Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers examined why fashion designers adopt Artificial Intelligence Generated Content, which is described as increasingly used in creative design. Using the Stimulus-Organism-Response framework combined with Self-Determination Theory, they surveyed 318 Chinese fashion-design practitioners and analyzed 21 items with PLS-SEM to link perceived risk, social influence and facilitating conditions to autonomy, competence, relatedness and behavioral intention.
A peer-reviewed survey published December 26, 2023 reviewed literature on fairness and bias in AI, focusing on sources such as data, algorithm, and human decision biases and the emerging issue of generative AI bias in synthetic media across healthcare, employment, criminal justice, and credit scoring.
This peer-reviewed review in GeroScience appraises CT-based prediction of hematoma expansion after spontaneous intracerebral hemorrhage, a major determinant of early deterioration. It compares contrast-enhanced signs like spot, leakage and iodine signs with non-contrast signs including blend, black hole, island, satellite, hypodensity and swirl signs, plus shape and heterogeneity, and evaluates composite scores and AI approaches including radiomics, machine learning and deep learning.
A scoping review published August 15, 2026 examined how nursing academics perceive and use AI in nursing education, synthesizing 15 studies from eight countries with 2004 academics. It found most believe AI will revolutionise education but actual use is selective and conservative, concentrated at the augmentation level for productivity and research writing rather than assessment or transformative pedagogy.
This peer-reviewed review from December 2025 examines how AI is reshaping cybersecurity, documenting a rise in AI-powered threats such as automated phishing, deepfake identity fraud, and adversarial machine learning attacks affecting communication networks, healthcare, finance, and government.
Between November 2024 and July 2025, researchers studied Ecuadorian perceptions of generative AI in the 2025 presidential election using surveys, expert interviews and sentiment analysis. They found participants believed AI-generated disinformation affected the result, did not improve trust in candidates or the electoral process, and produced widespread concern, mistrust and fear.
Published May 2024, this peer-reviewed study examined the downsides of AI in education through a systematic review of 56 studies and a validation survey of 260 participants from a Saudi Arabian university. It developed and tested a model that confirms concerns spanning human connection, data privacy and security, algorithmic bias, transparency, critical thinking, access equity, ethics, teacher development, reliability, and AI-generated content.
Published May 23, 2024, this emerging technology report reviews Google Gemini as a multimodal generative AI tool, describing its ability to process text, image, audio, and video inputs and generate diverse content, and summarizing recent empirical studies and technology-in-practice examples in education.
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