AI is employed in education to personalize learning experiences, provide adaptive feedback, and automate administrative tasks. It utilizes machine learning algorithms, natural language processing, and data analytics to enhance student engagement, optimize teaching methods, and streamline educational processes, leading to more effective and personalized education.
Researchers introduce the REHEARSAL system, a novel approach to improving conflict resolution skills. This interactive dialogue system allows users to engage in realistic conflict simulations, receive valuable feedback, and explore alternative approaches, all guided by Interest-Rights-Power (IRP) prompting rooted in conflict resolution theory.
This paper explores how artificial intelligence (AI) is revolutionizing regenerative medicine by advancing drug discovery, disease modeling, predictive modeling, personalized medicine, tissue engineering, clinical trials, patient monitoring, patient education, and regulatory compliance.
This study examines the public's reactions and sentiments towards ChatGPT's role in education through Twitter data analysis. It reveals a complex interplay of positive and negative sentiments, highlighting the need for comprehensive exploration of AI's integration into education and the importance of considering diverse perspectives.
This paper explores the integration of artificial intelligence (AI) and computer vision (CV) technologies in addressing urban expansion challenges, particularly in optimizing container movement within seaports. Through a systematic review, it highlights the significant role of AI and CV in sustainable parking ecosystems, offering valuable insights for enhancing seaport management and smart city development.
A study comparing the creativity of AI chatbots and human participants in the Alternate Uses Task (AUT) reveals that chatbots consistently produce creative responses, often surpassing humans. However, the study underscores the unique complexity of human creativity, highlighting that while AI can excel, it still struggles to fully replicate or surpass the best human ideas.
This study introduces an innovative framework for speech emotion recognition by utilizing dual-channel spectrograms and optimized deep features. The incorporation of a novel VTMel spectrogram, deep learning feature extraction, and dual-channel fusion significantly improves emotion recognition accuracy, offering valuable insights for applications in human-computer interaction, healthcare, education, and more.
This research paper explores the intersection of artificial intelligence (AI) and education by analyzing AI educational curricula and textbooks using text mining techniques. The study assesses the presence of key AI concepts, topic structures, and practical tools, offering valuable insights for structuring effective AI curricula and improving alignment with educational resources.
This research delves into the adoption of Artificial Intelligence (AI) in academic libraries, comparing the approaches of top universities in the United Kingdom (UK) and China. The study highlights that while Chinese universities emphasize AI in their strategies, British universities exhibit caution, with a limited focus on AI applications in libraries, and underscores the need for careful consideration of AI's role in higher education libraries, taking into account factors such as funding, value, and ethics.
Researchers explore the fusion of artificial intelligence, natural language processing, and motion capture to streamline 3D animation creation. By integrating Chat Generative Pre-trained Transformer (ChatGPT) into the process, it enables real-time language interactions with digital characters, offering a promising solution for animation creators.
A new study led by North Carolina State University reveals that an AI capable of self-examination performs better when it opts for neural diversity over uniformity. This "meta-learning" approach makes the AI up to 10 times more accurate in complex tasks, such as predicting the motion of galaxies, compared to conventional, homogenous neural networks.
Researchers discuss how artificial intelligence (AI) is reshaping higher education. The integration of AI in universities, known as smart universities, enhances efficiency, personalization, and student experiences. However, challenges such as job displacement and ethical considerations require careful consideration as AI's transformative potential in education unfolds.
Researchers have introduced the Fine-grained Energy Consumption Meter (FECoM) framework to tackle the energy consumption challenges of Deep Learning (DL) models. This novel approach provides precise method-level energy measurement, offering a granular view of energy consumption and enabling energy-efficient development practices in various domains.
Researchers delve into the integration of machine learning (ML) to refine prognostic accuracy in non-surgical root canal treatments (NSRCT). By utilizing advanced ML models like Random Forest (RF) and K Nearest Neighbours (KNN), traditional clinical prognostic approaches are enhanced, resulting in improved sensitivity and accuracy in predicting NSRCT outcomes.
Researchers explore the integration of AI and psychometric testing to measure emotional intelligence (EI) using eye-tracking technology. By employing machine learning models, the study assesses the accuracy of EI measurements and uncovers predictive eye-tracking features. The findings reveal the potential of AI to achieve high accuracy with minimal eye-tracking data, paving the way for improved measurement quality and practical applications in fields like management and education.
This study dives into the metaverse's influence on the interaction between humans and AI, specifically focusing on AI news anchors. Employing an expectation confirmation theory-based model, researchers explore the factors driving users' intention to watch news from AI anchors. The findings highlight the pivotal roles of perceived intelligence, satisfaction, and trust, shedding light on insights crucial for commercializing AI news anchors.
Researchers delve into the intricacies of user intent modeling in conversational recommender systems, revealing symbiotic relationships between models and features. Through systematic literature reviews and real-world case studies, they present a structured decision model that emphasizes practical adaptability and promotes collaboration, equipping academics and practitioners to innovate in the realm of AI-driven conversations.
Researchers investigate the potential of combining GPT-4 with plugins like Wolfram Alpha and Code Interpreter for solving complex mathematical and scientific problems. The study explores how this collaborative approach amplifies AI's capabilities in problem-solving, showcasing strengths and challenges in handling diverse problem scenarios. While GPT-4 and plugins exhibit promise, the study highlights the importance of refining their interaction and addressing limitations to fully harness the potential of AI-powered problem-solving.
The paper delves into recent advancements in facial emotion recognition (FER) through neural networks, highlighting the prominence of convolutional neural networks (CNNs), and addressing challenges like authenticity and diversity in datasets, with a focus on integrating emotional intelligence into AI systems for improved human interaction.
This article introduces cutting-edge deep learning techniques as a solution to combat evolving web-based attacks in the context of Industry 5.0. By merging human expertise and advanced models, the study proposes a comprehensive approach to fortify cybersecurity, ensuring a safer and more resilient future for transformative technologies.
The research paper delves into the future of leadership, discussing the potential for AI to assist and even substitute human leaders. It explores the effectiveness of AI in addressing employees' psychological needs and highlights the importance of understanding the ethical implications and the evolving roles of human leaders in this digital landscape.
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