A CONCEPTUAL FRAMEWORK FOR INCLUSIVE LEARNING IN CIRCUIT THEORY I USING AI AND DIGITAL SIMULATIONS

Abstract

This study proposes a conceptual framework for inclusive learning in Chapter 2 of Circuit Theory I; Basic Laws. As the foundation for advanced topics and subsequent courses such as Circuit Theory II, mastery of the concepts in this chapter is crucial for student progression. The framework redesigns traditional teaching by incorporating artificial intelligence (AI), digital simulations, and a flipped learning approach to address diverse learning challenges and improve conceptual understanding. The methodology combines simulation platforms (Multisim or LTSpice) with Snorkl AI applications to create an adaptive, feedback-driven learning environment. Students are introduced to key theories through pre-class materials and guided modules, followed by handson live demonstrations using both real circuits and software simulations. Snorkl AI provides realtime feedback, adaptive questioning, and personalized recommendations, ensuring that learners with varying abilities can progress at their own pace. This integration of flipped learning, digital simulations and AI-supported feedback aims to bridge the gap between theory and practice, while maintaining inclusivity in the classroom. Although this framework remains a proposed concept, its anticipated outcomes include improved comprehension of fundamental circuit laws, enhanced student engagement, and better preparedness for more advanced circuit analysis. In conclusion, this conceptual framework highlights how the strategic combination can transform the teaching of basic circuit theory into a more inclusive, engaging, and effective experience.