acquire a second language (L2) more successfully than others. On the other hand, other students seem to find difficulty with similar material. You might have noticed that some students are quick to engage with a task while others appear to disengage. How can language learning be structured in the face of these diverse responses? You might further question why the L2 acquisition of speaking or grammar (for example) is so easily taught through tasks, while the L2 acquisition of writing or listening comprehension seems resistant to this approach. These questions lie at the heart of teachers, learners, researchers, and policy makers who wish to improve L2 education. Nonetheless, there are two main aspects of L2 learning: Individual Differences (IDs) and Task-Based Language Teaching (TBLT). First, IDs are broadly defined as thepersonal characteristics that make learners differ from one another in how they approach and succeed in language learning (Skehan, 1989).

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Book Review

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Book Review: Individual Differences and Task-Based Language Teaching (2024), Edited by Shaofeng Li (John Benjamins)

Individual Differences in the AI and Digital Era: The Impact of ChatGPT and Beyond

Guest Editors:
Zhisheng (Edward) Wen 
The Hong Kong Polytechnic University - CPCE, Hong Kong SAR, China

Richard L. Sparks
Mt. St. Joseph University, USA

Hassan Mohebbi 
European Knowledge Development Institute, Turkey


Special Issue

For a long time, motivation and anxiety, as traits of individual differences (IDs), have been understood as significant factors in learning a second language (L2) successfully. Scholars conceptualized these two traits as learners&rsquo; internal traits. In this article, we, as the authors, use sociocultural theory (SCT) to revisit these traits and reconceptualize them. Therefore, we argue that these traits are not merely unchanged qualities; instead, they are the outcome of a learner&rsquo;s engagement in a socially meaningful practice during the L2 learning process. Meanwhile, current research findings show that other factors, such as linguistic aptitude, L1 literacy, and L2 metalinguistic skills, play a part in L2 learning. These factors are not only applicable to a traditional classroom environment, but we believe that with the emergence of AI-mediated learning tools, such as chatbots, adaptive learning platforms, and AI tutors, they can be used as scaffolding techniques for learners&rsquo; participation. We further argue that if AI tools are used systematically to meet learners&rsquo; needs, they can enhance learning processes, and this can reduce L2 anxiety and increase motivation. A sociocultural perspective, therefore, provides a productive way to understand how motivation and anxiety unfold in AI-mediated language learning, while remaining attentive to the linguistic resources that adult learners bring with them.

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Perspective Article

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Reconceptualizing Individual Differences through the Lens of Sociocultural Theory in AI-Mediated Language Learning

The rapid development of Large Language Models (LLMs) has offered new opportunities for personalized education while raising concerns about bias amplification, equity gaps, and the digital divide. This quasi-experimental study explores LLM applications in high school Computer Science (CS) education, focusing on bias mitigation and bridging to higher education. A sample of 616 students from a key high school in northern China was randomly assigned to an experimental group (LLM personalized instruction, n=308) or a control group (traditional instruction, n=308). The 8-week intervention built CS skills through progressive tasks from basic syntax to comprehensive projects, aligned with university domains (e.g., ACM CS2023). Methods integrated UNESCO (2025) and OECD (2025) guidelines, emphasizing low-bandwidth solutions and bias mitigation strategies such as prompt engineering and multi-model comparison. Data analysis combined quantitative approaches (t-tests, ANOVA, bridging index) and qualitative NVivo thematic coding to assess performance gains, subgroup equity, and bias indicators (MAB and MDB). Results showed significant improvements in the LLM group (p&lt;0.05), with a 15% average bridging index and 80%&plusmn;5% bias mitigation rate. However, urban-rural and gender biases still require further optimization. This study provides empirical insights into responsible LLM use in education and proposes policy frameworks and model optimization (unified super models vs. mixture of experts) to advance global equity and ethical AI integration.

