Expert Institutions and Policy Engineering in Middle Eastern Affairs: A Collective Review of Scattered Indexes
Amir Parvaresh, M.A regional Studies, Middle Eastern Studies, Allameh Tabataba’i University.
Shakiba Alihemat, B.S. Candidate, Department of Electrical Engineering, University of Isfahan, Iran.
MSI Journal of Education and Social Science | https://zenodo.org/records/18229173 | Page 01 to 07
Abstract
Vocabulary acquisition plays a pivotal role in foreign language proficiency. This study investigates the effectiveness of Artificial Intelligence (AI)-powered mind mapping on English vocabulary acquisition among non-English-major undergraduates at Nguyen Tat Thanh University (NTTU). By employing a quasi-experimental mixed-methods design, 20 third-year students were assigned to an Experimental Group (EG), which utilized AI-powered mind-mapping tool (GitMind), and a Control Group (CG), which followed traditional vocabulary instruction methods. Quantitative data from pre-tests and post-tests indicated that the EG achieved significantly higher vocabulary gains than the CG (p = .044), with a medium-to-large effect size (Cohen’s d = 0.66). Qualitative findings derived from Technology Acceptance Model-based questionnaires and focus group interviews revealed high perceived usefulness, enhanced learner confidence, and increased engagement. The findings suggest that AI-powered visual mapping can reduce extraneous cognitive load and facilitate deeper semantic processing, thereby supporting vocabulary acquisition in EFL higher education contexts.
Keywords: AI in education; mind mapping; vocabulary acquisition; EFL; Cognitive Load Theory; Technology Acceptance Model.
All articles published by MSIP are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of any MSIP article, including figures and tables.
For articles published under a Creative Commons CC BY 4.0 license, any part of the article may be reused for any purpose, including commercial use, provided that the original MSIP article is clearly cited.
THE IMPACT OF AI-POWERED MIND MAPPING ON ENGLISH VOCABULARY ACQUISITION AMONG NON-ENGLISH MAJORS: A MIXED-METHODS STUDY
Nguyen Huu Thoai, Center of Foreign languages, Nguyen Tat Thanh University, Ho Chi Minh city, Vietnam.
MSI Journal of Education and Social Science | https://zenodo.org/records/18183072 | Page 01 to 16
Abstract
Vocabulary acquisition plays a pivotal role in foreign language proficiency. This study investigates the effectiveness of Artificial Intelligence (AI)-powered mind mapping on English vocabulary acquisition among non-English-major undergraduates at Nguyen Tat Thanh University (NTTU). By employing a quasi-experimental mixed-methods design, 20 third-year students were assigned to an Experimental Group (EG), which utilized AI-powered mind-mapping tool (GitMind), and a Control Group (CG), which followed traditional vocabulary instruction methods. Quantitative data from pre-tests and post-tests indicated that the EG achieved significantly higher vocabulary gains than the CG (p = .044), with a medium-to-large effect size (Cohen’s d = 0.66). Qualitative findings derived from Technology Acceptance Model-based questionnaires and focus group interviews revealed high perceived usefulness, enhanced learner confidence, and increased engagement. The findings suggest that AI-powered visual mapping can reduce extraneous cognitive load and facilitate deeper semantic processing, thereby supporting vocabulary acquisition in EFL higher education contexts.
Keywords: AI in education; mind mapping; vocabulary acquisition; EFL; Cognitive Load Theory; Technology Acceptance Model.
All articles published by MSIP are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of any MSIP article, including figures and tables.
For articles published under a Creative Commons CC BY 4.0 license, any part of the article may be reused for any purpose, including commercial use, provided that the original MSIP article is clearly cited.
Boosting Fluency, Reducing Fear: Utilizing Generative AI as a Scaffolding Tool in EFL Speaking Classes
Nguyen Duy Tuan, Faculty of foreign languages, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam.
MSI Journal of Education and Social Science | https://zenodo.org/records/18181884 | Page 01 to 13
Abstract
Generative AI tools like Google Gemini have created new opportunities for language learning. However, empirical evidence regarding their impact on oral proficiency remains limited. This quasi-experimental study investigates the effectiveness of Google Gemini as a scaffolding tool for Vietnamese university freshmen (N=78). Over a seven-week intervention, the experimental group utilized Gemini for ideation, real-time feedback, and conversation simulation, while the control group followed traditional instruction. Data was collected via pre- and post-tests based on the CEFR B1 rubric, alongside anxiety surveys and semi-structured interviews. Paired sample t-tests revealed that the AI-assisted group demonstrated statistically significant improvements in Fluency and Discourse Management (p < .05) and a substantial reduction in speaking anxiety compared to the control group. However, no significant differences were observed in Grammatical Accuracy or Lexical Resource, suggesting that while AI effectively lowers the affective filter and promotes communicative flow, it may require longer-term integration to enhance linguistic precision. These findings challenge the view of AI as a mere correction tool, proposing instead its role as a psychological scaffold that empowers reticent learners to speak more confidently.
Keywords: Generative AI, Google Gemini, Speaking Anxiety, EFL Fluency, Scaffolding, Affective Filter
All articles published by MSIP are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of any MSIP article, including figures and tables.
For articles published under a Creative Commons CC BY 4.0 license, any part of the article may be reused for any purpose, including commercial use, provided that the original MSIP article is clearly cited.
