Arabic Language Processing: Current Status And Future Prospects Of Artificial Intelligence

Although technology-assisted Arabic language learning has attracted increasing scholarly attention, research specifically examining the use of the Wayground application to support Arabic grammar learning in higher education remains limited. This study aimed to explore the implementation of the Wayground application in Arabic language learning and its contribution to improving students’ understanding of Arabic grammar at Nahdlatul Ulama University of Purwokerto. A qualitative approach was employed using a descriptive case study design. Data were collected through classroom observations, semi-structured interviews, and documentation, and were analyzed using thematic analysis. The findings indicate that the use of the Wayground application increased students’ engagement, supported interactive learning, and facilitated a clearer understanding of Arabic grammar concepts through technology-based activities. These findings contribute to the theoretical development of technology-enhanced language learning and broaden understanding of digital media integration in Arabic language education. The study concludes that the Wayground application plays an important role in fostering an engaging and effective Arabic language learning environment and recommends the integration of technology-based platforms to strengthen grammar instruction in higher education. The study further offers practical implications for optimizing digital learning tools and highlights the need for future research on the long-term effects of digital applications on Arabic language proficiency and learning outcomes.

Artificial intelligence (AI) is changing many aspects of education and is gradually being introduced to language education. This article reviews the literature to examine main trends and common findings in relation to AI technologies and applications for second and foreign language learning and teaching. With special reference to computer-assisted language learning (CALL), the article explores natural language processing (NLP), data-driven learning (DDL), automated writing evaluation (AWE), computerized dynamic assessment (CDA), intelligent tutoring systems (ITSs), automatic speech recognition (ASR), and chatbots. It contributes to discussions on understanding and using AI-supported language learning and teaching. It suggests that AI will be continuously integrated into language education, and AI technologies and applications will have a profound impact on language learning and teaching. Language educators need to ensure that AI is effectively used to support language learning and teaching in AI-powered contexts. More rigorous research on AI-supported language learning and teaching is recommended to maximise second and foreign language learning and teaching with AI.

The amalgamation of artificial intelligence (AI) has led to significant changes in the healthcare industry, revolutionizing patient care, diagnostics, treatment, and research. This chapter discusses the recent advancements in AI technology that are influencing healthcare research and application. With the combination of large-scale healthcare databases and machine learning innovations, predictive analytics platforms have been created, enabling early disease detection, risk assessment, and personalized treatment plans. AI-powered diagnostic tools, which use natural language processing and computer vision, have shown remarkable accuracy in analyzing medical images and notes, thereby speeding up diagnosis processes. AI-powered robotics in surgery, guided by algorithms, improve precision, reduce invasiveness, and enable remote patient monitoring through telemedicine. AI also accelerates drug discovery, predicting drug effectiveness, and reducing time and cost for targeted therapies. To address ethical and privacy concerns, transparency and regulatory adaptations are being implemented. AI’s convergence with genomics and wearables promotes personalized treatments based on genetic and real-time data, enhancing interventions. The continuous advancements in AI technology have the potential to improve patient outcomes and diagnosis. While challenges persist, AI’s potential to revolutionize healthcare remains evident through expert collaboration, technological innovation, and patient-centric care.

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.

This study investigates the role of artificial intelligence (AI) in Arabic language processing, focusing on both its developmental potential and the challenges that threaten the language’s integrity and identity. Despite progress in natural language processing (NLP), the Arabic language still faces substantial barriers due to its intricate grammatical rules and semantic depth, which make it more complex to integrate into AI systems compared to other languages. In addition to technical obstacles, the application of AI raises ethical concerns—particularly around preserving the authenticity of Arabic and protecting intellectual property. Issues such as uncredited AI-generated content and reduced human involvement in language formulation may compromise the credibility and originality of information. The study aims to offer future-oriented solutions that address these ethical challenges while promoting a balanced approach between leveraging AI technologies and safeguarding the linguistic heritage of Arabic. Adopting a qualitative, literature-based methodology with a focus on ethical, linguistic, and technological analysis, the research underscores the urgent need for ethical and regulatory frameworks to guide AI implementation in this domain. The findings highlight the importance of continuous investment in research and development to support Arabic’s sustainable integration into AI-driven platforms. As a contribution, the study provides a conceptual foundation for responsible innovation, advocating for policies that protect the identity of the Arabic language while enabling its evolution within the digital age

