artificial intelligence

AI dependency and its relationships with anxiety, quality of life, and digital stress

The integration of artificial intelligence (AI) into higher education has accelerated, yet little is known about the psychological mechanisms underlying students’ reliance on AI. This study conceptualizes AI dependency as a complex cognitive–motivational construct that extends beyond mere usage, influencing anxiety, digital stress, and quality of life. A total of 521 participants (predominantly undergraduate, 80% female) were recruited via snowball sampling at King Abdulaziz University. Self-administered standardized instruments assessed AI dependency, AI-related general anxiety, digital stress, and quality of life. A network analysis approach was employed to examine the interrelations among AI dependency, cognitive offloading, anxiety, availability pressure, FoMO, digital overload, digital vigilance, social acceptance anxiety, and quality of life among university students. This approach allowed identification of central variables and conditional interactions within a dynamic psychological system. AI dependency emerged as a central node in the network, showing strong conditional associations with various digital stressors, including digital vigilance and fear of missing out. Anxiety appeared to occupy a bridging position in the network, linking cognitive reliance to environmental pressures, while these patterns of association were, in turn, related to reduced quality of life. Social acceptance anxiety translated cognitive pressures into relational–identity concerns, and cumulative effects manifested in reduced quality of life. The network revealed non-linear, conditional associations, highlighting that the psychological impact of AI dependency is mediated by cognitive, motivational, and contextual factors rather than by direct usage intensity alone. AI dependency is not a neutral or purely functional behavior but a central psychological construct with both potential advantages, such as reduced cognitive load and increased efficiency, and risks, including diminished autonomy, heightened anxiety, and long-term digital strain. These findings offer a culturally contextualized model for understanding AI’s influence on student well-being and provide a framework for interventions that target central nodes in the network to promote healthier engagement with AI in academic settings.

Data Set For Artificial Intelligence and Multicultural Religious Education: A Systematic Review of Ethical Mediation and Decolonial Practices

This dataset comprises the complete supplementary materials for a decolonial qualitative systematic review examining how Artificial Intelligence (AI) mediates Multicultural Religious Education (MRE) across Islamic, Christian, Jewish, Hindu, Buddhist, and indigenous educational traditions. The review synthesizes 254 peer-reviewed studies (2014–2026) from English, Indonesian, and Arabic sources, following PRISMA 2020 guidelines. The core contribution is an empirically-grounded framework identifying six AI mediation mechanisms along a continuum from algorithmic substitution (moral outsourcing, normative amplification, cultural flattening) to dialogic integration (hybrid deliberative mediation, contextual value re-embedding, dialogic ethical framing). The analysis further documents five decolonial possibilities emerging from Global South practices: epistemic re-centering, pedagogical sovereignty, cultural counter-flattening, governance innovation, and transnational solidarity. All supplementary materials supporting the main article are provided here, including complete study characteristics, inter-coder agreement matrices, quality appraisals, thematic analyses, and detailed appendices with operational definitions and conceptual frameworks. Supplementary Material Description SM-01 Study Characteristics Mapping – Complete bibliographic data, context, and methodology for all 254 studies SM-02 Inter-Coder Agreement Matrix – Theme-level Fleiss' κ scores demonstrating reliability (mean κ = 0.87) SM-03 JBI Critical Appraisal – Quality assessment scores with strengths and limitations for each study SM-04 PICo and Thematic-Content Analysis Matrix – Population-Interest-Context mapping with operational definitions SM-05 Theme-SWOT-CIMO Analysis – SWOT categorization with dominant mechanisms for all studies SM-06 Distribution of Reviewed Studies – Geographic, methodological, and educational level distribution (N=254) SM-07 Condensed Evidence Matrix – PICo × DEAM-MRE Domain × Region × Mechanism (N=254) SM-08 Mechanism Density Analysis – CIMO framework distribution across the corpus SM-09 Intercoder Reliability Summary – Derived from SM-02 with domain-level aggregation SM-10 MASTER DATA MATRIX SM-11 SECOND-CODER VERIFICATION REPORT SM-12 VALIDATION, RECONCILIATION SM-13 All References

FERIAL ZAYED THE WORLD'S FIRST OPEN-SOURCE DIAGNOSTIC ARTIFICIAL INTELLIGENCE FOR HUMANITY A Production-Ready Architecture for Global Health Integration

