Learning analytics and Generative AI to design and facilitate learning

forthcoming

Recent advancements in AI, specifically Generative AI (GenAI), have started to significantly impact how teaching and learning occur and how the respective institutions operate. GenAI will have to function in learning spaces and contexts alongside other activities. Therefore, it is expected that instruction and GenAI will complement each other and stand by each other. Yet, there is a limited understanding of how teachers/learners and AI interact, understand, respect, and learn from each other. The purpose of LEARNGENAI is to examine how GenAI is used and under which circumstances it has the potential to improve teaching and learning. The aim is to identify the critical success factors in a range of contexts, and use these findings to promote research and practice. Following an interdisciplinary approach that bridges the domains of computer science, pedagogy and educational sciences, teacher education, learning analytics, and learning theory, along with recent advances in technological tools and AI-based methods, the present project will explore how GenAI is effectively leveraged in education and attempt to isolate key success factors. Therefore, the main focus of the project will be to identify various strategies for implementing GenAI in learning and the success factors to support hybrid learning, understand how information sources, technological infrastructure and human skills and knowledge are ethically coalesced to achieve desired ends, and develop GenAI prototype applications and assess their impact in learning. By empirically investigating these issues and developing a framework of best practices, the ultimate goal is to guide educational institutions in their transition into the artificial intelligence era and enable them to enhance student experiences through personalized learning opportunities.