AI-powered Natural Language Processing powered by AI in Language Education: A Systematic Review

Objective: The development of foreign language teaching methodologies in the context of an evolving educational landscape is directly linked to the continuous integration of Artificial Intelligence (AI) in various fields. The integration of AI-driven tools and technologies has reshaped communication and educational experiences by creating opportunities for innovative pedagogical approaches in higher education in Kazakhstan. This paper explores the use of AI-powered smart technologies in English language learning and teaching, emphasizing their role in interactive educational activities, personalized learning environments, and critical thinking development among students at A.K. Kussayinov Eurasian Humanities Institute in Kazakhstan. Method used: Literature analysis, pedagogical experiment and students’ feedback to assess the impact of AI-driven applications on English learning in a controlled setting and understand students’ perceptions and motivation along with exploring existing research on AI-based approaches in education. Results showed that (1) the use of AI-driven smart technologies in English teaching significantly enhances the learning process. Students in experimental group, who used personalized AI-supported mobile applications, demonstrated improved engagement, motivation, and autonomy compared to the control group; (2) the increase of students’ interest in using smartphones for educational purposes and a positive attitude towards AI-assisted learning; (3) the effectiveness of AI-based personalization confirmation in enhancing better language acquisition outcomes. Novelty lies in (1) the integrated application of AI-driven smart technologies to create personalized learning environments in English learning and teaching at the institute level; (2) offering a practical model for implementing AI innovations in language education, particularly within the context of Kazakhstan’s higher education system.

This comprehensive review explores the utilization of Artificial Intelligence (AI) in language assessment and its transformative potential. Language assessment plays a vital role in education, employment, and societal integration. Language assessment has traditionally relied on human evaluators, who assess and score language performance based on standardized criteria. However, this manual assessment approach has limitations, including subjectivity, inter-rater variability, and scalability issues. With the rapid advancements in AI technologies, language assessment has experienced significant changes, offering more accurate, efficient, and innovative evaluation methods. The method used in this study is systematic review which critically examines the application of AI in language assessment, highlighting its benefits, challenges, and future prospects. The review covers various aspects, including automated scoring and evaluation, intelligent tutoring systems, natural language processing, advantages and benefits, challenges and considerations, and future directions. By harnessing the use of AI, language assessment can achieve unprecedented levels of objectivity, scalability, and personalization, while addressing ethical considerations. This review contributes to the understanding of AI's impact on language

The article examines the transformative impact of Artificial Intelligence (AI) on language learning, focusing on Arabic and English. It explores how AI technologies, including language learning platforms, translation and interpretation tools, and natural language processing (NLP), reshape traditional language education methods. AI-driven solutions offer personalized, adaptive, and dynamic learning experiences, moving beyond conventional approaches. By integrating AlAfnan’s Taxonomy of Educational Objectives, which emphasizes the cognitive, affective, and psychomotor domains, AI provides a more comprehensive framework for language learning and assessment. The article discusses how AI enhances language assessments by offering personalized feedback and adaptive testing that caters to individual learner progress in real-time. In the classroom, AI facilitates not only knowledge acquisition (cognitive domain) but also emotional engagement and cultural sensitivity (affective domain), as well as practical communication skills such as speaking and writing (psychomotor domain). Integrating AlAfnan’s Taxonomy ensures that AI-driven education addresses all facets of language mastery, from technical proficiency to emotional intelligence and cultural awareness. The ethical and cultural considerations of using AI in language learning are also analyzed, emphasizing the importance of inclusivity, respect, and responsible AI development. As AI continues to advance, it holds the potential to bridge linguistic and cultural gaps, making language learning more accessible and practical. AI, when aligned with AlAfnan’s Taxonomy, not only enhances the language learning process for Arabic and English learners but also promotes cross-cultural communication and global understanding, fostering more profound and meaningful language acquisition.

Technology is advancing rapidly in this globalization era. Modern technology such as Artificial Technology (AI) emerges as the result of digitalization and it has a favourable impact on many facets of life including in education sector. Generation Z or Gen Z, as digital natives, utilize digital technology including AI as an essential part of their daily routines. The purpose of this study is to investigate Gen Z students’ perspectives towards the use of technology particularly AI (artificial intelligence) technology in English language learning. The participants of this study were 30 students from English Education Department, class of TBI-3, fourth semester of State Islamic University of North Sumatera (UIN SU). Qualitative method was implemented in this study. To collect the data, two instruments were employed, through an interview technique and observation. The research findings revealed that 18 (60%) students agree with AI technology, 10 (33.3%) students were neutral and 2 (6.7%) students disagree with the utilization of AI technology in English language learning. AI technology seems to be more advanced in the future to assist human. Moreover, if it is integrated officially in education sector. Thus, Gen Z students should be wiser in accessing it. This study may broaden teachers’ and lecturers’ insight or horizon about current technology in English language learning.