FERIAL ZAYED THE WORLD'S FIRST OPEN-SOURCE DIAGNOSTIC ARTIFICIAL INTELLIGENCE FOR HUMANITY A Production-Ready Architecture for Global Health Integration

FERIAL ZAYED THE WORLD'S FIRST OPEN-SOURCE DIAGNOSTIC ARTIFICIAL INTELLIGENCE FOR HUMANITY A Production-Ready Architecture for Global Health Integration

FERIAL ZAYED THE WORLD'S FIRST OPEN-SOURCE DIAGNOSTIC ARTIFICIAL INTELLIGENCE FOR HUMANITY A Production-Ready Architecture for Global Health Integration

Generative Artificial Intelligence: Ethical challenges and pedagogical implications in emerging educational contexts

Generative Artificial Intelligence (GAI) has rapidly transformed higher education by introducing new possibilities for personalized learning and pedagogical innovation. It has emerged as a powerful support system capable of information processing and decision-making tasks in higher education. Unlike traditional educational technologies, models such as ChatGPT enable dynamic interaction with learners. Students engage with knowledge, complete academic tasks, and regulate their performance. Building upon previous studies, this article incorporates an expanded theorical framework, and a deeper ethical analysis of student perceptions and emotions, emphasizing its role as a support tool in their learning processes. The results suggest that while ChatGPT enhances efficiency, accessibility, and learner autonomy, it also introduces significant risks related to overreliance, academic integrity, and the loss of critical thinking skills. The study concludes that should be integrated as an academic mediator, emphasizing the need for ethical guidelines and pedagogical institutional redesign.   Spanish-language metadata / Metadatos en españolTítulo en español:Inteligencia Artificial Generativa: desafíos éticos e implicaciones pedagógicas en contextos educativos emergentes Resumen:La Inteligencia Artificial Generativa (IAG) ha transformado rápidamente la educación superior al introducir nuevas posibilidades para el aprendizaje personalizado y la innovación pedagógica. Se ha consolidado como un potente sistema de apoyo capaz de realizar tareas de procesamiento de información y toma de decisiones en el ámbito de la educación superior. A diferencia de las tecnologías educativas tradicionales, modelos como ChatGPT permiten una interacción dinámica con los estudiantes. Estos interactúan con el conocimiento, realizan tareas académicas y regulan su propio desempeño. A partir de estudios previos, este artículo incorpora un marco teórico ampliado y un análisis ético más profundo de las percepciones y emociones de los estudiantes, haciendo énfasis en el papel de la IAG como herramienta de apoyo en sus procesos de aprendizaje. Los resultados sugieren que, aunque ChatGPT mejora la eficiencia, la accesibilidad y la autonomía del estudiante, también introduce riesgos significativos relacionados con la dependencia excesiva, la integridad académica y la pérdida de habilidades de pensamiento crítico. El estudio concluye que la IAG debería integrarse como mediadora académica, destacando la necesidad de establecer directrices éticas y de llevar a cabo un rediseño pedagógico e institucional. Palabras Claves:Inteligencia Artificial Generativa, entorno educativo, procesos de aprendizaje Smart citations: SciteAI. Dimensions.Open Alex.

The ethics of refusal: Why the Indigenous ‘no’ matters in the age of artificial intelligence

ENACT Analytical Report 09 - Methods to facilitate the traceback and mitigate the spoofing of telecommunications