This landmark volume offers an introduction to the field of teaching Arabic as a foreign or second language. Recent growth in student numbers and the demand for new and more diverse Arabic language programs of instruction have created a need that has outpaced the ability of teacher preparation programs to provide sufficient numbers of well-qualified professional teachers at the level of skill required. Arabic language program administrators anticipate that the increases in enrollment will continue into the next decades. More resources and more varied materials are seriously needed in Arabic teacher education and training. The goal of this Handbook is to address that need.The most significant feature of this volume is its pioneer role in approaching the field of Arabic language teaching from many different perspectives. It offers readers the opportunity to consider the role, status, and content of Arabic language teaching in the world today. The Handbook is intended as a resource to be used in building Arabic language and teacher education programs and in guiding future academic research. Thirty-four chapters authored by leaders in the field are organized around nine themes: Background of Arabic Language Teaching; Contexts of Arabic Language Teaching; Communicative Competence in Arabic; The Learners; Assessment; Technology Applications; Curriculum Development, Design, and Models; Arabic Language Program Administration and Management; and Planning for the Future of Arabic Language Learning and Teaching. The Handbook for Arabic Language Teaching Professionals in the 21st Century will benefit and be welcomed by Arabic language teacher educators and trainers, administrators, graduate students, and scholars around the world. It is intended to create dialogue among scholars and professionals in the field and in related fields--dialogue that will contribute to creating new models for curriculum and course design, materials and assessment tools, and ultimately, better instructional effectiveness for all Arabic learners everywhere, in both Arabic-speaking and non-Arabic speaking countries.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

The active penetration of digital technology into all spheres of human activity has naturally led to processes of transformation of the education system. In recent times, the growing interest around the possibility of using artificial intelligence (AI) in the educational space indicate the relevance of the topic. The aim is to analyze methods and technologies for optimizing adaptive learning of the Russian language as a foreign one based on AI. The theoretical significance is determined by its contribution to exploring various approaches to integrating AI into the educational process, as well as further investigating the effectiveness of using AI in adaptive language learning. The practical significance lies in the possibility of applying its main research results when introducing adaptive learning platforms and applications into educational institutions and developing recommendations for optimizing language teaching programs. During the study, scientific and methodological literature dedicated to the application of AI in education was analyzed, considering its prospects and potential risks, the effectiveness of various platforms and tools for adaptive language learning was assessed. The study determines the role of AI in personalizing the educational process, optimizing the content and forms of training, improving assessment methods and the pace of learning based on real-time feedback. The paper also identifies the ethical challenges and technical limitations faced by researchers and practitioners in the field of using advanced AI technologies in the educational process. The research results allow us to assert that AI technologies should become an integral part of the modern language learning process, as they can significantly enhance student motivation and develop their speaking skills. The results and study material can be used in lectures on cognitive linguistics, computational linguistics, semantics and pragmatics, as well as the development of educational programs and technology.

This study discusses the revolution of Arabic language learning in the digital era, focusing on the role of Artificial Intelligence (AI) in enhancing language competence while fostering spiritual intelligence. Using a library research method, this study reviews recent literature (2020–2023) on the application of AI in language learning and Islamic education. The findings reveal that AI provides adaptive, interactive, and data-driven learning that strengthens the four core Arabic language skills (listening, speaking, reading, and writing), including specific aspects such as nahwu, sharaf, and phonetics. Furthermore, AI has the potential to support the integration of Islamic values through features such as prayer reminders, Qur’anic verses, and the internalization of learning ethics. This research highlights that AI should not be regarded merely as a technical tool but as a medium of technological spiritualization that combines intellectual competence with the cultivation of Islamic character and morality. The contribution of this article is to provide an integrative perspective on how AI can be utilized not only to strengthen Arabic linguistic competence but also as a medium for fostering Islamic spirituality within the context of education.

Fruits and vegetables preservation based on AI technology: Research progress and application prospects

Using novel comprehensive administrative micro data on business applications in the United States over the period 2004–2023, we examine the characteristics of business applications and resulting startups that aim to develop Artificial Intelligence (AI) technologies or use AI in providing goods and services. AI-related applications begin rising around 2012, accelerate after 2016, and surge in 2023, consistent with major advances in AI technologies around these years. This growth is highly uneven across states and sectors. AI applications are more likely to come from corporations, transition to startups at higher rates, and anticipate greater initial employment than other applications. Startups originating from these applications achieve higher employment and revenue, but have a lower survival rate, reflecting an ‘up-or-out' dynamic. Despite higher revenue, they exhibit a higher labor share and do not have higher labor productivity. The patterns suggest an accelerating AI diffusion through new business formation and a growing contribution from AI-related startups to business dynamism.

