AI applications

Artificial Intelligence (AI) is increasingly transforming the landscape of education, particularly within the domain of language learning, as evidenced by a growing body of research published in computer-assisted language learning (CALL) journals. These studies have examined the application of various AI technologies, including natural language processing (NLP), AI-driven educational platforms, automatic speech recognition, and chatbots, in facilitating language acquisition. The present study investigated the perceptions of 386 Iranian high school EFL students, utilizing the Students’ Perceived EFL Teacher Support Scale to evaluate the impact of AI-powered speaking assistance technologies, educational level, and learning setting on perceived teacher support. The findings revealed a tri-factorial structure underlying EFL teacher support, highlighting the compatibility of AI technologies with traditional pedagogical methods. This suggests that the integration of AI-powered tools into classroom instruction can enhance the overall effectiveness of language teaching and learning. To ensure optimal outcomes, educators are encouraged to strategically incorporate AI within pedagogically sound frameworks that maintain human-centered support. The study offers important implications for sustaining and enhancing teacher support in technology-enriched learning environments and underscores the need for further empirical research in this evolving area of applied linguistics and educational technology.

Background: Recent advancements in Natural Language Processing (NLP) have been significantly influenced by transformer models. However, challenges related to scalability, discrepancies between pretraining and finetuning, and suboptimal performance on tasks with diverse and limited data remain. The integration of Reinforcement Learning (RL) with transformers has emerged as a promising approach to address these limitations. Objective: This article aims to evaluate the performance of a transformer-based NLP model integrated with RL across multiple tasks, including translation, sentiment analysis, and text summarization. Additionally, the study seeks to assess the model's efficiency in real-time operations and its fairness. Methods: The hybrid model's effectiveness was evaluated using task-oriented metrics such as BLEU, F1, and ROUGE scores across various task difficulties, dataset sizes, and demographic samples. Fairness was measured based on demographic parity and equalized odds. Scalability and real-time performance were assessed using accuracy and latency metrics. Results: The hybrid model consistently outperformed the baseline transformer across all evaluated tasks, demonstrating higher accuracy, lower error rates, and improved fairness. It also exhibited robust scalability and significant reductions in latency, enhancing its suitability for real-time applications. Conclusion: This article illustrates that the proposed hybrid model effectively addresses issues related to scale, diversity, and fairness in NLP. Its flexibility and efficacy make it a valuable tool for a wide range of linguistic and practical applications. Future research should focus on improving time complexity and exploring the use of deep unsupervised learning for low-resource languages.