Executive Smmary The tracing of communications is used to fight many kinds of crime, but its effectiveness depends on the resources that law enforcement agencies (LEAs) can dedicate to acting upon intelligence supplied by communication providers (CPs). China has extradited many thousands of scammers from overseas call centres despite not asking foreign countries to build new systems to facilitate traceback. The USA’s strategy concentrates on technology, including heavy expenditure on systems that facilitate traceback. However, the resulting number of prosecutions has been modest despite large volumes of scam calls originating on major US networks and a continued rise in the amounts lost by American consumers to scams instigated by a phone call. Europe can draw lessons from the different approaches adopted by China and the USA, including their respective strengths and limitations. In turn, Europe has much to teach the rest of the world about its own successes in tackling scams. There is some ambiguity in what different parties mean when they discuss traceback. This leads to confusion about what is required of a traceback system. Solutions are designed incorrectly if there is no clear statement of requirements. Several respondents to our survey noted that traceback essentially involves asking CPs where a communication came from, per the records they retained. Countries have been tracing communications for many years. So, when considering how to implement a new system, we should ask what we are trying to trace, where we expect to trace it to, and what is deficient with the systems to be replaced. The notion that a global traceback system stems from work on an allied problem: the authentication of communications.‘Authentication’ is a way of saying work has been done to prevent impersonation. Much of this work is imperfect. This report links the questions of how to tackle spoofing with how to trace communications. Tracing is more general in application. And if we know the origin of a communication with certainty, because it has been authenticated and the sender was subject to rigorous know-your-customer (KYC) checks, then ‘tracing’ that communication is just a peculiar way of saying we look at the sender’s address per the communication. Chapter 5 of this report evaluates 15 methods for authentication or tracing (or both). Considering all the options helps to clarify the strengths and weaknesses of each one. The effectiveness of traceback methods based on hop-by-hop tracing may be limited by criminal adaptation and cross-border implementation challenges. Hop-by-hop tracing is easy to defeat on a technical level because criminals will find alternate routes for their traffic to prevent it being traced. They have decades of experience of routing international communications traffic to evade detection. Hop-by-hop tracing can also be obstructed by laws that prevent the international exchange of relevant data. Even if all countries could be persuaded to support a global hop-by-hop method of tracing dialled voice calls, it would still be rendered redundant before it entered service because criminals would stop using dialled voice calls and start using other ways to transmit their voice.Phone users are switching to over the top services that allow them to speak and send messages to friends, family and businesses without dialling telephone numbers. So are scammers. We should be clear about whether we want to speed up the way tracing has been done in the past – by making enquiries of each CP in turn – or if LEAs want to identify the sources of crime as directly as possible. Transmitting tracing and authentication data in-band within telecoms networks means it follows the same route as the communication, and hence incurs the risk of being interrupted at any hop. Transmitting data out of band avoids those risks because it only requires cooperation between the authorities and CPs at the origin and destination of the communication. Out of band methods are less mature at present but have a more realistic prospect of eventual success. Some individual nations are setting up out of band data exchanges between their telcos without trying to standardise for international cooperation. Meanwhile, the limitations identified in current STIR/SHAKEN implementations have prompted renewed interest in alternative approaches, including out of band SHAKEN and Open Verified Communication. The EU has an opportunity to become a technological leader in this domain by connecting the threads of this work to its eIDAS roadmap for digital ID. European LEAs could look to the future by seeking general-purpose authentication and tracing systems that can also be applied to the newer generations of services that deliver voice and messaging communications through over-the-top and hybrid routes. This would involve out of band transfers of authentication and traceback data so the flow of data is independent of the specifics of the communication protocols implemented by the private sector. The benefit would be that a future-proofed tracing and authentication framework would ignore the increasingly artificial distinction between a voice call instigated using a dialled number or an app, or between text messages sent via SMS, RCS, WhatsApp and iMessage. For all the focus on novel network technologies, it is easy to overlook the fact that Europe is already a world leader in tackling spoofing. The widespread adoption of ECC Recommendation (23)03 has greatly reduced the number of inbound international calls that spoof the domestic phone numbers of Europeans.Where figures are available, it is not unusual to see this control being credited for reductions in scam calls of around 70 or 80 percent. Its effectiveness greatly alters the cost-benefit argument for other controls that require different countries to cooperate. Sender ID registries are similarly effective at reducing the most common text message scams. Some of the most effective anti-scam methods covered by this report have been put into effect in countries that have advanced anti-scam policies, such as Singapore and Australia. But Europe does not need to look to other continents for leadership. Ireland provides an example of a national strategy that has combined relatively simple measures with reported reductions in telecom-related scams. The EU is well-placed to coordinate efforts to tackle networked crime, just as it led the telecoms and tech industries through the adoption of the GSM standards for mobile telephony, the introduction of mobile roaming, and the data privacy rules enshrined in GDPR. ECC Recommendation (23)03 is an example of recent European leadership in consumer protection; eIDAS establishes a positive future for digital ID that serves the needs of EU citizens. By first concentrating on methods that obstruct the flow of scam communications, and then implementing bilateral exchanges of data with countries that want to aid the prosecution of criminals they harbour, Europe can make the most tangible gains in reducing scams. Europe can do this by favouring the authentication and tracing technologies that already prioritise the goal of consumer protection while respecting European standards for security and privacy. This report was prepared by the External Expert Erik Priezkalns, while the topic of the report was requested by Europol’s European Cybercrime Centre (EC3) to analyse existing and operational technical solutions to mitigate Caller-ID and Sender-ID spoofing. We’re collecting feedback on this report through the EU Survey Platform, if you’d like to share your thoughts please click on the link below. https://ec.europa.eu/eusurvey/runner/enact-report-feedback

