GenAI Enabled Learning Ecosystems (GELEs)

Abstract

The accelerated emergence of Generative Artificial Intelligence (GenAI) heralds a transformative era for organizational learning and development (L&D) functions. Traditional workplace learning methods, often characterized by static, one-size-fits-all approaches, face growing challenges in meeting rapidly evolving workforce skill demands, engagement deficits, and scalability constraints. This doctoral research investigates how organizations can strategically leverage GenAI to transform employee learning into a personalized, scalable, and effective function aligned with the future of work. Grounded in established theoretical frameworks including the Technology Acceptance Model (TAM), Theory of Reasoned Action (TRA), Sociocultural Learning Theory, and Human Society Theory, this study provides a multidisciplinary lens to understand GenAI’s integration into L&D ecosystems.
Through a mixed-methods research design, comprising quantitative surveys of over 100 senior L&D professionals and qualitative interviews with Chief Learning Officers (CLOs), complemented by a systematic review of current industry whitepapers and reports, the study reveals a landscape of growing experimentation with GenAI tools. Predominantly employed in content creation activities, advanced applications such as virtual coaching, learning analytics, and personalized learning pathways are emerging but remain underutilized in many organizations. Key barriers impeding widespread adoption include skill gaps in GenAI literacy, ethical and privacy concerns, leadership inertia, unclear strategic direction, and resource constraints. These barriers reflect the complex interplay of technical, social, and organizational factors shaping technology acceptance and sustained usage.
To address these challenges and harness GenAI’s enabling capabilities, this research introduces the GenAI-Enabled Learning Ecosystem (GELES) framework—a comprehensive, end-to-end model integrating strategy, people, technology, governance, and measurement within the ADDIE instructional design paradigm. The framework operationalizes GenAI as a set of reusable services embedded across analysis, design, development, implementation, and evaluation phases, emphasizing human-in-the-loop checkpoints, ethical governance, continuous capability building, and strategic alignment with organizational goals. Emerging L&D roles—including AI Learning Strategists, Content Curators, Prompt Engineers, and Data Analysts—are delineated, highlighting a necessary transition from traditional content producers to strategic orchestrators and AI-augmented facilitators.
Keywords: Generative Artificial Intelligence, organizational learning, Learning and Development, technology adoption, workforce transformation, AI ethics, blended learning, GenAI-enabled learning ecosystem