Quantum computing is emerging as a key enabler of digital transformation in data science and STEM education. This study investigates how quantum computing can be meaningfully integrated into higher education by combining a dual approach: a structured assessment of the specialized literature and a practical evaluation of educational tools. First, a science mapping study based on 281 peer-reviewed publications indexed in Scopus (2015–2024) identifies growth trends, thematic clusters, and international collaboration networks at the intersection of quantum computing, data science, and education. Second, a comparative analysis of widely used educational platforms—such as Qiskit, Quantum Inspire, QuTiP, and Amazon Braket—is conducted using pedagogical criteria including accessibility, usability, and curriculum integration. The results highlight a growing convergence between quantum technologies, artificial intelligence, and data-driven learning. A strategic framework and roadmap are proposed to support the gradual and scalable adoption of quantum literacy in university-level STEM programs.

Particulate matter smaller than 2.5 μm (PM2.5) in Madrid is a critical concern due to its impacts on public health. This study employs advanced methodologies, including the CRISP-DM model and hybrid Prophet–Long Short-Term Memory (LSTM), to analyze historical data from monitoring stations and predict future PM2.5 levels. The results reveal a decreasing trend in PM2.5 levels from 2019 to mid-2024, suggesting the effectiveness of policies implemented by the Madrid City Council. However, the observed interannual fluctuations and peaks indicate the need for continuous policy adjustments to address specific events and seasonal variations. The comparison of local policies and those of the European Union underscores the importance of greater coherence and alignment to optimize the outcomes. Predictions made with the Prophet–LSTM model provide a solid foundation for planning and decision making, enabling urban managers to design more effective strategies. This study not only provides a detailed understanding of pollution patterns, but also emphasizes the need for adaptive environmental policies and citizen participation to improve air quality. The findings of this work can be of great assistance to environmental policymakers, providing a basis for future research and actions to improve air quality in Madrid. The hybrid Prophet–LSTM model effectively captured both seasonal trends and pollution spikes in PM2.5 levels. The predictions indicated a general downward trend in PM2.5 concentrations across most districts in Madrid, with significant reductions observed in areas such as Chamartín and Arganzuela. This hybrid approach improves the accuracy of long-term PM2.5 predictions by effectively capturing both short-term and long-term dependencies, making it a robust solution for air quality management in complex urban environments, like Madrid. The results suggest that the environmental policies implemented by the Madrid City Council are having a positive impact on air quality.

The adoption of green technologies is a central component of sustainability transitions, yet it cannot be fully understood through individual cognitive evaluations alone. Increasing evidence suggests that citizens’ acceptance of environmentally oriented technologies is shaped by institutional and normative contexts that influence behavioural decision-making. In response to this challenge, this study develops a conceptual extension of the Technology Acceptance Model (TAM) to examine green technology acceptance from an integrative perspective grounded in environmental psychology and behavioural public policy. The analysis is based on a structured conceptual synthesis informed by bibliometric and semantic evidence from 789 Scopus-indexed articles published between 2021 and 2025, including early-indexed records from 2025 available at the time of data retrieval. Recurrent patterns within this corpus link technology acceptance research with institutional constructs such as trust, legitimacy, transparency, and community support. These insights are complemented by a structured expert elicitation exercise using the Analytic Hierarchy Process (AHP), applied in an exploratory manner to examine the relative salience of institutional dimensions within the proposed framework. Rather than offering empirical validation of behavioural relationships, the study advances a theoretically grounded conceptual model in which institutional trust and legitimacy operate as contextual conditions shaping the relationship between perceived usefulness, perceived ease of use, and behavioural intention in sustainability-related adoption contexts. Institutional transparency and community support further reinforce perceptions of fairness, credibility, and social endorsement. Overall, the resulting framework conceptualises green technology adoption as a socially and institutionally embedded process, providing a coherent analytical foundation for future empirical research and for the design of behavioural public policies aimed at supporting sustainability transitions.

Air quality modeling has become a strategic area within data science, particularly in urban contexts where pollution exhibits high variability and nonlinear dynamics. This study provides a mathematical and computational comparison between two predictive paradigms: the classical Long Short-Term Memory (LSTM) model, designed for sequential analysis of time series, and the quantum model Quantum Support Vector Machine (QSVM), based on kernel methods applied in Hilbert spaces. Both approaches are applied to real PM2.5 concentration data collected at the Plaza Castilla monitoring station (Madrid) over the period 2017–2024. The LSTM model demonstrates moderate accuracy for smooth seasonal trends but shows limited performance in detecting extreme pollution events. In contrast, the QSVM achieves perfect binary classification through a quantum kernel based on angle encoding, with significantly lower training time and computational cost. Beyond the empirical results, this work highlights the growing potential of Quantum Artificial Intelligence as a hybrid paradigm capable of extending the boundaries of classical models in complex environmental prediction tasks. The implications point toward a promising transition to quantum-enhanced predictive systems aimed at advancing urban sustainability.

The accelerated expansion of advanced technologies—particularly artificial intelligence, intelligent systems, and interactive digital environments—is influencing contemporary media ecosystems and contributing to changes in educational practices. This study provides a systematic and descriptive bibliometric mapping of recent scientific production on artificial intelligence in education, algorithmic ethics, and digital inequality. A total of 229 Scopus-indexed documents published between 2021 and 2026 were analyzed using Biblioshiny and VOSviewer to examine publication patterns, influential authors and sources, and the conceptual structure of the field. Results indicate a marked increase in research output since 2024, with an annual growth rate of 47.58%, an average of 8.68 citations per document, and an international co-authorship rate of 24.45%. These indicators reflect an expanding and increasingly collaborative research landscape, accompanied by a diversification of thematic priorities within the field. The analysis identifies five thematic clusters: (1) the technical foundations of AI and digital transformation; (2) intelligent and immersive learning environments; (3) personalized and adaptive learning systems; (4) AI literacy and pedagogical integration; and (5) ethical considerations, including algorithmic bias and educational robotics. The findings highlight the need for explicit justification of database selection, strengthened critical AI literacy, and context-sensitive strategies that address disparities in access, skills, and institutional capacity. Overall, this study offers a coherent overview of a research area that is currently expanding and undergoing conceptual reorganization, providing evidence-informed insights for future research, policy development, and the design of equitable AI-driven educational technologies.