Aim/Purpose: Evaluate teachers’ perceptions, strategies, and challenges in integrating artificial intelligence (AI) into K-12 education and identify patterns and trends in the data from the reviewed studies. Background: This systematic review examines a decade of innovation to explore the transformative impact of AI on education (2014–2024). Adhering to PRISMA 2020 guidelines, the study uncovers key trends, challenges, and breakthroughs in AI-driven teaching and learning, offering a comprehensive perspective on how AI reshapes educational practices and methodologies. Methodology: The study employs a systematic review to analyze the implementation of AI techniques and tools in primary education, following the PRISMA 2020 guidelines to ensure the reliability and effectiveness of the findings. To achieve this, an extensive search was conducted in academic databases such as Web of Science, Scopus, and ERIC, focusing on empirical studies and peer-reviewed articles published between 2014 and 2024. Only accessible, peer-reviewed articles classified under Education and Educational Research and published in English or Spanish were selected. The search strategy was structured into five categories aligned with the research questions to identify relevant studies accurately. The selection process was carried out in three phases – Identification, Screening, and Inclusion – applying predefined criteria to guarantee the quality and relevance of the selected studies. Of an initial total of 514,919 articles, 488,940 were excluded for not meeting the inclusion criteria. After removing duplicates and evaluating titles, abstracts, and full texts, a final set of 28 studies was included. Contribution: The study explores the integration of AI in primary education, revealing both teachers’ enthusiasm and the challenges they face. While AI is perceived as a tool to enhance critical thinking, problem-solving, and student engagement, its implementation is limited by insufficient training, resources, and institutional support. Despite these obstacles, teachers show confidence in designing AI-integrated curricula, though this is weakened by inadequate infrastructure and technical support, highlighting the need for continuous professional development. The study also stresses the importance of establishing a competency framework for AI literacy and adopting a systemic approach to AI education. Additionally, ensuring safe learning environments by addressing data privacy and AI biases remains a key challenge. Overcoming these issues is essential for the ethical and effective integration of AI, maximizing its benefits while safeguarding student equity and security. Findings: - Educators see the potential of AI to personalize learning. - Barriers are lack of training and resources for teachers. - Importance of continuous training in digital skills. - Need for policies that promote AI literacy. - Collaboration with experts to optimize AI in the classroom. Recommendations for Practitioners: Teachers are encouraged to collaborate in using AI tools to enhance educational outcomes, supported by continuous professional development programs, clear policies that safeguard privacy and promote equality, and a framework that preserves human autonomy in integrating AI technologies. Recommendation for Researchers: The lack of empirical research on AI interventions in education limits understanding of its true impact, highlighting the need for future studies to fill this gap and optimize its application for greater educational benefits. Impact on Society: The integration of AI in K-12 education is not just an opportunity; it is a necessity to prepare future generations for an increasingly digital world. While AI has the potential to revolutionize learning by fostering critical thinking, personalization, and engagement, its impact depends on how effectively it is implemented. To ensure its benefits, it is essential to empower educators and students with AI literacy, address issues like bias and data privacy, and establish robust legal frameworks for fair and transparent use. Without proactive policies, AI could widen educational inequalities instead of reducing them. A responsible, human-centered approach is needed to create an inclusive, ethical, and effective AI-powered education system. Future Research: The article highlights the urgency of future empirical research to better understand the real impact of AI in education, as the lack of intervention studies limits its optimal application. Analyzing how AI influences learning outcomes, teaching dynamics, equity, and accessibility is essential, along with investigating the pedagogical competencies and technological conditions that affect its adoption. To this end, expanding the scope of studies is recommended by incorporating multicultural and multilingual perspectives, exploring AI applications across various disciplines and educational levels, and promoting interdisciplinary approaches that address ethical, social, and pedagogical dimensions.

This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. Conducted in accordance with PRISMA guidelines and using a novel adaptation of the Downs and Black (D&B) scale, this study evaluates 89 research papers and provides insights into the evolving landscape of RUL estimation. Our analysis reveals an evolving landscape of methodological approaches, with different techniques showing distinct capabilities in capturing complex degradation patterns in EV batteries. While recent years have seen increased adoption of DL methods, the effectiveness of different approaches varies significantly based on application context and data characteristics. However, we also uncover critical challenges, including a lack of standardized evaluation metrics, prevalent overfitting problems, and limited dataset sizes, that hinder the field’s progress. To address these, we propose a comprehensive set of evaluation metrics and emphasize the need for larger and more diverse datasets. The review introduces an innovative clustering approach that provides a nuanced understanding of research trends and methodological gaps. In addition, we discuss the ethical implications of DL in RUL estimation, addressing concerns about privacy and algorithmic bias. By synthesizing current knowledge, identifying key research directions, and suggesting methodological improvements, this review serves as a central guide for researchers and practitioners in the rapidly evolving field of EV battery management. It not only contributes to the advancement of RUL estimation techniques but also sets a new standard for conducting systematic reviews in technology-driven fields, paving the way for more sustainable and efficient EV technologies.

Incorporating technology into virtual education encourages educational institutions to demand a migration from the current learning management system towards an intelligent virtual educational system, seeking greater benefit by exploiting the data generated by students in their day-to-day activities. Therefore, the design of these intelligent systems must be performed from a new perspective, which will take advantage of the new analytical functions provided by technologies such as artificial intelligence, big data, educational data mining techniques, and web analytics. This paper focuses on primary education in developing countries, showing the design of an intelligent virtual educational system to improve the efficiency of primary education through recommendations based on reliable data. The intelligent system is formed of four subsystems: data warehousing, analytical data processing, monitoring process and recommender system for educational agents. To illustrate this, the paper contains two dashboards that analyze, respectively, the digital resources usage time and an aggregate profile of teachers’ digital skills, in order to infer new activities that improve efficiency. These intelligent virtual educational systems focus the teaching–learning process on new forms of interaction on an educational future oriented to personalized teaching for the students, and new evaluation and teaching processes for each professor.

This article presents the results obtained by analyzing the data traffic that originated in a system with intelligent soil moisture sensors (Wireless Sensor Network—WSN) that transmit through a wireless network. This study sought to integrate smart agriculture and IoT (Internet of Things) applications in potato crops in various rural settings. Using these measurements, the data analysis was performed through the ARIMA (autoregressive integrated moving average model) and SARIMA (seasonal autoregressive integrated moving average model) time series following the Box–Jenkins methodology. GRETL (Gnu Regression, Econometrics and Time-series Library) free software was used to generate a teletraffic behavior prediction model in a larger-scale implementation. The main objective was the creation of a model that allows an analysis and simulation about the behavior of the main performance parameters that a medium-scale WSN system would have for the monitoring of a crop. Thanks to this analysis, it will be possible to determine the technical characteristics that a sensor deployment should have in a specific area and for a specific crop.