Ida Skubis – författare

Human–AI Collaboration in Research: Practical Applications, Ethical Frameworks, and Future Directions positions human–AI collaboration (HAIC) as a defining feature of contemporary research ecosystems. The book examines the incorporation of AI across the research lifecycle, including research design, literature work, data collection and processing, analysis, interpretation, academic writing, and dissemination. It highlights the opportunities created by automation and generative systems, alongside the challenges raised for research integrity, accountability, transparency, privacy, and epistemic authority. Ethical and regulatory foundations are addressed through established frameworks such as the Belmont Report and the Declaration of Helsinki, as well as European governance instruments including the Ethics Guidelines for Trustworthy AI, the General Data Protection Regulation, and the EU Artificial Intelligence Act. By combining interdisciplinary perspectives from robotics, management research, and education, the volume translates abstract ethical principles into concrete research-relevant practices, offering a coherent and human-centred approach to trustworthy AI-supported inquiry.

The book offers original, research-ready frameworks and applied guidance for responsible HAIC, combining real-world case studies with practical strategies for trustworthy AI use. It equips researchers, educators, and management scholars with tools for human oversight, transparency, bias mitigation, and accountable AI-supported workflows, ensuring scientific rigour alongside innovation.

Human–AI Collaboration in Research: Practical Applications, Ethical Frameworks, and Future Directions positions human–AI collaboration (HAIC) as a defining feature of contemporary research ecosystems. The book examines the incorporation of AI across the research lifecycle, including research design, literature work, data collection and processing, analysis, interpretation, academic writing, and dissemination. It highlights the opportunities created by automation and generative systems, alongside the challenges raised for research integrity, accountability, transparency, privacy, and epistemic authority. Ethical and regulatory foundations are addressed through established frameworks such as the Belmont Report and the Declaration of Helsinki, as well as European governance instruments including the Ethics Guidelines for Trustworthy AI, the General Data Protection Regulation, and the EU Artificial Intelligence Act. By combining interdisciplinary perspectives from robotics, management research, and education, the volume translates abstract ethical principles into concrete research-relevant practices, offering a coherent and human-centred approach to trustworthy AI-supported inquiry.

The book offers original, research-ready frameworks and applied guidance for responsible HAIC, combining real-world case studies with practical strategies for trustworthy AI use. It equips researchers, educators, and management scholars with tools for human oversight, transparency, bias mitigation, and accountable AI-supported workflows, ensuring scientific rigour alongside innovation.

This interdisciplinary book demonstrates how generative artificial intelligence (GenAI) changes higher education across diverse academic fields, including philology, management, engineering, and psychology. Drawing on empirical case studies, surveys, and experiments, this book combines theoretical and practical perspectives to explore AI’s impact on learning and cognitive development. Each chapter offers discipline-specific analysis of how GenAI reshapes pedagogy – supporting multilingual education, enhancing reflective thinking, fostering self-regulated learning, and encouraging ethical awareness. By comparing case studies from varied academic and cultural contexts, this book highlights the opportunities and challenges of AI adoption, emphasizing responsible, creative, and critically informed approaches to academic innovation. It will appeal to researchers of AI in Higher Education across disciplines. Key features:

- Explains how GenAI transforms higher education across a variety of disciplines, from logistics to psychology.

- Includes practical and theoretical insights into AI’s role in reshaping pedagogy, fostering critical thinking, and supporting multilingual education.

- Provides a global perspective on the opportunities and challenges of AI adoption through diverse case studies and empirical research.

This interdisciplinary book demonstrates how generative artificial intelligence (GenAI) changes higher education across diverse academic fields, including philology, management, engineering, and psychology. Drawing on empirical case studies, surveys, and experiments, this book combines theoretical and practical perspectives to explore AI’s impact on learning and cognitive development. Each chapter offers discipline-specific analysis of how GenAI reshapes pedagogy – supporting multilingual education, enhancing reflective thinking, fostering self-regulated learning, and encouraging ethical awareness. By comparing case studies from varied academic and cultural contexts, this book highlights the opportunities and challenges of AI adoption, emphasizing responsible, creative, and critically informed approaches to academic innovation. It will appeal to researchers of AI in Higher Education across disciplines. Key features:

- Explains how GenAI transforms higher education across a variety of disciplines, from logistics to psychology.

- Includes practical and theoretical insights into AI’s role in reshaping pedagogy, fostering critical thinking, and supporting multilingual education.

- Provides a global perspective on the opportunities and challenges of AI adoption through diverse case studies and empirical research.