Generative AI in Research : Applications in Research Design, Data Analysis and Feedback / / by Oluwaseun Kolade, Abiodun Egbetokun, Adebowale Owoseni

• Record Nr.: UNINA9911049085703321
• Autore: Kolade Oluwaseun
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Palgrave Macmillan, , 2025
• ISBN: 3-032-02440-4
• Edizione: [1st ed. 2025.]
• Descrizione fisica: 1 online resource (236 pages)
• Collana: Education Series
• Disciplina: 378.1734631
• Soggetti: Technological innovations; Education - Research; Innovation and Technology Management; Research Methods in Education
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Chapter 1: Gen AI for Research revolution or risk -- Chapter 2: Generative AI Applications in Research Design -- Chapter 3: Gen AI enabled data generation and simulation in Social Sciences -- Chapter 4: Generative AI Use Cases for Data Processing and Analysis -- Chapter 5: Towards Methodological Innovation CoCreating Research Design with Generative AI -- Chapter 6: Reviewing manuscripts for originality significance and rigour -- Chapter 7: Gen AI for public engagement and knowledge exchange -- Chapter 8: The future of AI for knowledge production.
• Sommario/riassunto: The growing popularity of Generative AI has stirred new debates about the future of knowledge production. With a prompt and a click, regular users can now generate contents on just about any topic of interest, drawing from hundreds of billions of parameters with which the latest versions of Gen AI models are trained. As Generative AI rapidly evolves with more advanced features and capabilities, stakeholders have expressed worries that AI models will displace humans as central agents in the research process. This book examines the case for and against applications of Gen AI in research, highlighting the prospects and pitfalls. Using exemplar prompts and custom GPTs created by the ; authors, it explores prospective use cases for automated data processing, complex modelling and simulations; applications in experimental designs; and review of draft manuscripts. The book also engages with key issues around algorithmic bias, inaccuracies, fake information, epistemic injustice, and the ethics of AI applications in research. In some ways a companion piece to the authors' previous title, 'Generative AI in Higher Education: Innovation Strategies for Teaching and Learning', this book has a particularly practical appeal for researchers, as well as university officials and policymakers getting to grips with the explosion of AI-assisted research. It will also be of value to scholars of AI and innovation strategy in higher education. Oluwaseun Kolade is a Full Professor of Entrepreneurship and Digital Transformation at Sheffield Business School, Sheffield Hallam University, UK. He has authored more than 100 academic outputs spanning digital transformation, AI, circular economy and SMEs strategies. Abiodun Egbetokun is Senior Lecturer in Business Management at De Montfort University, Leicester, UK, and a Senior Fellow of the Higher Education Academy (SFHEA). His current research examines the implications of LLMs in research, industry and higher education. Adebowale Owoseni is a Senior Lecturer in Information Systems at De Montfort University, Leicester, UK, and a Senior Fellow of the Higher Education Academy (SFHEA). He transitioned to academia in 2019 after a 13-year career in fintech.