Artificial Intelligence in Mobile Computing for English Language Testing: Redefining Automated Language Assessment

Artificial intelligence's (AI) quick development has created new opportunities in a number of domains, including education. Language evaluation is one such field that has experienced a great deal of innovation. The utilization of auto encoders to redefine automated language assessment is the focus of this work, which investigates the integration of artificial intelligence (AI) in mobile computing for English language evaluation. Traditional language assessment approaches are being challenged by AI's capability to offer real-time, scalable, and personalized evaluations. By leveraging autoencoders deep learning models optimized for unsupervised learning and feature extraction—the proposed system evaluates diverse linguistic attributes such as grammar, spelling, pronunciation, fluency, and comprehension. Autoencoders transform high-dimensional input data into low-dimensional latent spaces, enabling efficient assessment of nuanced language patterns. The methodology incorporates the LibriSpeech ASR Corpus for data preprocessing, feature extraction, and training. Key innovations include robust handling of diverse input types (text and audio), accurate reconstruction of linguistic features, and scalable evaluation of proficiency. Performance metrics such as accuracy, reconstruction error, and fluency scores demonstrate the model's superior learning and assessment capabilities. Compared to other deep learning methods like LSTMs, CNNs, and Transformers, the autoencoder-based approach excels in accuracy and adaptability, offering impartial and precise assessments. The study "concludes" with a discussion of the benefits and challenges of AI-driven automated testing, providing insights into its potential to revolutionize educational assessment and reduce the burden on instructors by automating routine evaluations