Toward Equitable Access to Technical Training via Multilingual Conversational Agents
DOI:
https://doi.org/10.54097/mvsf7017Keywords:
Multilingual conversational agents, technical training, equitable access, natural language processing, dialogue systems, vocational education, large language modelsAbstract
The global technical skills gap disproportionately affects non-English-speaking populations who encounter substantial language barriers when attempting to access digital training resources. Multilingual conversational agents (MCAs), powered by advanced natural language processing (NLP) technologies, offer a promising pathway toward democratizing technical education across linguistic communities. This paper presents a mixed-methods investigation into the design, deployment, and evaluation of an MCA system tailored for vocational and technical skill acquisition in multilingual environments. Drawing on transformer-based multilingual language models, recurrent encoder-decoder architectures, and adaptive task-oriented dialogue management frameworks, the proposed system was assessed across four language groups — Spanish, Arabic, Mandarin Chinese, and Hindi — in simulated technical training scenarios covering information technology fundamentals and industrial safety protocols. Quantitative results indicate statistically significant improvements in learning outcomes, task completion rates, and user engagement compared to English-only baseline systems. Qualitative findings further reveal that language alignment between learner and agent substantially reduces cognitive load and increases perceived system credibility. This research contributes a validated architecture and evaluation framework for practitioners and policymakers committed to equitable access to technical education through intelligent conversational systems.
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