The study demonstrates that AI integration in pesantren is an epistemically and institutionally negotiated process in which technological affordances are selectively incorporated through established structures of knowledge, authority, and ethical formation.
M. Qulyubi, S. Bahri, Hayaturrohman Hayaturrohman et al.· Munaddhomah: Jurnal Manajeme...· 0 citations
This narrative review synthesises clinically relevant evidence on AI-enabled early diagnostics, precision therapeutic pathways, and the principal structural barriers to responsible adoption to find AI is best positioned as an augmentation of clinical judgement rather than a replacement for it.
M. Ahmad, Muhammad Ibrahim Ahmed, Ahmad Sajjad Ashraf· Journal of Advances in Medic...· 0 citations
An integrated model synthesizing the technology acceptance model (TAM) and social exchange theory is developed and tested to explain the context-dependent drivers of sustainable knowledge sharing in the AI era, demonstrating the context-dependent role of core constructs like “perceived cost.”
Fenix Xin Feng, Jiang-Fei Chen, Yi-Qiao Liu et al.· VINE Journal of Information...· 0 citations
The findings demonstrated that the accuracy and completeness of chatbot-generated information significantly influence perceived usefulness, while information format influences perceived ease of use, while information format influences perceived ease of use.
Ghada ElSayad, Nor Hasliza Md. Saad· Journal of Applied Research...· 0 citations
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The model is based on the principles of interpretivist epistemology, construct-validity theory, and human-in-the-loop (HITL) AI principles and redefines AI as an enhancement tool and not an autonomous interpreter, which sets a conceptual background to future empirical verification of AI systems run by humans.
E. Oladunmoye, M. A. Adewusi· WAMDEVIN International Journ...· 0 citations
Machine Learning techniques were the most commonly applied AI methods in pharmacovigilance, followed by natural language processing, deep learning, neural networks, and symbolic and explainable AI.
Gita Kusnadi, Grace Wangge, Ari A. Perdana· International Journal of Pub...· 0 citations
Artificial intelligence (AI) systems do more than alter choice architecture: they select, explain, personalise and repeatedly reinforce options. This conceptual paper develops a cognitive–behavioural pathway model explaining when AI-assisted decision-making may narrow the intention–behaviour gap in environmentally sust...
This study investigates the transferability of adversarial attacks across XGBoost and LightGBM models in IDS and uses Explainable AI (XAI) techniques to identify features that influence model decisions and shows that feature-importance patterns can be used to examine model vulnerability under adversarial perturbations.
Abdlelah Abdlatef, Marwa D. Abdulkareem, Duygu Çakır· International Journal of Inf...· 1 citation
The HPA framework is introduced, a capability-centred evaluative framework that integrates perspectives from cognitive ergonomics, work design, and human-centred AI through four dimensions: Optimisation, Retention, Expansion, and Engagement.
Lisa Diamond, M. Oppermann· Proceedings of the 2026 Euro...· 0 citations
The findings suggest that trustworthy AI integration requires addressing pre-existing structural failures before introducing algorithmic decision support, and offer design recommendations for AI-supported appraisal systems, with implications for HCI research and organizational practice.
Andreas Harnindito, Akshat Sawarni· Proceedings of the 2026 Euro...· 0 citations
EASCI (Experience, Articulation, Structuring, Consolidation, Innovation), a five-stage framework that separates cognitive tasks and restricts AI involvement, is introduced, to mitigate working-memory overload.
A sequential political-economy model in which an AI vendor chooses auditability and substantive mitigation, a deployer monitors after adoption while facing switching costs, and enforcement depends on verifiable evidence is developed.
Florian A. D. Burnat, Brittany I. Davidson· arXiv.org· 2 citations
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Microsoft Research Blog· microsoft.comOct 30, 2026
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
Microsoft Research Blog· microsoft.comOct 29, 2026
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