ENACT Analytical Report 09 - Methods to facilitate the traceback and mitigate the spoofing of telecommunications

Executive Smmary The tracing of communications is used to fight many kinds of crime, but its effectiveness depends on the resources that law enforcement agencies (LEAs) can dedicate to acting upon intelligence supplied by communication providers (CPs). China has extradited many thousands of scammers from overseas call centres despite not asking foreign countries to build new systems to facilitate traceback. The USA’s strategy concentrates on technology, including heavy expenditure on systems that facilitate traceback. However, the resulting number of prosecutions has been modest despite large volumes of scam calls originating on major US networks and a continued rise in the amounts lost by American consumers to scams instigated by a phone call. Europe can draw lessons from the different approaches adopted by China and the USA, including their respective strengths and limitations. In turn, Europe has much to teach the rest of the world about its own successes in tackling scams. There is some ambiguity in what different parties mean when they discuss traceback. This leads to confusion about what is required of a traceback system. Solutions are designed incorrectly if there is no clear statement of requirements. Several respondents to our survey noted that traceback essentially involves asking CPs where a communication came from, per the records they retained. Countries have been tracing communications for many years. So, when considering how to implement a new system, we should ask what we are trying to trace, where we expect to trace it to, and what is deficient with the systems to be replaced. The notion that a global traceback system stems from work on an allied problem: the authentication of communications.‘Authentication’ is a way of saying work has been done to prevent impersonation. Much of this work is imperfect. This report links the questions of how to tackle spoofing with how to trace communications. Tracing is more general in application. And if we know the origin of a communication with certainty, because it has been authenticated and the sender was subject to rigorous know-your-customer (KYC) checks, then ‘tracing’ that communication is just a peculiar way of saying we look at the sender’s address per the communication. Chapter 5 of this report evaluates 15 methods for authentication or tracing (or both). Considering all the options helps to clarify the strengths and weaknesses of each one. The effectiveness of traceback methods based on hop-by-hop tracing may be limited by criminal adaptation and cross-border implementation challenges. Hop-by-hop tracing is easy to defeat on a technical level because criminals will find alternate routes for their traffic to prevent it being traced. They have decades of experience of routing international communications traffic to evade detection. Hop-by-hop tracing can also be obstructed by laws that prevent the international exchange of relevant data. Even if all countries could be persuaded to support a global hop-by-hop method of tracing dialled voice calls, it would still be rendered redundant before it entered service because criminals would stop using dialled voice calls and start using other ways to transmit their voice.Phone users are switching to over the top services that allow them to speak and send messages to friends, family and businesses without dialling telephone numbers. So are scammers. We should be clear about whether we want to speed up the way tracing has been done in the past – by making enquiries of each CP in turn – or if LEAs want to identify the sources of crime as directly as possible. Transmitting tracing and authentication data in-band within telecoms networks means it follows the same route as the communication, and hence incurs the risk of being interrupted at any hop. Transmitting data out of band avoids those risks because it only requires cooperation between the authorities and CPs at the origin and destination of the communication. Out of band methods are less mature at present but have a more realistic prospect of eventual success. Some individual nations are setting up out of band data exchanges between their telcos without trying to standardise for international cooperation. Meanwhile, the limitations identified in current STIR/SHAKEN implementations have prompted renewed interest in alternative approaches, including out of band SHAKEN and Open Verified Communication. The EU has an opportunity to become a technological leader in this domain by connecting the threads of this work to its eIDAS roadmap for digital ID. European LEAs could look to the future by seeking general-purpose authentication and tracing systems that can also be applied to the newer generations of services that deliver voice and messaging communications through over-the-top and hybrid routes. This would involve out of band transfers of authentication and traceback data so the flow of data is independent of the specifics of the communication protocols implemented by the private sector. The benefit would be that a future-proofed tracing and authentication framework would ignore the increasingly artificial distinction between a voice call instigated using a dialled number or an app, or between text messages sent via SMS, RCS, WhatsApp and iMessage. For all the focus on novel network technologies, it is easy to overlook the fact that Europe is already a world leader in tackling spoofing. The widespread adoption of ECC Recommendation (23)03 has greatly reduced the number of inbound international calls that spoof the domestic phone numbers of Europeans.Where figures are available, it is not unusual to see this control being credited for reductions in scam calls of around 70 or 80 percent. Its effectiveness greatly alters the cost-benefit argument for other controls that require different countries to cooperate. Sender ID registries are similarly effective at reducing the most common text message scams. Some of the most effective anti-scam methods covered by this report have been put into effect in countries that have advanced anti-scam policies, such as Singapore and Australia. But Europe does not need to look to other continents for leadership. Ireland provides an example of a national strategy that has combined relatively simple measures with reported reductions in telecom-related scams. The EU is well-placed to coordinate efforts to tackle networked crime, just as it led the telecoms and tech industries through the adoption of the GSM standards for mobile telephony, the introduction of mobile roaming, and the data privacy rules enshrined in GDPR. ECC Recommendation (23)03 is an example of recent European leadership in consumer protection; eIDAS establishes a positive future for digital ID that serves the needs of EU citizens. By first concentrating on methods that obstruct the flow of scam communications, and then implementing bilateral exchanges of data with countries that want to aid the prosecution of criminals they harbour, Europe can make the most tangible gains in reducing scams. Europe can do this by favouring the authentication and tracing technologies that already prioritise the goal of consumer protection while respecting European standards for security and privacy. This report was prepared by the External Expert Erik Priezkalns, while the topic of the report was requested by Europol’s European Cybercrime Centre (EC3) to analyse existing and operational technical solutions to mitigate Caller-ID and Sender-ID spoofing. We’re collecting feedback on this report through the EU Survey Platform, if you’d like to share your thoughts please click on the link below. https://ec.europa.eu/eusurvey/runner/enact-report-feedback