The rapid development of Natural Language Processing (NLP) technology has provided new perspectives and tools for the acquisition of second languages. As our world becomes increasingly interconnected, the role of NLP in facilitating language learning has become more prominent. This paper reviews the multifaceted applications of NLP technology in language learning, including auxiliary teaching functions such as reading assistance, writing feedback, oral interaction, and personalized learning. These applications have significantly enhanced language learners' abilities in vocabulary acquisition, grammatical application, pronunciation accuracy, and reading comprehension through real-time feedback, enhanced interactivity, and promotion of cultural understanding. Despite the immense potential of NLP technology in second language learning, challenges such as technical accuracy, cultural adaptability, and data privacy exist. This paper proposes strategies, such as ensuring technical accuracy, curating diverse datasets and safeguarding data privacy to address these challenges. Looking forward, the future development of NLP technology in second language education holds great promise. As NLP continues to evolve, it is expected to contribute to more personalized, effective, and accessible language learning solutions. This paper aims to provide valuable references and insights for educators, technology developers, and policymakers, enabling them to harness the transformative potential of NLP in language education and to navigate the future of educational technology with confidence and foresight.

Language learning is under the influence of many factors, including social and emotional aspects. In this regard, teachers play a pivotal role in language education; however, developments in technology, particularly in artificial intelligence (AI) technology, have started to change the roles of teachers and the atmosphere of the language learning environment. AI technology offers lots of opportunities to enhance language education, whereas it brings along ethical considerations. To date, some previous studies have focused on ethical considerations for AI use, mostly in higher education; nevertheless, there has been observed a lack in the studies establishing ethical guidelines for AI use in language education with a special and direct focus on social and emotional aspects of learning with the help of expert language educators' ideas. Therefore, the present study follows the procedure of the Delphi Method with the experts to create guidelines that may be helpful for language educators and researchers. At the end of the three‐round Delphi study, a guideline including 43 items was created, and the themes of the guideline were discussed with previous studies, their relationship with the social and emotional aspects of learning and their importance. The established guideline holds a significant feature for language educators, language learners, developers and researchers in the field of language education.

The integration of artificial intelligence (AI) in higher education is transforming traditional learning frameworks, presenting unprecedented opportunities for personalized, efficient, and inclusive education. This paper explores the ways AI technologies, including machine learning algorithms, natural language processing, and intelligent tutoring systems, are reshaping educational methodologies and environments in higher education. By examining AI-driven applications such as adaptive learning platforms, automated assessment tools, and virtual teaching assistants, this study highlights how AI enhances student engagement, facilitates tailored learning experiences, and streamlines administrative tasks. Furthermore, this paper addresses the ethical considerations, challenges, and potential biases associated with AI implementation, emphasizing the need for transparent, equitable practices to optimize AI's positive impact on learning. Ultimately, this research underscores AI's transformative potential in making higher education more accessible and adaptive to diverse learner needs, setting the stage for a future of AI-empowered, data-driven education. This study utilizes a comprehensive literature review and case studies to demonstrate the potential and challenges of AI implementation, highlighting ethical considerations, data privacy, and the need for policy frameworks to support responsible AI usage. By addressing these multifaceted aspects, this research emphasizes the strategic role of AI in shaping the future of higher education.

This chapter explores the integration of Artificial Intelligence (AI) in higher education with an emphasis on pedagogy, instruction, and administration for student learning and achievement. The paradigm shift moved the focus from teacher-centered to learner-centered systems involving AI-powered tools for personalized learning systems such as intelligent tutoring, adaptive learning systems, natural language processing (NLP), machine learning (ML), robotics, automated grading, and feedback to offer enhanced learning opportunities for teaching and learning. However, the AI technological transformation presented challenges in the form of ethical considerations, algorithm biases, curriculum development, lack of infrastructure, and accessibility issues. The chapter discusses the history and evolution of AI, AI technologies in higher education, enhancing pedagogy with AI in higher education, faculty support, and administrative efficiency. Additionally, it addresses the ethical considerations and future trends for AI adoption in higher education.

