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Results 331-335 of 335 (Search time: 0.016 seconds).
    • Metamodeling for Extended Reality
    Authors: Muff, Fabian;  Advisor: -;  Co-Author: - (2025)

    This open access book which is based on the author’s dissertation in 2024 explores the challenges of metamodeling in the context of extended reality and emphasizes the need for new concepts in metamodeling to effectively combine it with extended reality technologies. The central question of this work is how metamodeling can be used “in” and “for” extended reality. The book is structured in nine chapters: Chapter 1 introduces the topic by providing background information and outlining the research objectives, questions, methodology and structure. Chapter 2 delves into the existing literature and developments in the field. It covers various aspects of modeling, such as conceptual, enterprise, and metamodeling, as well as extended reality (XR), virtual reality (VR), augmented realit...
    • Machine Learning in Sports
    Authors: Fujii, Keisuke;  Advisor: -;  Co-Author: - (2025)

    This open access book provides cutting-edge work on machine learning in sports analytics, emphasizing the integration of computer vision, data analytics, and machine learning to redefine strategic sports analysis. This book not only covers the essential methodologies of capturing and analyzing real sports data but also pioneers the integration of real-world analytics with digital modeling, advancing the field toward sophisticated digital modeling in sports. Through a seamless blend of theoretical frameworks and practical applications, the book illustrates how these integrated technologies can be utilized to predict, evaluate, and suggest next plays in sports. By leveraging the power of machine learning, the book presents cutting-edge approaches to sports analytics, where data fro...
    • Feature Models: AI-Driven Design, Analysis and Applications
    Authors: Felfernig, Alexander; Falkner, Andreas; Benavides, David;  Advisor: -;  Co-Author: - (2024)

    This open access book provides a basic introduction to feature modelling and analysis as well as to the integration of AI methods with feature modelling. It is intended as an introduction for researchers and practitioners who are new to the field and will also serve as a state-of-the-art reference to this audience. While focusing on the AI perspective, the book covers the topics of feature modelling (including languages and semantics), feature model analysis, and interacting with feature model configurators. These topics are discussed along the AI areas of knowledge representation and reasoning, explainable AI, and machine learning.
    • User Experience + Artificial Intelligence
    Authors: Spallazzo, Davide; Sciannamè, Martina; Ceconello, Mauro;  Advisor: -;  Co-Author: - (2025)

    This open access book addresses the thriving trend of embedding artificial intelligence (AI) and machine learning (ML) capabilities in products and services reaching the lay public, focusing on the user experience (UX) they prompt from a designerly perspective. It offers a UX evaluation method designed explicitly for AI-infused systems to answer one of the core problems affecting the relationship and interactions people have with such artefacts. The work investigates how people perceive and make sense of systems integrating AI capabilities, trying to understand how their meaning and significance can affect the experience of such products and what design challenges may arise. Given the fundamental premise that current UX methods cannot address AI-infused artefacts, it introduces the ...
    • ChatGPT: A reliable assistant for the evaluation of students’ written texts?
    Authors: Atasoy, Arzu; Nezhad Arani, Saieed Moslemi;  Advisor: -;  Co-Author: - (2025)

    There is growing interest in the potential of Artificial Intelligence (AI) to assist in various educational tasks, including writing assessment. However, the comparative efficacy of human and AI-powered systems in this domain remains a subject of ongoing exploration. This study aimed to compare the accuracy of human raters (teachers and pre-service teachers) and AI systems (ChatGPT and trained ChatGPT) in classifying written texts. The study employed both chi-square tests and logistic regression analysis to examine the relationship between rater groups (human vs. machine) and the accuracy of text classification. Initial chi-square analyses suggested no significant differences in classification accuracy between human and AI raters. However, the logistic regression model revealed a si...