AI ‐Smart Classroom for English Translation: An Adaptive HMM ‐Based Framework

ABSTRACT This study develops an AI‐enhanced smart classroom framework to overcome the limitations of traditional English translation instruction, particularly in addressing the theory‐practice gap. We designed a three‐phase intelligent teaching system incorporating Hidden Markov Models (HMM) for: (1) adaptive pre‐class preparation, (2) immersive virtual translation scenarios, and (3) automated post‐class assessment. A 6‐week controlled experiment ( N = 100) compared this approach with traditional instruction using quantitative metrics including engagement levels, translation accuracy, proficiency rates, and satisfaction surveys. The experimental group showed statistically significant improvements ( p < 0.05): 12.7% higher engagement ( d = 1.21), 8.3% better culture‐specific translation accuracy, 6.9% faster proficiency attainment, and 89.2% satisfaction rate (vs. 82.1% control). HMM analysis effectively tracked learning progression and identified competency gaps. The study demonstrates HMM's effectiveness for modeling translation competence development and validates AI‐enhanced instruction as a viable solution for translation education. The implemented framework offers a replicable model for intelligent language learning systems.