This study presents a systematic mapping of inclusive pedagogical models in STEM education, focusing on integrating emotional intelligence, resilience, and motivation from a gender perspective. The research aims to identify strategies that promote inclusive learning environments and reduce gender gaps in STEM disciplines. A total of 753 studies were initially identified, with 51 articles meeting the inclusion criteria and being analyzed in depth. The results reveal that active methodologies, emotional intelligence training, mentoring programs, and the presence of female role models are key strategies for fostering women’s participation and retention in STEM fields. Additionally, the findings highlight the growing importance of integrating socio-emotional skills in STEM education to improve academic performance and strengthen resilience and motivation, particularly in under-represented groups. The study discusses challenges such as teacher resistance, lack of training, and contextual barriers that affect the implementation of inclusive models. It also reflects on the influence of cultural and linguistic factors, especially in Latin American contexts. This work expands the understanding of inclusive pedagogical practices in STEM and provides relevant recommendations for educators, curriculum designers, and policymakers aiming to foster equity and sustainability in education.

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in educational buildings represents an emerging opportunity to enhance intelligent environmental monitoring, data analysis, and energy optimization. This article presents a systematic literature review focused on AI-based applications in IoT-enabled learning environments, with special attention to indoor air quality (IAQ) management. A total of 585 documents were initially retrieved from Web of Science, Scopus, and IEEE Xplore using two targeted search strings. After removing duplicates and applying successive relevance filters based on title, abstract, and pertinence, 128 final documents were selected for full-text analysis. This study addresses four research questions: (RQ1) Which AI techniques are applied to environmental data analysis in educational contexts? (RQ2) What methods are used to detect sensor anomalies in IoT-based monitoring systems? (RQ3) How is AI applied in real-time decision making based on air quality indicators? (RQ4) What AI-driven strategies support energy efficiency in classrooms? The results reveal a growing use of machine learning and deep learning models, such as convolutional neural networks, decision trees, and LSTM architectures, particularly in applications focused on air quality classification, fault detection, and predictive control. Supervised learning methods were the most frequently applied, with CNN-based models leading in air quality prediction tasks and decision trees being preferred for anomaly detection. Deep learning approaches showed higher accuracy but required greater computational resources, limiting their use in low-cost educational environments. However, the literature also shows a lack of contextualized implementations, especially in low-resource or Latin American environments, and a limited focus on user-centered and educationally integrable systems. In addition, the review identifies a research gap regarding the integration of environmental and educational data, suggesting the potential for future empirical studies that evaluate real classroom conditions using IoT devices to inform AI-driven energy optimization strategies in academic settings.