Teachers' and students' perceptions of AI-generated concept explanations: Implications for integrating generative AI in computer science education by Soohwan Lee, Ki-Sang Song

“…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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Integrating Textual Queries with AI-Based Object Detection: A Compositional Prompt-Guided Approach by Silvan Ferreira, Allan Martins, Daniel G. Costa, Ivanovitch Silva

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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

“…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

“…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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The use of artificial intelligence in STEM communication-intensive courses and its impact on science identity by Jennifer Garcia Ramos, Zakiya Wilson-Kennedy

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Enhancing surgical object detection in laparoscopic cholecystectomy with explicit positional relationship modeling by Yinan Xu, Yutong Ban, Yue Zhao, Dolores Krauss, Christiane Bruns, Jennifer Eckhoff, Hans Fuchs

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Explainable AI-Integrated and GAN-Enabled Dynamic Knowledge Component Prediction System (DKPS) Using Hybrid ML Model by Swathieswari Mohanraj, Shanmugavadivu Pichai

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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

“…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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Towards AI Copyright Equilibrium by Dermawan Artha

“…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

“…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

“…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

“…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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Personal experience with AI-generated peer reviews: a case study by Nicholas Lo Vecchio

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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

“…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

“…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

“…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

“…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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Challenges and Benefits of Implementing AI in Timber Construction for Circular Economy Goals by Mohsen Ghobadi, Samad M. E. Sepasgozar

“…However, it has not yet been fully integrated into the application of CE principles and has explicitly been overlooked in the context of timber construction. …”
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Evaluating Generative AI in Mental Health: Systematic Review of Capabilities and Limitations by Liying Wang, Tanmay Bhanushali, Zhuoran Huang, Jingyi Yang, Sukriti Badami, Lisa Hightow-Weidman

“…ConclusionsFuture research could use more sophisticated evaluation methods, such as few-shot and chain-of-thought prompting to fully uncover GenAI’s potential. Longitudinal studies and broader comparisons with human benchmarks are needed to explore the effects of GenAI-integrated mental health care.…”
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Using generative AI for reading question creation based on PIRLS 2011 framework by Zhipeng Wen, Samuel Kai Wah Chu

“…Findings reveal that ChatGPT 3.5 excels at generating factual questions, leveraging explicit details from text, and demonstrates improvement in interpret-and-integrate questions when detailed instructions are provided. …”
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