Towards Solar Nowcasting: Short-Term Solar Irradiance Forecasting with All-Sky Imagers and Artificial Intelligence

The increasing share of solar photovoltaics (PV) in power grids and buildings is reshaping the energy landscape but also introducing operational challenges due to the inherent variability of solar irradiance. Rapid cloud movements can cause short-term fluctuations in PV output, making it difficult to ensure grid stability, manage power imbalances, and optimize energy use within Building Energy Management Systems (BEMS). This thesis, titled Towards Solar Nowcasting, contributes to overcoming these challenges by advancing short-termsolar forecasting techniques, with a particular emphasis on real-time, image-based forecasting also known as solar nowcasting. To this end, the research begins with a comprehensive review of solar forecasting techniques, highlighting the growing importance of Artificial Intelligence (AI) methods in capturing complex irradiance patterns across diverse time horizons, from ultra-short (1 minute) to 24 hours ahead. A particular focus is placed on the potential of neural networks and hybrid AI models, as well as the critical need for standardized datasets and benchmarking practices to ensure accurate model evaluation and performance. This review forms the foundation for the development of innovative nowcasting solutions. Building on these insights, the thesis presents a data-driven short-termsolar forecasting framework using all-sky imagers (ASIs) and deep learning. Specifically, cloud movement is tracked using optical flow models, and future sky states are generated to serve as inputs for Convolutional Neural Networks (CNNs) and Long Short-TermMemory (LSTM) networks. This hybrid approach enables accurate Global Horizontal Irradiance (GHI) predictions up to 20 minutes ahead. The developed models demonstrate significant improvements over baseline persistence methods, achieving ramp skill scores of up to 39% under sunny conditions. To address the limitations of existing methods in complex weather scenarios, the thesis further develops an innovative hybrid AI framework that combines superpixel-based cloud detection, Support Vector Machines (SVMs), CNNs, and Kalman filtering. This approach integrates high-resolution sky images, advanced computer vision techniques, and adaptive weather classification to deliver reliable GHI forecasts for horizons up to one hour. Tested on extensive datasets from the Netherlands, the method showed marked improvements in forecast accuracy, particularly under challenging conditions such as overcast or rainy skies, where conventional models often fail. Finally, the thesis translates these forecasting advancements into practical applications for congestion management, power imbalance mitigation, and building energy management. By benchmarking statistical, AI-based, and sky-imager-driven PV forecasting techniques, the study demonstrates that the integration of real-time sky image data significantly enhances short-term PV power forecasts. This, in turn, supports grid operators in implementing proactive congestion control, reduces reliance on costly balancing reserves, and enables intelligent energy management strategies within buildings, including load shifting, battery storage optimization, and increased PV self-consumption. In summary, in this thesis, advanced short-termsolar forecasting techniques have been developed to address key operational challenges arising fromthe growing integration of solar photovoltaics (PV) into modern energy systems. By combining all-sky imaging, artificial intelligence, and hybrid machine learning frameworks, this work demonstrates significant improvements in the accuracy and reliability of solar nowcasting. The proposed methodologies provide practical solutions for congestion management, power imbalance reduction, and optimized building energy management. Overall, this thesis contributes to enabling a more reliable and efficient integration of solar energy, supporting the broader goals of grid stability, energy flexibility, and the ongoing energy transition.

