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Enhancing ESL Vocabulary Acquisition through AI-Based Learning Systems Tanvir Mostafa
Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i1.38

Abstract

Purpose – Vocabulary is one of the key aspects of second-language development, as it facilitates reading, listening, speaking, and writing. Nevertheless, because lexical development needs to be practiced repeatedly, with long-term motivation and long-term memory encouragement, many ESL learners have problems with remembering and applying new vocabulary. Recent advancements in artificial intelligence (AI) have provided new opportunities for vocabulary teaching in the form of adaptive practice, automated feedback, multimodal support, simulated dialogue, and long-term progress monitoring.Methods – This narrative review explores how AI-based learning systems can be used to support vocabulary acquisition in ESL learners based on existing research on vocabulary learning, technology-assisted language learning, and recent research on AI and generative tools.Findings – According to the literature reviewed, AI-assisted learning may be helpful if systems deliver tasks of suitable difficulty, the ability to repeat and retrieve information, contextualized input and feedback. Nonetheless, the success of AI relies on good pedagogy, prudent design of instructions, validation of results and teacher mediation.Research implications – Critical issues include misinformation, privacy issues, and learners’ overreliance on technology.Originality – AI should be seen as an auxiliary resource and not a substitute for principled vocabulary instruction.
Teachers' Attitudes Toward the Use of Artificial Intelligence in English Language Teaching: A Scoping Review With Implications for Bangladesh Tanvir Mostafa
Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 2 (2026): Artificial Intelligence in Lifelong and Life-Course Education
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aillce.v1i2.35

Abstract

Purpose – This scoping review maps recent literature on English teachers’ attitudes toward artificial intelligence (AI) in English language teaching (ELT) and discusses its implications for Bangladesh. It addresses the limited synthesis of evidence across empirical studies, systematic reviews, and policy reports on teachers’ perceptions of generative AI, automated writing support, speech recognition technologies, and adaptive learning systems.Design – Guided by established scoping review frameworks and PRISMA-ScR reporting guidance, searches were reconstructed and updated in May 2026 through Google Scholar, ERIC, SpringerLink, ScienceDirect, Frontiers, MDPI, journal websites, and relevant institutional sources. Search terms covered artificial intelligence, generative AI, ChatGPT, ELT, EFL/ESL, teacher attitudes, teacher perceptions, AI literacy, and Bangladesh. After duplicate removal and screening, 18 sources were included. Data were manually coded thematically by context, design, evidence base, AI applications, attitudinal direction, benefits, concerns, and implications.Findings – Five themes emerged: perceived usefulness, perceived ease of use and AI literacy, ethical and pedagogical issues, contextual and institutional challenges, and teacher identity and agency. Teachers generally showed conditional positivity toward AI, valuing its support for material preparation, feedback, assessment, differentiated instruction, and time efficiency, while remaining concerned about cheating, overreliance, hallucinations, bias, privacy, unequal access, and reduced pedagogical control.Research implications – Responsible AI use in Bangladeshi ELT requires teacher-centered professional development, redesigned assessment, clear institutional policy, and locally relevant classroom models. This review does not infer causality.Originality – The study offers a focused thematic map for future research, professional development, policy design, and responsible AI integration in ELT