Amid the growing significance of artificial intelligence (AI) in education and its increasing societal and economic impact on the contemporary workforce, this book examines the delivery of an AI additional qualification implemented through an adaptive learning system designed to support the acquisition of AI-related knowledge and skills among learners in vocational education and training (VET) and continuing vocational education and training (CVET). At the core of the book lies the investigation of key learner characteristics and system features that contribute to enhancing AI learning outcomes. Drawing on a quasi-experimental research design, data from 222 participants were gathered over two school years. Self-reported questionnaires administered in standardized online surveys captured learners' perceptions throughout the AI course. Statistical analyses in R provide empirical support for the effectiveness of learners' prior AI knowledge and the system's individualized and adaptive features in improving AI learning outcomes. Nevertheless, the study reveals that learners' experience with digital learning applications has no significant influence. In addition, the analysis of AI learning outcomes across learning style groups yields no conclusive support on the effect of learning preferences on cognitive achievements within adaptive learning environments. The findings provide implications for researchers, stakeholders in VET and CVET, and developers of adaptive learning systems.

Autorentext

Dr. Efthymia Petridou is a Postdoctoral Research Associate at the Faculty of Psychology and Educational Sciences at Ludwig Maximilians University of Munich, Germany. Her research focuses on the integration of artificial intelligence in higher education as well as in continuing and vocational education and training. She is particularly interested in investigating individualized learning support and examining learning processes and outcomes within personalized and adaptive learning environments.