Journal of English Language Teaching and Applied Linguistics

Evaluating The Effectiveness of The Intercultural Communication Needs (ICN) Model on Developing Intercultural Competence Among Iranian EFL University Students

Vahid Ghorbani — Tue, 10 Feb 2026 00:00:00 +0700

Background: Intercultural communicative competence (ICC) has become a central objective in English as a Foreign Language (EFL) education due to increasing global interaction. However, EFL instruction in Iran has traditionally prioritized grammatical and lexical accuracy, offering limited opportunities for intercultural development. To address this gap, the Intercultural Communication Needs (ICN) model integrates three core macro-needs, language, emotion, and culture, into instructional practice
Aims: This study examined the effectiveness of the ICN model in enhancing ICC among Iranian university EFL learners.
Methods: A quasi-experimental pretest–posttest design was employed with 100 undergraduate students divided into experimental and control groups. Over a six-week period, the experimental group received ICN-based instruction, while the control group followed conventional EFL instruction. Data were collected using a researcher-developed 15-item ICC questionnaire measuring linguistic, emotional, and cultural needs. Paired-samples and independent-samples t-tests were conducted, and effect sizes were calculated
Results: The results indicated a significant improvement in the experimental group’s overall ICC, with a large effect size (Cohen’s d = 1.735). Among the ICN components, language showed the greatest gains, followed by emotion and culture. In addition, students reported increased confidence, empathy, and curiosity toward intercultural interaction
Conclusion: The findings provide empirical support for the ICN model as an effective pedagogical framework for fostering holistic intercultural competence in EFL contexts and highlight the importance of integrating linguistic, emotional, and cultural dimensions in language instruction.

Iranian EFL Instructors’ Perspectives on Integrating Artificial Intelligence Applications into English Language Teaching and Learning

Hossein Isaee — Wed, 25 Feb 2026 00:00:00 +0700

Background: AI tools are increasingly recognized for their potential to enhance language acquisition, personalize instruction, and improve classroom efficiency.
Aims: This study investigates the perceptions of English as a Foreign Language (EFL) instructors in Iran regarding the integration of artificial intelligence (AI) applications into teaching and learning.
Methods: A quantitative research design was employed, collecting data via a structured survey from 50 EFL instructors across public and private universities in Iran.
Result: Findings indicate that most instructors viewed AI applications positively, particularly for their ability to deliver tailored feedback, foster engagement, and adapt learning to students’ individual needs. AI-assisted tools, such as Duolingo and ELSA Speak, were noted as effective for improving pronunciation, grammar, and vocabulary. However, challenges such as limited technological infrastructure, potential cultural mismatches in content, and high implementation costs were also highlighted. Statistical analysis revealed significant differences in perceptions of AI benefits based on teaching experience, but no significant differences in perceived challenges.
Conclusion: The study concludes by recommending increased teacher training, investment in localized AI resources, and strategies to ensure cultural relevance in AI-driven language learning.

Sustainable Development in Teaching: Examining Puzzle-Based Instruction, Professional Success, and the Role of AI-Driven Education

