The Interaction between Chatbot Pattern and learning style to Develop English Vocabulary Skills for intermediate School Students in the UAE
This research investigates the interaction between chatbot patterns (linguistic and voice) and learning styles (visual and auditory) in developing English vocabulary skills among intermediate school students in the UAE. A quasi-experimental design was employed with four groups of male and female Grade 8 students (N = 72), categorized based on their learning styles (visual vs. auditory) and chatbot interaction patterns (linguistic-based vs. voice-based). The participants were randomly assigned to the following groups: Visual-Linguistic, Visual-Voice, Auditory-Linguistic, and Auditory-Voice. To achieve the research objectives, several instruments were utilized, including a Learning Style Questionnaire (based on the VARK model), an English Vocabulary Skills Checklist, and a Vocabulary Knowledge Skills Test (pre- and post-tests). The chatbot was implemented through a web-based platform and integrated within Teams, the school's official Learning Management System (LMS). Quantitative results revealed significant differences in vocabulary acquisition between pre-test and post-test scores across all groups, with the greatest improvements observed in the Visual-Linguistic group, followed by the Auditory-Voice, Auditory-Linguistic, and Visual-Voice groups. The results also showed significant differences between the linguistic and voice groups in favor of the linguistic chatbot, with a high impact, while no significant differences were found between visual and auditory groups, with only a low impact. These findings underscore the effectiveness of chatbot-based interventions in enhancing English vocabulary skills, particularly when chatbot interaction types are aligned with students' learning styles. The research recommends integrating chatbot technologies tailored to students' learning styles into English language curricula to improve vocabulary acquisition and overall language proficiency.
Authors
- Mohamed Elsayed Elnaggar (ORCID: https://orcid.org/0000-0002-8326-4495)
- Ahmed Adel Nouri Jumaa
- Haggag Mohamed Haggag (ORCID: https://orcid.org/0000-0001-5278-5127)
Publication Details
- Journal
- Technium Romanian Journal of Applied Sciences and Technology
- Published
- 2025-04-23
- DOI
- https://doi.org/10.47577/technium.v29i.12741
- Primary Topic
- AI in Service Interactions
- Type
- article
- Field-Weighted Citation Impact
- 0.00