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Teaching German within digital paradigm of education: AI-based approaches and tools
Get PDFIn view of the increased popularity of AI tools in teaching foreign languages, particularly German, and the corresponding concerns that arose, this article explored the futuristic prospects of learning German with AI. It examined how these technologies had revolutionized the learning process and what learners could expect in the future. The study’s methodology was based on a systemic paradigm and involved the use of content analysis and elements of case studies, relying on a wide array of literature sources extracted from general and specialized scientometric databases. The findings showed that AI in language teaching represented a powerful approach to engaging students and enhancing learning outcomes. The most innovative methods, such as the integration of massively multiplayer online role-playing games (MMORPGs) into educational processes, yielded the most effective results. The study attempted to outline the correlation between various AI-based teaching approaches and existing educational theories that characterized the contemporary educational landscape. Furthermore, it proposed an appropriate schematic model that could serve as a foundation for further research in the field, including studies with an interdisciplinary focus
Leveraging Gamification to Sustain Student Motivation and Emotional Resilience in Higher Education During Wartime: Case Studies from Ukraine
Get PDFIn the conditions of war in Ukraine, the educational process underwent profound transformations, accompanied by a decrease in student motivation and an increase in emotional exhaustion. The relevance of the topic was due to the need to find effective psychological and pedagogical tools to support student engagement in times of crisis. The purpose of the study was to find out the impact of gamification on student motivation; the object was the educational process in higher education institutions during the war. The research methodology was based on a questionnaire, comparative analysis, qualitative interviews, and empirical observation of gamified educational practices in three higher education institutions. The results of the study showed that team gamification, the use of adaptive online platforms, and instant feedback mechanisms were the most effective in wartime. Gamification was shown to increase academic engagement, reduce anxiety, and raise satisfaction with the learning process. Particularly high rates were recorded among psychology and engineering students. Gamified elements, such as virtual rewards, interactive missions, and cooperative tasks, proved to be effective not only in terms of learning but also in providing emotional support. The practical significance of the results lay in the possibility of adapting the cases to other educational contexts and developing strategies to overcome motivational decline during a crisis. The findings could be useful for teachers, educational administrators, and content developers who were looking for innovative solutions in the face of uncertainty
Application of Data Mining for the Prediction of Academic Performance in University Engineering Students at the National Autonomous University of Mexico, 2022
Get PDFIntroduction: In the present study, data mining is applied to predict the academic performance of university Engineering students at the National Autonomous University of Mexico during the year 2022. The introduction addresses the importance of understanding and anticipating academic performance as a means to implement more effective and personalized educational strategies.Objective: Develop a predictive model capable of identifying determining factors in the academic performance of students and predicting their future performance.Methodology: The methodology used includes the collection of academic and sociodemographic data from students, as well as the use of data mining techniques such as cluster analysis, decision trees and neural networks. The data was preprocessed to ensure quality and divided into training and test sets to validate the predictive model.Results: The results show that the developed model has a high accuracy in predicting academic performance, identifying key variables such as class attendance, participation in extracurricular activities and performance in previous exams. These variables were essential to build a robust and reliable model.Conclusion: the application of data mining has proven to be an effective tool to predict the academic performance of engineering students. This model not only provides a valuable tool for administrators and educators in decision making, but also opens new avenues for future research in the field of personalized education and improving academic performance
Use of artificial intelligence in the detection of coffee rust: An exploratory systematic review
Get PDFCoffee rust, caused by the fungus Hemileia vastatrix, is a fungal disease that affects coffee production and quality, so its early detection is crucial to prevent massive outbreaks and protect production. This article analyzes the most effective factors, the algorithms used, the accuracy of the models, and the challenges in the detection of coffee rust, through an exploratory systematic review of 35 empirical articles obtained from Scopus, IEEE Xplore and SciELO. The review identifies that the most determinant factors for detection include humidity, temperature and the presence of shade. The most commonly used algorithms are Convolutional Neural Networks (CNN), Support Vector Machines (SVM) and Random Forest, highlighting CNN for its ability to process and analyze images with an accuracy of 99.57%, followed by Artificial Neural Networks (ANN) with 98% and SVM with 96%. However, it is concluded that challenges remain such as the need for high quality labeled datasets, variability in environmental conditions and implementation costs. This study provides a comprehensive overview of recent advances and areas for improvement in coffee rust detection, providing information for researchers, practitioners and decision makers in the agricultural sector
Artificial neural networks with better analysis reliability in data mining
Get PDFIf there are relatively few cases, semi-supervised learning approaches make advantage of a large amount of unlabeled data to assist develop a better classifier. To expand the labeled training set and update the classifier, a fundamental method is to select and label the unlabeled instances for which the current classifier has higher classification confidence. This approach is primarily used in two distinct semi-supervised learning paradigms: co-training and self-training. However, compared to self-labeled examples that would be tagged by a classifier, the real labeled instances will be more trustworthy. Incorrect label assignment to unlabeled occurrences might potentially compromise the classifier\u27s accuracy in classification. This research presents a novel instance selection method based on actual labeled data. This will take into account the classifier\u27s current performance on unlabeled data in addition to its performance on actual labeled data alone. This uses the accuracy changes in the newly trained classifier over the original labeled data as a criterion in each iteration to determine whether or not the selected most confident unlabeled examples would be accepted by a subsequent iteration. Naïve Bayes (NB) will be used as the basic classifier in the co-training and self-training studies. The findings indicate that the accuracy and categorization of self-training and co-training will be greatly enhanced by SIS. As compared to semi-supervised classification methods, it will enhance accuracy, precision, recall, and F1 score, according to the findings
