Collaborating Generative AI with Federated Learning to Enhance Outcome Based Education System

Outcome-Based Education is a student-centered educational paradigm that emphasizes students' overall development. The goal of outcome-based education (OBE) is to prepare students for life, not just for college or the workforce. It is based on concepts: a) clarity of focus (the desired objective is met by the curriculum design, instructional delivery, and evaluation). b) More opportunities (the methods and multiple opportunities for learners to demonstrate their ability) c) High standards (all learners must achieve important goals of education) d) design down (create the curriculum with the desired results in mind). By personalizing information, automating processes, and offering immediate feedback, generative AI improves learning through Outcome-Based Education (OBE), which in turn increases student’s engagement and results. The creation of precise, quantifiable learning outcomes and course objectives that are in line with students' fundamental abilities in various facets of their personal and professional lives is made easier by this data-driven approach. AI helps create a comprehensive educational framework that integrates information, skills, and values into the curriculum design process, guaranteeing that students acquire not only academic understandings but also practical expertise and ethical awareness. By facilitating data analysis and model training while preserving privacy and decentralization, federated learning presents a promising way to improve outcome-based education (OBE). This paper discusses the collaboration of Federated learning and Generative AI to design a frame work based on homogenous group of students with action Plan- Do- Check- Act and heterogeneous group of learners based on Students – Division- Teams- Achievement.