Showing 1 - 20 results of 126 for search '(implicit OR explicit) ai integration', query time: 0.12s Refine Results
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    Teachers' and students' perceptions of AI-generated concept explanations: Implications for integrating generative AI in computer science education by Soohwan Lee, Ki-Sang Song

    Published 2024-12-01
    “…The findings highlight the importance of considering both teachers' and students' perspectives when integrating GAI into computer science education. The study proposes strategies for designing GAI-based explanations that cater to learners' needs and emphasizes the necessity of explicit AI literacy instruction. …”
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    Interactive Viral Marketing Through Big Data Analytics, Influencer Networks, AI Integration, and Ethical Dimensions by Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Christos Klavdianos

    Published 2025-05-01
    “…By integrating foundational concepts from social network theory, advanced graph models, and behavioral dynamics, the paper demonstrates how the interplay between network structures, influencer behaviors, and AI-driven algorithms significantly redefines traditional marketing paradigms. …”
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    A systematic review of the impact of generative AI on postgraduate research: opportunities, challenges, and ethical implications by Mabirizi, Vicent, Katushabe, Calorine, Muhoza, Gloria, Rugasira, Jack

    Published 2025
    “…The review concludes with a recommendation for a tailored framework for the responsible integration of GenAI into postgraduate research with a focus on institutional oversight, human- AI collaboration, and ethical application. …”
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    Legal regulation of AI-assisted academic writing: challenges, frameworks, and pathways by Runyang Gao, Danghui Yu, Biao Gao, Heng Hua, Zhaoyang Hui, Jingquan Gao, Cha Yin

    Published 2025-04-01
    “…Regarding academic integrity, new risks, such as “credibility illusion” and “implicit plagiarism,” have become prominent in AI-generated content, necessitating adaptive regulatory mechanisms. …”
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    Socializing AI: Integrating Social Network Analysis and Deep Learning for Precision Dairy Cow Monitoring—A Critical Review by Sibi Chakravathy Parivendan, Kashfia Sailunaz, Suresh Neethirajan

    Published 2025-06-01
    “…This review critically analyzes recent advancements in dairy cow behavior recognition, highlighting novel methodological contributions through the integration of advanced artificial intelligence (AI) techniques such as transformer models and multi-view tracking with social network analysis (SNA). …”
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    Local AI Governance: Addressing Model Safety and Policy Challenges Posed by Decentralized AI by Bahrad A. Sokhansanj

    Published 2025-07-01
    “…Policies and technical safeguards for artificial intelligence (AI) governance have implicitly assumed that AI systems will continue to operate via massive power-hungry data centers operated by large companies like Google and OpenAI. …”
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    Towards AI Copyright Equilibrium by Dermawan Artha

    Published 2024-12-01
    “…To balance generative AI (GenAI) innovation with the protection of copyright for authors and performers, it is necessary to recalibrate the concept of “public interest.” …”
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    AI and Primary Care: Scoping Review by Gellert Katonai, Nora Arvai, Bertalan Mesko

    Published 2025-08-01
    “…Artificial intelligence (AI) has the potential to support PHC by enhancing diagnosis, workflow efficiency, and clinical decision-making. …”
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    Development of AI in the Caribbean: algorithms, challenges and recommendations by Amir Mohammed, Sheikh Ahad Ahmad

    Published 2025-03-01
    “…This article provides a concise overview, explicitly delving into popular AI algorithms employed in developing Anomaly Detection Systems (ADSs) applicable to Cyber-Physical Systems (CPSs), the state of AI and guidelines for adopting AI within the Caribbean region.…”
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    On the role of knowledge graphs in AI-based scientific discovery by Mathieu d’Aquin

    Published 2025-01-01
    “…However, for machine learning models to be truly used as a tool for scientific advancement, we have to find ways for the knowledge implicitly gained by these models from their training to be integrated with the explicitly represented knowledge captured through knowledge graphs. …”
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    AI for mental health: clinician expectations and priorities in computational psychiatry by Leo Fischer, Paula Antonia Mann, Minh-Hieu H. Nguyen, Stefan Becker, Shiva Khodadadi, Antonia Schulz, Sharmili Edwin Thanarajah, Jonathan Repple, Tim Hahn, Andreas Reif, Amir Salamikhanshan, Sarah Kittel-Schneider, Winfried Rief, Christoph Mulert, Stefan G. Hofmann, Udo Dannlowski, Tilo Kircher, Felix P. Bernhard, Hamidreza Jamalabadi

    Published 2025-06-01
    “…Together, this study provides a clinician-driven roadmap for AI integration, emphasizing predictive models based on ecological momentary assessment (EMA) data to forecast disorder trajectories and support real-world practice.…”
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    Implications of algorithmic bias in AI-driven emergency response systems by Katsiaryna Bahamazava

    Published 2026-01-01
    “…In this paper, we introduce a framework to evaluate the economic implications of algorithmic bias specifically for the emergency response systems that incorporate AI. Unlike existing research, which mostly addresses technical or ethical aspects in isolation, our approach integrates economic theory with algorithmic fairness to quantify and systematically analyze how biases in data quality and algorithm design impact resource allocation efficiency, response time equity, healthcare outcomes, and social welfare. …”
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    Exploring pre-service teachers’ generative AI readiness and behavioral intentions by Phuong Bui, Tiina Korhonen, Sini Kontkanen, Sorella Karme, Satu Piispa-Hakala, Marjaana Veermans

    Published 2025-08-01
    “…These findings highlight the need for structured AI literacy training in teacher education, emphasizing prompt engineering, evaluative judgment, and strategic AI integration. …”
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    A Systematic Review and Evaluation of Sustainable AI Algorithms and Techniques in Healthcare by Yehia Ibrahim Alzoubi, Ahmet E. Topcu, Ersin Elbasi

    Published 2025-01-01
    “…AI algorithms and tools are categorized into three groups: explicit AI algorithms for sustainability for energy efficiency (e.g., Federated Learning, Hybrid Quantum-Classical Optimization, Modified Lempel-Ziv-Welch (mLZW)), traditional AI algorithms for sustainable healthcare (e.g., Bidirectional Long Short-Term Memory (Bi-LSTM), Backpropagation Neural Networks (BPNNs), Convolutional Neural Networks (CNNs)), and sustainable AI techniques (e.g., Adaptive Sampling, AutoML for Model Compression (AMC)) that support low-power computing (e.g., edge computing, neuromorphic hardware, adaptive sampling). …”
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