Artificial Intelligence-Driven Smart Aquaculture: Revolutionizing Sustainability through Automation and Machine Learning
Get PDFAI incorporation in aquaculture has transformed the industry completely, making crucial processes automated, maximizing productivity, and promoting sustainability. AI, specifically machine learning, refers to the application of modern smart aquaculture systems for tasks such as fish species classification, health monitoring, feed regulation, and management of water quality. It thereby sets inefficiency issues right while reducing impacts on the environment through real-time data-driven decision-making. This article deals with very recent developments in the applications of AI and machine learning in aquaculture, pointing out their importance in increasing production as well as eco-friendly management of aquatic environment
AI Application in Climate-Smart Agricultural Technologies: A Synthesis Study
Get PDFClimate change poses significant challenges to global agriculture, necessitating innovative solutions to enhance sustainability and productivity. Artificial intelligence (AI) has emerged as a key enabler in climate-smart agricultural technologies (CSAT), offering data-driven approaches to optimize resource use, mitigate climate risks, and improve decision-making. This study aims to evaluate AI\u27s integration into CSAT, focusing on its applications, benefits, and adoption challenges, particularly in climate-vulnerable regions. A bibliographic review employing machine learning (ML) and natural language processing (NLP) techniques was conducted to analyze over 40,000 scientific articles from global academic databases. Topic modeling and classification algorithms were applied to identify key trends, adoption barriers, and implementation pathways for AI-driven CSAT. The study also incorporated expert validation through the Delphi method to refine AI-generated insights and ensure their alignment with real-world agricultural challenges. Findings indicate that AI enhances decision-making in conservation agriculture, precision farming, water management, and market intelligence. AI-powered tools facilitate early pest detection, optimize irrigation schedules, and provide real-time climate advisory services, significantly improving agricultural resilience and food security. However, major barriers to AI adoption include high implementation costs, limited digital literacy, and inadequate infrastructure, particularly in low-income regions. Despite these challenges, AI-driven CSAT presents significant potential to transform agriculture, especially in climate-affected areas. Strategic investments in digital literacy, infrastructure development, and supportive policy frameworks are essential to facilitate AI adoption. Strengthening interdisciplinary collaboration among researchers, policymakers, and farmers will be crucial in advancing sustainable agricultural practices and ensuring long-term food security
A Practical Approach to Increase Crop Production Using Wireless Sensor Technology
Get PDFIntroduction; The global demand for food production continues to rise due to the growing population and changing consumption patterns. Traditional agricultural practices often fail to meet this demand efficiently, leading to the exploration of innovative technologies to enhance crop productivity. Wireless sensor technology (WST) has emerged as a promising tool to monitor and optimize agricultural practices, providing real-time data on various environmental parameters crucial for crop growth. Objective; This study aims to evaluate the effectiveness of wireless sensor technology in increasing crop production. By integrating WST into conventional farming practices, we seek to optimize resource usage, reduce waste, and improve crop yields. Methods; We have proposed an IoT-enabled soil nutrient classification and crop recommendation model to recommend crops. By incorporating machine learning, artificial intelligence (AI), the cloud, sensors, and other automated equipment into the decision-assisting system, farmers will be able to take decisive actions without relying entirely on regional farming offices. Results; The analysis showed that the plot using wireless sensor technology exhibited a significant increase in crop yield compared to the traditional plot. Soil moisture levels were maintained within optimal ranges, leading to better water usage efficiency. Additionally, the automated system adjusted fertilizer application based on real-time soil nutrient data, resulting in improved plant health and productivity. Conclusions; The integration of wireless sensor technology in agriculture presents a practical and effective approach to increase crop production. This technology enables precise monitoring and management of critical growth parameters, resulting in higher yields and more efficient resource use. Adopting WST can significantly contribute to meeting the global food demand while promoting sustainable farming practices
Integrating AI Chatbots in ESL and CFL Instruction: Revolutionizing Language Learning with Artificial Intelligence
Get PDFThe integration of artificial intelligence (AI) in language teaching has emerged as a transformative approach, particularly in the realms of English as a Second Language (ESL) and Chinese as a Foreign Language (CFL). This article explores the potential of AI chatbots as effective tools for enhancing language acquisition. By examining the current landscape of AI in language education, we identify the unique benefits that chatbots bring to the learning process, including personalized interaction, immediate feedback, and continuous engagement. The article delves into the design and implementation of AI chatbot systems tailored for ESL and CFL contexts, highlighting their role in vocabulary development, grammar practice, and conversational skills. Furthermore, it addresses the challenges and limitations of using chatbots in language teaching, proposing strategies for overcoming these obstacles. Through case studies and empirical data, the article demonstrates how AI chatbots can be harnessed to create a dynamic and interactive learning environment that caters to the diverse needs of language learners. Ultimately, this work advocates for the thoughtful integration of AI chatbots to complement traditional teaching methods, thereby paving the way for more effective and accessible language educatio