CHALLENGES OF AI IN LEARNING TECHNICAL VOCABULARY IN ENGINEERING EDUCATION: AN INTEGRATED EMPIRICAL AND SCHOLARLY REVIEW
CHALLENGES OF AI IN LEARNING TECHNICAL VOCABULARY IN ENGINEERING EDUCATION: AN INTEGRATED EMPIRICAL AND SCHOLARLY REVIEW

Summary/Abstract: AI technologies continue transforming engineering education, particularly in how engineering students acquire technical vocabulary. This article integrates a comprehensive scholarly literature review with empirical survey data from 126 engineering students at University Politehnica of Bucharest and Valahia University of Târgovişte to examine the challenges inherent in using artificial intelligence for technical vocabulary learning. Findings from real academic literature emphasize concerns related to cognitive load, AI literacy, algorithmic bias, domain‑specific accuracy, curriculum readiness, and structural inequities. Survey data further reveal that 55.6% of students report inaccurate explanations, 40.5% face cost and access barriers, and 27.8% encounter insufficient subject‑specific vocabulary in AI outputs. The discussion synthesizes contemporary research from engineering education scholarship—including Liu (2025), Hallmark et al. (2025), Mosly (2024), and Schleiss et al. (2024)—with the empirical findings to identify structural, pedagogical, and sociotechnical barriers that must be addressed to ensure responsible AI‑supported vocabulary learning in engineering contexts.

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