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Personalized Applications of Large Language Models in Pre-University Computer Science Education: Bridging Global Equity, Mitigating Bias, and Addressing Structural Challenges

There is a growing concern that the increased efficiency of using Generative Artificial Intelligence (GAI) text in the completion of educational tasks related to English language learning may come at the cost of whether or not students develop real language proficiency. This study investigated the perceptions and usage patterns of ten English-majored university students - whose English proficiency levels ranged from A2 to C1 (as designated by The Common European Framework of Reference for Languages - CEFR) - in their use of ChatGPT and other AI tools for coursework and self-study. Findings from interviews revealed notable individual differences. However, while all students acknowledged improved efficiency through GAI use, their perceived impact on language proficiency varied. Some A2 or B1 learners used GAI for speaking practices. Other students, particularly C1-level, perceived AI generated text as a tool for improving efficiency rather than language proficiency. These findings highlight the need to distinguish proficiency from efficiency and account for individual differences. Implications are made for a pedagogy that includes GAI-integrated language instruction.

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Efficiency ≠ Proficiency: College English as a Foreign Language Students’ Perceptions on the Use of Generative AI

The integration of artificial intelligence (AI) into task-based language teaching (TBLT) promises personalized learning at scale. Yet without grounding in the rich tradition of individual differences (IDs) research, AI risks reducing learners to simplified variables and substituting algorithmic accommodation for genuine cognitive engagement. This paper builds a bridge between established ID-TBLT research and emerging AI design. We critically synthesize how cognitive IDs (working memory, attentional control, language aptitude) and affective IDs (motivation, anxiety, enjoyment, boredom, and flow) interact with task complexity to shape L2 performance&mdash;acknowledging both established findings and persistent debates. From this synthesis, we articulate design challenges for AI-mediated TBLT, distinguishing what is technically feasible from what requires further research, and acknowledging inherent tensions (scaffolding vs. substitution, personalization vs. equity, detection vs. privacy, flow vs. instructional efficiency). We then extend this framework through Bui's (2026) longitudinal study of learner perceptions in AI-assisted speaking tasks, using empirical findings on learner adaptation and "prompt literacy" to illustrate and refine our design challenges. The result is a dual contribution: a systematic translation of ID-TBLT research into AI design challenges, and a refined, six-frontier research agenda for developing intelligent, adaptive TBLT systems that are cognitively grounded, affectively attuned, and ethically responsible.

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Individual Differences in Task-Based Language Teaching: A Critical Synthesis for AI-Mediated Learning Design

The rapid integration of generative AI into language education has exposed a dual challenge: a &ldquo;grounding gap&rdquo; in its cognitive shallowness, and a profound ethical peril that such technology may accommodate rather than empower learners. This editorial interrogates whether a bridge is possible. We argue that working memory (WM)&mdash;empirically central to language aptitude and learning&mdash;offers the most viable, if fraught, interface for such a bridge. We introduce the Cognitive Load&ndash;WM Interaction (CLWM) Matrix not as a solution, but as a critical heuristic and necessary safeguard. It is designed to enforce a key distinction: between AI that grounds learning by managing cognitive load and AI that bypasses cognitive effort. From this, we derive a dual-path risk-aware research agenda, focused on developing WM-aware pedagogical specifications and probing hybrid AI architectures. The conclusion is not a blueprint, but a condition: progress in AI must be subordinated to progress in understanding and protecting the human cognitive processes it seeks to engage.

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Editorial

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Toward Cognitively Grounded AI: Can a Working Memory Interface Bridge Individual Differences and System Design?

Standardized testing has been increasingly criticized as a form of student evaluation due to its lack of focus on student individuality. In response, calls for authentic student assessment have encouraged educators, like Angela Stockman, to challenge traditional methods of student evaluation in favor of holistic approaches. Her book, The Writing Teacher&rsquo;s Guide to Pedagogical Documentation: Rethinking How We Assess Learners and Learning, extends those critiques; her intervention suggests pedagogical documentation of learning processes as a nuanced replacement for evaluating students. Stockman&rsquo;s 3-part book is a comprehensive framework for instructors looking to adopt a fresh perspective on student evaluation, offering a welcome change from traditional methods of assessment and instead emphasizing personal growth and student-centered learning.