Technology based on artificial intelligence (AI) is a revolutionary force that is changing economies, civilizations, and industries all over the world. AI, which has its roots in computer science and cognitive psychology, is a wide range of tools and methods designed to make robots capable of doing activities that have historically required human intellect. This abstract examines the many facets of artificial intelligence (AI) technology, including its fundamentals, uses, difficulties, and ramifications. Artificial Intelligence (AI) technology comprises several subfields such as robotics, computer vision, natural language processing, machine learning, and expert systems. Particularly, machine learning techniques have propelled incredible progress by allowing computers to learn from data and make judgments or predictions without the need for explicit programming. Natural language processing allows machines to comprehend, interpret, and produce human language, hence facilitating human-computer interaction. Machines can now see, analyze, and interpret visual data from the real world thanks to computer vision technology. Applications of AI technology may be found in a wide range of industries, including manufacturing, healthcare, finance, transportation, agriculture, education, and entertainment. AI-powered solutions help in drug discovery, medical imaging analysis, diagnosis, and customized therapy in the healthcare industry. AI algorithms are used in finance to power automated trading, fraud detection, risk assessment, and customer support. AI makes it possible for transportation to include predictive maintenance, traffic management, and driverless cars. Artificial Intelligence enhances supply chain management, quality assurance, and production processes in manufacturing. AI technology has the potential to revolutionize many industries, but it also comes with dangers and problems. These include privacy concerns, security hazards, ethical dilemmas, issues with prejudice and fairness, and effects on society and employment. Responsible AI methods, legal frameworks, multidisciplinary cooperation, and ethical standards are all necessary to meet these issues. Future prospects for AI technology development include the ability to solve challenging issues, spur creativity, increase productivity, and improve quality of life. But to fully utilize AI, one must take a comprehensive strategy that strikes a balance between the advancement of technology and ethical issues, human values, and social well-being. In summary, artificial intelligence (AI) technology is at the vanguard of innovation, presenting never-before-seen possibilities to transform whole sectors, spur economic expansion, and tackle global issues. AI has the ability to usher in a future of greater human-machine collaboration, innovation, and wealth through the promotion of collaboration, transparency, and ethical stewardship. the Ranking of the Artificial Intelligence using the TOPSIS Method . Interpretable Models is got the first rank whereas is the Ethical AI is having the Lowest rank. Keywords: Explainable AI (XAI), Interpretable Models, Ethical AI ,Responsible AI, Robustness and Adversarial Defense, Continual Learning, Federated Learning, Human-Centric AI, AI Governance and Policy

This study presents a bibliometric mapping of research on artificial intelligence (AI) in language learning and teaching, offering a systematic overview of trends, influential contributors, and emerging research themes. Despite rapid growth in AI-enhanced education, comprehensive analyses of global patterns and thematic focus remain scarce. Using Scopus advanced search with the keywords “AI,” “teaching,” “learning,” and “language,” 653 publications were retrieved. Data were cleaned and harmonized with OpenRefine to ensure consistency, followed by statistical and graphical analyses using Scopus Analyzer. Network visualizations were generated in VOSviewer, including keyword co-occurrence, country co-authorship, and citation linkages, to uncover research clusters and collaboration patterns. Results indicate that AI, language learning, and educational technology dominate the literature, with China, the United States, and the United Kingdom leading in productivity and international collaboration. Keyword analysis revealed six thematic clusters, highlighting personalized learning, natural language processing, machine learning applications, and AI-driven pedagogical strategies. Country co-authorship generated eight clusters, reflecting strong collaborative networks across East Asia, North America, and Europe. These findings provide critical insights into the structure and evolution of AI in language education, guiding future research priorities, fostering global collaboration, and informing educational policy. The study underscores AI’s transformative potential in language teaching and learning while charting directions for interdisciplinary and cross-national research advancement.

<p>The integration of Artificial Intelligence (AI) in higher education has been accelerated by the challenges imposed by the COVID - 19 pandemic, which have significantly altered the educational landscape. This paper explores the transformative potential of AI technologies in enhancing learning outcomes and pedagogical approaches in higher education. It examines AIs role in personalizing learning experiences, augmenting faculty capabilities, and improving administrative efficiency. Through a detailed analysis of recent literature, the study underscores the opportunities AI presents for adaptive learning systems, automated assessment tools, and data - driven decision - making processes, while addressing the challenges such as ethical considerations, data privacy, and the need for infrastructure adjustments. The findings highlight AIs critical role in addressing immediate educational disruptions and shaping a more efficient and responsive educational system.</p>

The integration of Artificial Intelligence (AI) in higher education has been accelerated by the challenges imposed by the COVID - 19 pandemic, which have significantly altered the educational landscape. This paper explores the transformative potential of AI technologies in enhancing learning outcomes and pedagogical approaches in higher education. It examines AIs role in personalizing learning experiences, augmenting faculty capabilities, and improving administrative efficiency. Through a detailed analysis of recent literature, the study underscores the opportunities AI presents for adaptive learning systems, automated assessment tools, and data - driven decision - making processes, while addressing the challenges such as ethical considerations, data privacy, and the need for infrastructure adjustments. The findings highlight AIs critical role in addressing immediate educational disruptions and shaping a more efficient and responsive educational system.