Mehdi Manoochehrzadeh — Sat, 28 Feb 2026 00:00:00 +0700

Background: Recent scholarship emphasizes critical pedagogy and learner-centered approaches as cornerstones of sustainable ELT, aligning with UNESCO’s Education for Sustainable Development (ESD) goals. One promising pedagogical framework is Puzzle-Based Instruction (PBI), which engages learners through problem-solving, discovery, and critical thinking
Aims: This study investigates the interplay between puzzle-based instruction (PBI), teachers’ professional success, and the integration of AI-driven educational tools as a catalyst for sustainable development in English language teaching (ELT). Specifically, it examines the prevalence and application of PBI principles among Iranian English language teachers (IELTs) in high schools and universities, alongside the extent to which learner-centered approaches are adopted in classroom practice.
Methods: A stratified random sampling method was used to select 35 IELTs from diverse EFL contexts in Iran. Data were collected using two validated instruments: The inquiry-led puzzle teaching review (comprising five subsections and nine scenarios) and the Successful Iranian EFL Teacher Questionnaire (SIETQ), which encompasses 12 professional competence factors.
Result: The study found a significant link between knowledge of PBI and professional success, with BA- and MA-level teachers showing lower awareness and engagement with socio-political content. It highlights the need for targeted professional development to improve PBI competence and critical thinking. Additionally, the research underscores the transformative potential of AI-driven education, like adaptive learning systems and automated feedback tools, to enhance reflective practice and support personalized instruction.
Conclusion: Implications are offered for policymakers, curriculum designers, and teacher educators seeking to integrate both critical pedagogical and technological dimensions into ELT.

Rethinking Assessment Practices Through AI And Alternative Approaches

Angélica María Rojas-Isaza — Sat, 28 Feb 2026 00:00:00 +0700

Background: Teacher education programs in the Global South face persistent challenges in implementing fair and context-responsive assessment practices. In southern Colombia, public universities operate within structural constraints including limited funding, heavy workloads, and sociocultural diversity that complicate the adoption of innovative and equity-oriented evaluation models. At the same time, Artificial Intelligence (AI) is reshaping pedagogical decision-making, raising both possibilities and ethical concerns regarding teacher agency, cultural relevance, and data governance.
Aims: This study examines how AI-supported alternative assessment practices can enhance reflective learning, autonomy, and critical awareness among pre-service English teachers in southern Colombia while remaining aligned with decolonial and context-sensitive educational principles.
Methods: Drawing on a qualitative descriptive design, the research analyzes classroom observations, student-produced assessment artifacts, and reflective journals collected during a 16-week university course on formative and process-based evaluation.
Result: Findings indicate that AI-supported assessment expanded students’ multimodal design repertoires, strengthened epistemic agency in their interaction with technological tools, and fostered deeper reflection on validity, coherence, and contextual relevance. However, these developments were mediated by structural constraints, including digital access gaps and practicum realities in under-resourced schools.
Conclusion: The study concludes that AI-assisted alternative assessment can foster more equitable and context-responsive learning experiences when integrated dialogically and critically, grounded in decolonial commitments, and attuned to institutional and material realities in the Global South.

Cybernetic Communication: Exploring the Role of AI as an Empathic Human Listener

Mohd Hanafi Jumrah, Aisah Hossin — Wed, 01 Apr 2026 00:00:00 +0700

Background: The emergence of artificial intelligence (AI) has transformed the landscape of human life, particularly in the way emotional expression is mediated. Humans have discovered new channels for emotional engagement through interactions with AI.

Aims: This study explores the phenomenon of emotional cybernetic communication, focusing on how individuals interact with AI systems such as ChatGPT as empathic conversational agents.

Methods: A qualitative research design was adopted, using in-depth semi-structured interviews with 28 active AI Chabot users in Malaysia, selected through purposive sampling. At the data analysis stage, the qualitative software ATLAS.ti was used to conduct thematic analysis following the procedures outlined by Braun & Clarke.

Result: The study employs Parasocial Interaction Theory, focusing on three dimensions: (i) One-way relationships, (ii) Simulative intimacy, and (iii) Emotional involvement and trust. The findings reveal that participants perceive AI as a non-judgmental, emotionally responsive, psychologically safe space to express their thoughts and feelings. Three main themes emerged: (i) Emotional intimacy with AI, (ii) Empathic trust in AI, and (iii) The therapeutic value of interaction without human social pressure.

Conclusion: This study shows that parasocial interactions between humans and AI Chabot’s constitute a unique and complex emotional experience. This study contributes to the growing discourse on human-AI communication by highlighting the evolving role of AI not merely as an informational tool but as an emotionally engaged talker. The implications extend to digital mental health support, the ethics of machine empathy, and design of future human-AI interactions.