Application of the Neolearning Methodology in a Networked Corporate University

Angela Maria Fleury de Oliveira, Patrícia de Sá Freire and Rivaldo Arruda

Chapter 12 in AI-Driven Revolution:Transforming the Business Landscape, 2025, pp 261-285 from World Scientific Publishing Co. Pte. Ltd.

Abstract: The COVID-19 pandemic caused a shortage of qualified professionals, affecting companies’ ability to meet their needs. To fill these gaps, many resorted to building Networked Corporate Universities (NCU), understanding that they could accelerate the qualification/requalification processes of their teams. The following research question was raised: “How can the neolearning methodology be effectively applied in a NCU to develop leadership skills and generate value for the organization?” In light of this question, the general objective of this study was to investigate the application of the neolearning methodology in a networked corporate university, focusing on the development of leadership skills and the generation of value for the organization, which were the main findings and implications of the study. Neolearning consists of learning to learn by doing, bringing together the four pillars of UNESCO, the 4 Is, the concepts of experiential learning, active methodologies, and taking care of the relational processes between individual, group, and organization. The knowledge application stage in a program of this NCU took place through Challenge-Based Learning (CBL). The research method used was action research in the Leadership Development Program (LDP) of a NCU — UCR, a pilot project for the application of the neolearning methodology.

Keywords: Artificial Intelligence; Data Analytics; AI; Digital Landscape; Organizational Strategies; AI Technologies; Machine Learning; Natural Language Processing; Robotics; Digital Transformation; Business Models; Efficiency; Value Propositions; Advanced Analytics; Predictive Modelling; Customer Experiences; AI-driven; Ethical AI; Data Privacy; Algorithmic Bias; Regulation Compliance; Responsible AI; Sustainable AI; Practical Applications; Business Innovation; Emerging Technologies; Industry 4.0; High Tech; Ethics Regulation; Business Leadership; Pattern Recognition; Information Technology; Entrepreneurs; Management (search for similar items in EconPapers)
JEL-codes: L1 L2 L21 L26 M1 (search for similar items in EconPapers)
Date: 2025
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