The Second Language Acquisition field has been significantly impacted by a greater emphasis on individualized learning and rapid developments in artificial intelligence (AI). Although increasingly adaptive language learning tools are being developed with the application of AI to the Computer Assisted Language Learning field, there have been concerns regarding insufficient information and teacher preparation. To effectively utilize these tools, teachers need an in-depth overview on recently developed AI-based language learning tools. Therefore, this review synthesized information on AI tools that were developed between 2017 and 2020. A majority of these tools utilized machine learning and natural language processing, and were used to identify errors, provide feedback, and assess language abilities. After using these tools, learners demonstrated gains in their language abilities and knowledge. This review concludes by presenting pedagogical implications and emerging themes in the future research of AI-based language learning tools.

Residents and fellows can play a helpful role in promoting safe and effective machine learning tools in sleep medicine. Here we highlight the importance of establishing ground truths, considering key variables, and prioritizing transparency and accountability in the development of machine learning tools within the field of artificial intelligence (AI). Through understanding, communication, and collaboration, in-training physicians have a meaningful opportunity to help progress the field towards safe machine learning tools in sleep medicine.

The article is devoted to the problem of using artificial intelligence (AI) tools in the system of teaching foreign languages in higher education institutions. The study includes an overview of modern approaches to language teaching, the advantages, and possibilities of using AI in the educational process, as well as an analysis of the impact of these tools on the academic achievements of students. The objectives of the research are as follows: to determine the effectiveness and impact of artificial intelligence tools on the process of teaching foreign languages in higher education institutions, and to identify the most productive pedagogical strategies for integrating AI tools into foreign language instruction. The integration of AI into the process of teaching foreign languages is substantiated, and the potential benefits for students and teachers are highlighted. Besides, interactive language programs and learning platforms that allow students to practice a foreign language in virtual situations, and receive feedback are analysed. The use of artificial intelligence makes it possible to create adaptive learning platforms that individually adjust to the needs and abilities of each learner. Particular attention is paid to the possibility of individualizing the learning process through artificial intelligence, which allows students to learn a foreign language according to their needs and pace. The use of AI makes it possible to identify the optimal teaching methods for each student based on their individual abilities, learning styles, and interests. This teaching approach helps learners master the material more effectively, ensures maximum adaptation to their needs, and provides more successful results in learning foreign languages. The risks of using AI in the educational process are identified, including the risk of turning language learning into a mechanical process without taking into account the context and cultural characteristics. The need to ensure system resilience and data security is emphasized, as a large amount of information that may relate to the personal data of students can be stored in AI systems. Based on the studied data and analysis, conclusions are drawn about the prospects for integrating artificial intelligence into foreign language teaching. It is noted that the use of artificial intelligence tools in foreign language teaching may prove to be a promising tool for improving the educational process, which will ensure more effective learning in the future.

The use of artificial intelligent (AI) tools in education has had a significant impact on learning experiences and outcomes. This review looks at recent advances in artificial intelligence tools and their implications for future research and practice. The review article followed the PRISMA method. Relevant articles on AI in education, specifically those describing the integration of AI or machine learning in undergraduate was extracted from the SCOPUS database. The inclusion criteria focus on articles directly related to teaching and training in structured programs that published in 2019 to 2023. A total of twelve documents were recognized and subjected to hand identification for subsequent analysis. The result shows that cognitive tutors, which are interactive learning environments facilitated by intelligent tutoring systems, improve learning outcomes. The challenge of balancing instructional assistance and self-directed learning, on the other hand, is inherent in AI-driven tools. AI tools in higher education provide numerous advantages at the institutional, social, and instructional levels. Disruptive AI tools such as ChatGPT have emerged, but challenges include job displacement concerns and the need for constant adaptation. AI language learning tools are important in language acquisition processes because they provide personalized learning paths and interactive engagement. However, ethical considerations and competencies related to AI-based tools in education are assessed, with parallels drawn to healthcare guidelines. It emphasizes the importance of strong ethical frameworks, as well as the critical role of educators and professionals in responsible AI use, in order to maintain a symbiotic relationship between human expertise and technological advancement. Overall, this review summarizes recent advances in the use of artificial intelligence tools to revolutionize education, emphasizing the importance of ongoing research, cross-disciplinary collaboration, and careful implementation.