Differentiation strategies face higher uncertainty and dynamism because of design and functionality of their service in higher education. This is closely related with contextual knowledge and neoliberal approach. Researchers provide the contextual knowledge of neoliberal approach in Pakistan and state of social justice regarding higher education in the country. Neoliberal approach in education has been identified as a new trend in developing countries. Business approach in education has started treating education as a commodity and students as a costumer. Knowledge-based economy is one of the strongest factors influencing the neoliberal approach in higher education. There are certain circumstances for adaptation of this approach in higher education; however, the intellectual community needs to be aware of the pros of this approach. Humanitarian approach need to be taken care of by the government bodies in higher education. Ethical and leadership programs based on agile management may be helpful for faculty members to reduce social injustice through their teaching approach.

To evaluate the impact of artificial intelligence (AI) technologies on clinical trial processes, identify quantitative benefits, and determine areas requiring further research. A comprehensive literature review was conducted examining AI applications across clinical trial phases. The study analysed machine learning (ML), natural language processing (NLP), computer vision, reinforcement learning (RL), and other AI technologies as applied to clinical research processes. AI implementations have delivered substantial quantitative benefits across various aspects of clinical trials (CT). International Business Machine (IBM) Watson enabled an 80% increase in patient accrual to breast cancer trials within just 11 mo. In silico medicine’s generative tensorial reinforcement learning (GENTRL) platform accelerated the drug discovery timeline by a factor of 15, reducing it to just 46 days. Saama Technologies' deep learning (DL) approach detected 30% more anomalous data cases compared to traditional methods. Pfizer’s use of AI-driven quantitative systems pharmacology (QSP) models led to a 60% reduction in Phase 2 dose cohorts. AiCure’s AI-powered monitoring system achieved 25% higher medication adherence and completed trials 30% faster. Meanwhile, Unlearn. AI’s digital twin technology enabled a 30% reduction in control group size without compromising statistical power. These outcomes highlight AI’s powerful role in improving the efficiency, speed, and quality of CT. AI is trans formatively enhancing CT through improved recruitment efficiency, protocol optimization, data quality management, and patient monitoring. However, challenges remain in data quality, algorithm interpretability, regulatory compliance, workflow integration, and bias mitigation. Future research should focus on advanced predictive modelling, explainable AI development, federated learning for privacy preservation, AI-human collaboration models, real-world data integration, and standardized validation procedures. Ethical considerations and regulatory frameworks specifically addressing AI in CT require further development to realize the full potential of these technologies.

The integration of Artificial Intelligence (AI) in English Language Teaching (ELT) represents a transformative shift in language education, offering innovative tools and approaches to enhance learning outcomes and experiences. This study explores the various AI technologies and applications employed in ELT, including personalized learning platforms, intelligent tutoring systems, automated writing evaluation tools, and language learning apps. Through a comprehensive review of existing literature, theoretical frameworks, and empirical studies, this paper examines the effectiveness, pedagogical implications, ethical considerations, and future directions of AI integration in ELT. Key findings reveal that AI-driven tools provide personalized, adaptive, and interactive learning experiences tailored to individual learners' needs, promoting student engagement, autonomy, and proficiency development. While AI technologies offer numerous benefits for language instruction, including improved learning outcomes, teacher efficiency, and accessibility, they also raise ethical and social considerations, such as data privacy, algorithmic bias, and equity issues. Addressing these challenges requires collaborative efforts among educators, policymakers, researchers, and technology developers to ensure responsible and equitable AI use in language education. Looking ahead, future directions include advancing AI technology, integrating AI and pedagogy, promoting ethical and responsible AI use, providing teacher training and professional development, fostering collaborative research and evaluation, innovating in assessment and evaluation, and promoting global collaboration and knowledge sharing in ELT. By embracing innovation, collaboration, and ethical practice, educators can harness the transformative power of AI technologies to create dynamic, inclusive, and effective language learning environments that empower learners to succeed in today's interconnected world.