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.
This research investigates the determinants of earthquake insurance uptake in Oklahoma post-2011 seismic events. Through supervised machine learning, it identifies influential factors including age, gender, ethnicity, political affiliation, tenure, housing status, education, income, earthquake experience, and environmental attitudes. The study emphasizes the significance of awareness and advanced machine learning tools for predictive modeling in managing environmental risks and advocates for informed disaster management strategies.
Despite prior positive notions, this study on augmented reality (AR) in a Chinese vocational college setting challenges its efficacy. In a three-stage experiment on architectural education, AR did not significantly improve academic performance, revealing diverse impacts across genders and grades. The study emphasizes the nuanced relationship between AR and learning outcomes, urging a cautious approach and tailored educational strategies based on individual student characteristics.
This research delves into the synergy of Artificial Intelligence (AI) and Internet of Things (IoT) security. The study evaluates and compares various AI algorithms, including machine learning (ML) and deep learning (DL), for classifying and detecting IoT attacks. It introduces a novel taxonomy of AI methodologies for IoT security and identifies LSTM as the top-performing algorithm, emphasizing its potential applications in diverse fields.
This study delves into the influence of exposure to social bots on individuals' perceptions and policy preferences regarding these automated accounts on popular platforms like Twitter, Facebook, and Instagram. The research reveals that even minimal exposure distorts perceptions of bot prevalence and self-efficacy, triggering reactive policy sentiments among social media users.
In this study, the authors delve into psychological factors influencing consumer attitudes toward AI systems. They categorize barriers into opacity, emotionlessness, rigidity, autonomy, and non-humanness, examining how each relates to human cognition. The study emphasizes the need for targeted, context-specific interventions based on system capabilities and user inclinations, providing insights for improving acceptance and mitigating risks in the adoption of AI technologies.
The AI-driven transformation of the labor market demands a nuanced approach to skill measurement. Researchers, in a recent AISeL publication, illuminate the dynamic nature of AI skills and propose a novel dynamic co-occurrence method, addressing limitations in static approaches. Leveraging empirical analysis, they reveal the evolving landscape of AI skills and demonstrate the superior performance of the dynamic method in capturing emergence and evolution.
Researchers introduce a pioneering framework leveraging IoT and wearable technology to enhance the adaptability of AR glasses in the aviation industry. The multi-modal data processing system, employing kernel theory-based design and machine learning, classifies performance, offering a dynamic and adaptive approach for tailored AR information provision.
This study investigates the widespread usage of AI tools, particularly ChatGPT and GPT-4, among German students across disciplines. With nearly two-thirds of students utilizing these tools, the research emphasizes the need for further exploration into usage patterns, perceptions, and potential implications for teaching and learning.
Researchers introduce LLaVA-Interactive, an innovative multimodal human and artificial intelligence (AI) interaction prototype. This system enables users to engage in multi-turn dialogues and interact through visual and language prompts, offering diverse applications, from content creation to culinary guidance, and inspiring future research in multimodal interactive systems.
This paper presents a comprehensive survey of large language model (LLM) evaluation across various dimensions, including knowledge, reasoning, alignment, safety, and specialized domains. It covers a wide range of evaluation benchmarks and methodologies to assess the capabilities, ethical considerations, robustness, and application-specific performance of LLMs, aiming to guide the development of LLMs that are beneficial and trustworthy.
This study delves into customer preferences for automated parcel delivery modes, including autonomous vehicles, drones, sidewalk robots, and bipedal robots, in the context of last-mile logistics. Using an Integrated Nested Choice and Correlated Latent Variable model, the research reveals that cost and time performance significantly influence the acceptability of technology, with a growing willingness to explore novel delivery automation when cost and time align.
This article explores the challenges and approaches to imparting human values and ethical decision-making in AI systems, with a focus on large language models like ChatGPT. It discusses techniques such as supervised fine-tuning, auxiliary models, and reinforcement learning from human feedback to imbue AI systems with desired moral stances, emphasizing the need for interdisciplinary perspectives from fields like cognitive science to align AI with human ethics.
This research presents a novel machine learning approach to evaluate the effectiveness of educational systems in different regions of Brazil using Large-Scale Education Assessment data. The study reveals disparities in educational outcomes across regions and provides insights into the effectiveness of policies in different areas, offering a more flexible and precise evaluation framework compared to traditional methods.
This article delves into the importance of embracing open-source principles in AI technology. It emphasizes the advantages of open-source models in fostering collaboration, accessibility, and accountability while addressing the challenges posed by proprietary AI technology. The author advocates for a responsible and inclusive approach to open-source AI to promote innovation and societal welfare.
Researchers introduce a Convolutional Neural Network (CNN) model for system debugging, enabling teaching robots to assess students' visual and movement performance while playing keyboard instruments. The study highlights the importance of addressing deficiencies in keyboard instrument education and the potential of teaching robots, driven by deep learning, to enhance music learning and pedagogy.
This research presents an innovative method called TF2 for generating synchronized talking face videos driven by speech audio. The system utilizes generative adversarial networks (GANs) and a Multi-level Wavelet Transform (MWT) to transform speech audio into different frequency domains, improving the realism of the generated video frames.
This study explores the development and usability of the AIIS (Artificial Intelligence, Innovation, and Society) collaborative learning interface, a metaverse-based educational platform designed for undergraduate students. The research demonstrates the potential of immersive technology in education and offers insights and recommendations for enhancing metaverse-based learning systems.
This paper explores the potential of metaverse technology, including augmented reality (AR), virtual reality (VR), and mixed reality (MR), in the field of plant science. It discusses how extended reality (XR) technologies can transform learning, research, and collaboration in plant science while addressing the challenges and hurdles in adopting these innovative approaches.
In a groundbreaking study, AI-driven data analysis accurately predicts Greco-Roman wrestlers' competitive success, with just an 11% error rate. This research has the potential to revolutionize athlete selection and training in various sports, offering valuable insights for coaches and athletes alike.
In a groundbreaking study, researchers delve into the intricate web of psychological reactions people have towards robots. This comprehensive research effort introduces the Positive-Negative-Competence (PNC) model, categorizing diverse psychological processes into three dimensions.
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