Generative artificial intelligence for sustainable tourism: alleviating cognitive overload to foster well-being and eco-responsible behavior

Automatic classification of cyber incidents using privacy-preserving artificial intelligence

As cyber incidents increase in complexity, diversity and frequency, cybersecurity practitioners find it more challenging to extract meaningful threat intelligence and insights from cyber incident reports. This problem is worsened by the limited number of these reports; as cyber incident victims may withhold and/or downplaytheir reports due to reputational and privacy concerns. Therefore, this study aims to determine whether cyber incident reports, which have been stripped of personal data (pseudonymised), can be classified according to the Spanish INCIBE Cyber Incident Taxonomy. Seven transformers (SecBERT, BERT, DistilBERT, BERTweet, SecRoBERTa, ALBERT, RoBERTa) and four traditional machine learning classifiers (Random Forest, Multinomial Naïve Bayes, XGBoost, Support Vector Machine, using two encoders - Bag of Words and Term Frequency - Inverse Document Frequency), were trained to classify cyber incidents which were pseudonymised using Data Masking, Data Tokenisation and Data Substitution. The best-performing model attained an F1-score of 78.5% on the non-pseudonymised CECILIA-10C-900 dataset and 83% on the dataset, which was pseudonymised using Data Masking, D-CECILIA-10C-900-MAS. Therefore, this research demonstrates that it is possible for a single AI model to balance enhanced privacy with strong threat intelligence analysis capabilities.

Automatic classification of cyber incidents using privacy-preserving artificial intelligence

As cyber incidents increase in complexity, diversity and frequency, cybersecurity practitioners find it more challenging to extract meaningful threat intelligence and insights from cyber incident reports. This problem is worsened by the limited number of these reports; as cyber incident victims may withhold and/or downplaytheir reports due to reputational and privacy concerns. Therefore, this study aims to determine whether cyber incident reports, which have been stripped of personal data (pseudonymised), can be classified according to the Spanish INCIBE Cyber Incident Taxonomy. Seven transformers (SecBERT, BERT, DistilBERT, BERTweet, SecRoBERTa, ALBERT, RoBERTa) and four traditional machine learning classifiers (Random Forest, Multinomial Naïve Bayes, XGBoost, Support Vector Machine, using two encoders - Bag of Words and Term Frequency - Inverse Document Frequency), were trained to classify cyber incidents which were pseudonymised using Data Masking, Data Tokenisation and Data Substitution. The best-performing model attained an F1-score of 78.5% on the non-pseudonymised CECILIA-10C-900 dataset and 83% on the dataset, which was pseudonymised using Data Masking, D-CECILIA-10C-900-MAS. Therefore, this research demonstrates that it is possible for a single AI model to balance enhanced privacy with strong threat intelligence analysis capabilities.