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The writing teacher’s guide to pedagogical documentation: Rethinking how we assess learners and learning by Angela Stockman

This study examines the influence of teachers&rsquo; behaviors on students&rsquo; foreign language speaking anxiety (FLSA) in Turkish middle school classrooms. Conducted with 55 students and two non-native English teachers over 14 weeks, the research included classroom observations, semi-structured interviews, and a foreign language anxiety scale. The findings reveal that teachers' behaviors have a moderately significant impact on students' anxiety levels, with positive behaviors reducing FLSA and negative behaviors exacerbating it. The results highlight discrepancies between teachers&rsquo; self-perceptions and their observed classroom practices, underscoring the need for greater teacher awareness of their impact on students&rsquo; learning experiences. This research contributes to the literature on FLSA by focusing on young learners in a Turkish context, emphasizing the importance of teacher attitudes and strategies in mitigating anxiety and fostering effective oral communication. The research took place in a Turkish state school classroom with 55 students and two non-native English language teachers over a period of 14 weeks. The findings concluded that teachers&rsquo; behaviours have an important impact, albeit small, on students' speaking anxiety levels, where positive behaviours decrease FLSA, while negative behaviours increase it. The outcomes also highlight a mismatch between teachers' self-perception and their classroom experiences.

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The Influence of Teachers’ Behaviour on Learners’ Foreign Language Speaking Anxiety

This paper explores the functional-semantic methodology of foreign language teaching for non-philologists. It surveys schools in Kazakhstan, the USA and some European countries and the grammatical and semantic features of English used for professional communication. In surveying programmes, methodologies, and resources, fundamental concepts for professional grammar and vocabulary were established. Evidence suggests that using speech patterns in professionally orientated communication enhances learning in foreign languages. Transformative practices facilitate the improvement of communicative competence by incorporating flexible grammar patterns and transforming language units into &ldquo;real life&rdquo; scenarios. A semantic study shows that professionally orientated thematic vocabulary fields help professionals remember and use language units. The functional-semantic methodology structures pedagogical information coherently and maximises the relationship between form, meaning, and scenarios. The Kazakhstani experience shows that tourism professionals should be trained in intercultural communication, professional vocabulary and terminology, and grammatical transformations. The functional-semantic methodology is a foreign language teaching methodology for professionally orientated learning and offers insightful curriculum and resource recommendations, the paper concludes.

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Teaching Foreign Language Communication in the Context of a Functional and Semantic Approach

Integrating artificial intelligence into translation studies has proven to enhance learners&rsquo; translation skills and practice. However, overreliance on this technological tool may give rise to various translation-related issues. This study, therefore, investigates both the benefits and potential risks associated with the use of ChatGPT in translation training for Vietnamese English-major students. It also aims to propose some pedagogical approaches that position AI as more of a scaffolding tool than a substitute in translation training at a university in Vietnam. Sixty third-year English-major students participated in the study and were divided into two groups, with the control group completing the translation tasks mannually while the experimental group utilized ChatGPT for both learning and assessment. Pre-tests and post-tests were used, and the assessment was based on EMT (European Master&rsquo;s in Translation) dimensions, including language, intercultural, thematic, and strategic competences. Semi-structured interviews were also carried out to explore student perception about the intervention. Findings indicate that ChatGPT improved translation quality by enhancing students&rsquo; language, thematic, and strategic competencies; however, it did not really influence their intercultural competence. The problems of pseudo-efficiency and learner autonomy remain a concern.

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When Support Becomes Dependency: Investigating ChatGPT’s Role in Shaping EFL Students’ Translation Competence

This study investigates the application of Artificial Intelligence (AI) in a Problem-Based Learning (PBL) environment, with emphasis on information retrieval in the field of public technology procurement, from the perspective of risk management and the use of gamified strategies. The research followed a qualitative/quantitative approach in five stages: development of a case study supported by AI; creation of an original board game to simulate real decision-making situations; identification and categorization of perceived risks; application of a structured Likert-scale questionnaire to measure perception; and critical discussion of the results. This research involved professionals directly engaged in ICT acquisition processes and demonstrated that the use of games adapted to the organizational reality (serious game) enhanced engagement, supported knowledge retention, and broadened awareness of relevant risks. Among the main findings were concerns with supplier lock-in and cost underestimation, as well as the high acceptance rate (93.8%) of the gamified experience compared to the traditional expository model. It is concluded that the integration of AI, gamification, and risk management constitutes an innovative and replicable methodology, capable of strengthening training in complex governmental environments, while fostering effective communication and more informed decision-making.

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Artificial Intelligence Applied in Problem-Oriented Learning for Information Retrieval in a Gamified Environment