AI-Driven Outcome-Based Education for Complex Problem-Solving: Technical Framework and Practical Exploration of Micro-Course Clusters

Abstract

This paper proposes an AI-integrated Outcome-Based Education (OBE) framework that combines backward design with micro-course clusters to enhance complex problem-solving (CPS) competencies in higher education. The technical architecture integrates three core computer technologies: (1) Python-based system dynamics simulation algorithms for mapping interdisciplinary variables; (2) machine learning optimization using Scikit-learn to predict student CPS development trends; and (3) IoT edge computing via Arduino-Raspberry Pi communication for real-time project monitoring. A 18-week empirical study with 180 undergraduates validated the model: AI-driven real-time feedback loops improved CPS competency gains by 42%, with 91% of capstone projects meeting industry deployment standards. This framework bridges academia-industry gaps in AI education through actionable technical design.