Contributions and Discussions on Advancing 21st Century Skills Through EPATHLO: Prosocial Educational Games with Artificial Intelligence

Intelligent systems reference library, Год журнала: 2025, Номер unknown, С. 277 - 309

Опубликована: Янв. 1, 2025

Язык: Английский

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Computer Games for Entertainment and Education: A Literature Review and Exploration on Artificial Intelligence Integration

Intelligent systems reference library, Год журнала: 2025, Номер unknown, С. 25 - 62

Опубликована: Янв. 1, 2025

Язык: Английский

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Deep Learning in Oncology: Transforming Cancer Diagnosis, Prognosis, and Treatment

Emerging Trends in Drugs Addictions and Health, Год журнала: 2025, Номер unknown, С. 100171 - 100171

Опубликована: Фев. 1, 2025

Язык: Английский

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Issues and trends in generative AI technologies for decision making

Intelligent Decision Technologies, Год журнала: 2025, Номер 19(2), С. 574 - 584

Опубликована: Фев. 24, 2025

Generative AI (GenAI) technologies are examined through the lens of issues and trends related to decision making. After examining foundations technology particularly large language models (LLM), opportunities for GenAI be used in decision-making process intelligence, design, choice implementation explored. With its ability rapidly generate insights, present optimized solutions, provide detailed analysis given input, has demonstrated that it can assist augment human Although systems have potential transform content creation cognition, they also raise around accuracy, misinformation, ethics, bias, morality, social impacts, privacy, copyright, legality, explainability, among others. Addressing these challenges is important maximize efficacy

Язык: Английский

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An explainable machine learning method for predicting and designing crashworthiness of multi-cell tubes under oblique load

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 147, С. 110396 - 110396

Опубликована: Фев. 26, 2025

Язык: Английский

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Exploring the influence of artificial intelligence integration on personalized learning: a cross-sectional study of undergraduate medical students in the United Kingdom

BMC Medical Education, Год журнала: 2025, Номер 25(1)

Опубликована: Апрель 17, 2025

Abstract Background With the integration of Artificial Intelligence (AI) into educational systems, its potential to revolutionize learning, particularly in content personalization and assessment support, is significant. Personalized supported by AI tools, can adapt individual learning styles needs, thus transforming how medical students approach their studies. This study aims explore relationship between use for self-directed among undergraduate UK variables such as year study, gender, age. Methods cross-sectional involved a sample 230 from universities, collected through an online survey. The survey assessed usage including students’ attitudes towards accuracy, perceived benefits, willingness mitigate misinformation. Data were analyzed using descriptive statistics linear logistic regression examine associations demographics. Results analysis revealed that age significantly influenced pay tools ( p = 0.012) gender was linked concerns about inaccuracies 0.017). Female more likely take steps risks misinformation 0.045). also found variability based on with first-year showing higher reliance tools. Conclusion has greatly enhance personalized students. However, issues surrounding misinformation, equitable access need be addressed optimize education. Further research recommended longitudinal effects outcomes.

Язык: Английский

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What factors predict user acceptance of ChatGPT for mental and physical healthcare: an extended technology acceptance model framework

AI & Society, Год журнала: 2025, Номер unknown

Опубликована: Май 3, 2025

Язык: Английский

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Leveraging IoT Micro-Factories for Equitable Trade: Enhancing Semi-Finished Orange Juice Value Chain in a Citriculture Society

Systems, Год журнала: 2025, Номер 13(5), С. 384 - 384

Опубликована: Май 16, 2025

Sustainable development initiatives are essential for enhancing the social economy and environmental preservation in marginalised rural areas of Tanzania. This study examines impact an IoT micro-factory on sustainable development, addressing issues such as inadequate production techniques, agribusiness monopolisation practices, shortage small-scale factories, failure to leverage global market comparative advantages. It explores mediating role architectural innovation moderating industrial symbiosis. The surveyed 196 participants, including 100 orange farmers, 96 engineers beverage sector, conducted interviews with 3 managers consultants. SmartPLS 4 was used evaluate relationships between constructs. results indicate that both micro-factories networks (GPNs) have a direct influence social-economic development. Architectural mediates these relationships, while symbiotic moderates interaction innovation. findings emphasise importance gaps knowledge, skills, equitable trade. stakeholders should prioritise factories promote communities developing countries.

Язык: Английский

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TCDformer-based Momentum Transfer Model for Long-term Sports Prediction

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 128310 - 128310

Опубликована: Май 1, 2025

Язык: Английский

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Towards reconciling usability and usefulness of policy explanations for sequential decision-making systems

Frontiers in Robotics and AI, Год журнала: 2024, Номер 11

Опубликована: Июль 22, 2024

Safefy-critical domains often employ autonomous agents which follow a sequential decision-making setup, whereby the agent follows policy to dictate appropriate action at each step. AI-practitioners reinforcement learning algorithms allow an find best policy. However, systems lack clear and immediate signs of wrong actions, with consequences visible only in hindsight, making it difficult humans understand system failure. In learning, this is referred as credit assignment problem. To effectively collaborate system, particularly safety-critical setting, explanations should enable user better predict behavior so that users are cognizant potential failures these can be diagnosed mitigated. diverse have innate biases or preferences may enhance impair utility explanation agent. Therefore, paper, we designed conducted human-subjects experiment identify factors influence perceived usability objective usefulness for setting. Our study had two factors: modality shown (Tree, Text, Modified Programs) “first impression” agent, i.e., whether saw succeed fail introductory calibration video. findings characterize preference-performance tradeoff wherein participants language-based significantly more useable; however, were able objectively agent’s when provided form decision tree. results demonstrate user-specific factors, such computer science experience (p < 0.05), situational watching crash id="m2">< impact perception explanation. This research provides key insights alleviate prevalent issues regarding innapropriate compliance reliance, exponentially detrimental settings, providing path forward XAI developers future work on policy-explanations.

Язык: Английский

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