Deep Neural Networks (DNNs) have revolutionized the field of Artificial Intelligence (AI). These networks have enabled machines to learn complex tasks. DNNs are especially useful when the task involves large amounts of data. This is because they can effectively model the non-linear relationships that exist in the data. This allows them to make accurate predictions for previously unseen data. Deep Neural Networks have the potential to become a powerful tool to drive advances in artificial intelligence technologies. They can be used for a variety of tasks such as computer vision, natural language processing, and robotics. As the use of DNNs becomes more commonplace, it is expected that they will play a key role in creating smarter and more advanced Artificial Intelligence systems.

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

Artificial intelligence (AI) into education is becoming a transformative agent offering new chances for enhancing administrative processes, teaching, and learning. Particularly machine learning (ML) and deep learning (DL), recent advances in artificial intelligence technologies have shown great potential in predicting academic achievement, improving teaching strategies, and so supporting decision-making inside educational institutions. Notwithstanding these advances, there are obvious problems and limits that have to be addressed if we are to fully exploit the potential of artificial intelligence in the field of education. Recent research reveals significant limits like poor contextual adaptability of artificial intelligence models, insufficient integration of emerging technologies like augmented reality (AR), and challenges in improving distance learning. Although the integration of AR into educational systems is still under investigated, current artificial intelligence models usually rely on generalised datasets lacking the diversity of educational environments. The shift to online learning has underscored even more the requirement of solid, contextually relevant models to manage assessment strategies, student interaction, and technology acceptance. By means of a comprehensive examination of the corpus of present literature, this paper evaluates the present position of artificial intelligence applications in education so highlighting research needs and constraints. Emphasising their capacity to solve the discovered challenges, the survey focusses on ML and DL application. By means of analysis of current studies and recommended future research routes, this study aims to offer pragmatic insights and recommendations for enhancing the efficiency of artificial intelligence in educational environments.

Pharmacovigilance, the science of monitoring drug safety, plays a crucial role in identifying and mitigating adverse drug reactions (ADRs). However, underreporting in pharmacovigilance systems—estimated to have a median rate of 94 %—poses a significant threat to patient safety by hindering the detection of safety signals. The need to address these gaps is paramount, especially with the rising complexity of healthcare data. The advent of artificial intelligence (AI) and big data technologies offers promising solutions to overcome the limitations of traditional pharmacovigilance methods.The application of AI and machine learning (ML) technologies, including natural language processing (NLP) and deep learning, has the potential to revolutionize drug safety monitoring by automating the detection of ADRs from diverse data sources, such as electronic health records (EHRs), spontaneous reporting systems, and social media. These tools can process unstructured data and uncover patterns not easily identifiable through conventional approaches. Additionally, AI can enable real-time pharmacovigilance, which is especially critical in an era of increasing polypharmacy and diverse patient populations. AI-driven models are being utilized to detect drug-drug interactions (DDIs), predict ADRs, and enhance the overall efficiency of pharmacovigilance processes.Despite these advancements, several challenges remain. The performance of AI models is heavily dependent on the quality and quantity of data available. Inadequate or poorly curated datasets can lead to inaccurate ADR detection, particularly in resource-limited settings. Moreover, the heterogeneity of data sources necessitates robust AI models capable of integrating various types of data while ensuring accurate and reliable outputs. There is also a pressing need to address the transparency and explainability of AI models, as the opaque decision-making processes of current algorithms often impede their acceptance among pharmacovigilance professionals.Future directions must focus on improving the quality and standardization of datasets, advancing NLP techniques for better interpretation of clinical narratives, and developing explainable AI models. Regulatory frameworks should evolve to support AI deployment in pharmacovigilance, ensuring the establishment of best practices for AI implementation and the creation of large-scale, publicly available training datasets.Additionally, AI models should go beyond correlation-based approaches by integrating causal inference techniques, which will allow for a more accurate understanding of the relationship between drugs and ADRs. Human oversight will still be required to validate AI findings, but ongoing efforts to improve the robustness of AI systems will reduce dependency on manual interventions and scale the use of AI in pharmacovigilance.The integration of AI and big data in pharmacovigilance has the potential to transform drug safety monitoring, addressing many of the challenges posed by increasing data complexity and the need for real-time analysis. As these technologies continue to evolve, they promise to make pharmacovigilance more efficient, accurate, and comprehensive, thereby improving patient safety and rational drug use.