The emergence of artificial intelligence (AI) and its derivative technologies, such as machine learning (ML) and deep learning (DL), heralds a new era of knowledge management (KM) presentation and discovery. KM necessitates ML for improved organisational experiences, particularly in making knowledge management more discoverable and shareable. Machine learning (ML) is a type of artificial intelligence (AI) that requires new tools and techniques to acquire, store, and analyse data and is used to improve decision-making and to make more accurate predictions of future outcomes. ML demands big data be used to develop a method of data analysis that automates the construction of analytical models for the purpose of improving the organisational knowledge. Knowledge, as an organisation’s most valuable asset, must be managed in automation to support decision-making, which can only be accomplished by activating ML in knowledge management systems (KMS). The main objective of this study is to investigate the extent to which machine learning applications are used in knowledge management applications. This is very important because ML with AI capabilities will become the future of managing knowledge for business survival. This research used a literature review and theme analysis of recent studies to acquire its data. The results of this research provide an overview of the relationship between big data, machine learning, and knowledge management. This research also shows that only 10% of the research that has been published is about machine learning and knowledge management in business and management applications. Therefore, this study gives an overview of the knowledge gap in investigating how ML can be used in KM for business applications in organisations.

<p>Artificial Intelligence (AI) is used in our day-to-day life. AI for journalism is a reality. Just like other vital aspects of our life, AI has also entered the world of journalism. Many reputed news organizations have adopted AI in journalism to perform various newsroom tasks. In today’s digital world, several technologies are powering journalism. One such technology that is transforming the journalism field is artificial intelligence. Accuracy is a core value of journalism. With AI and machine learning systems, there’s a statistical element of uncertainty which means it’s impossible to guarantee 100 percent accuracy. AI tools such as OpenAI’s ChatGPT, Microsoft’s Bing chatbot, and Google’s Bard have been subject to debate now. AI tools have the potential to assist in a variety of tasks, such as scraping PDF files, writing code, and translating languages. AI can be a helpful tool for journalism students and media researchers. A main concern among computational journalists is that AI sometimes hallucinates data. This review paper highlights how artificial intelligence can help journalists.</p>

Artificial intelligence (AI) is a helpful technology. By providing college students with more individualized and productive learning environments, they support education. This research examines how artificial intelligence (AI) works in education and enhances teaching and learning results for Mindoro State University students and teachers. The study will survey students and instructors to gauge their perspectives on AI, assess its benefits and obstacles in education, and propose strategies for successfully implementing AI into teaching and learning techniques. The poll will ask students about their understanding of AI and their experiences using AI-powered learning tools. According to the results of this study, students and teachers at the University have a strong belief and a positive outlook on the potential of AI to significantly improve the learning journey and outcomes for both students and instructors, as evidenced by the weighted mean rating of 4.37 for all categories combined. Overall, respondents strongly agree that artificial intelligence has the potential to considerably improve student and instructor learning and teaching outcomes at the University, as indicated by the average mean score of 4.23, which is characterized as "strongly agree." The study's findings will provide educators and policymakers with critical insights into the proper and successful use of artificial intelligence (AI) to improve student learning outcomes.

In this article, selected new directions in knowledge-based artificial intelligence (AI) and machine learning (ML) are presented: ontology development methodologies and tools, automated engineering of WordNets, innovations in semantic search, and automated machine learning (AutoML). Knowledge-based AI and ML complement each other ideally, as their strengths compensate for the weaknesses of the other discipline. This is demonstrated via selected corporate use cases: anomaly detection, efficient modeling of supply networks, circular economy, and semantic enrichment of technical information.

The repercussions of artificial intelligence implementations for edu- cation can be observed at both an individual and collective level. At the personal level, AI can recommend suitable learning materials to students. In contrast, at the collective level, it can affect the relationships between various stakeholders, such as how educators perceive learners’ progress. Artificial Intelligence (AI) can be categorized based on its competencies, specifically Artificial Narrow Intelligence (ANI) and Artificial General In- telligence (AGI). The former, also known as weak AI, refers to the AI tools that have been developed and implemented to date. Conversely, the latter pertains to AI tools that have not yet been accomplished and, if achieved, would be comparable to human intelligence and capabilities.

With the rapid development of the artificial intelligence industry, its application in higher vocational education has gradually become a research hotspot. This study explores how AI tools can enhance the quality of teaching, assist teachers, and improve students' learning experiences. Through the research and analysis of various AI tools, it was found that AI tools can adapt to the learning needs of students in a personalized manner, provide effective teaching support, and thus improve teaching efficiency. AI tools can also help teachers handle heavy daily tasks, such as grading assignments and exams, allowing them to invest more energy in instructional design. These tools can also provide enlightening data feedback to help teachers and students better understand the progress and effectiveness of learning. The results of this study indicate that the application of AI tools in higher vocational education has enormous potential and may have a profound impact on teaching methods. This paper is dedicated to analyzing the research on the application of AI tools in the field of higher vocational education.