As AI systems increasingly impact public life, there is a growing demand for Responsible AI (RAI) education that is technically grounded, ethically informed, and accessible to learners from diverse academic backgrounds. This paper presents an interdisciplinary curricular model that integrates machine learning (ML) foundations, explainable AI (XAI) tooling, and civic relevance within a unified pedagogical framework. Unlike conventional CS-centric offerings, our course was designed for students across computer science, humanities, education, and management disciplines. The curriculum introduced tools such as SHAP and LIME alongside ethical reflection exercises, fairness audits, and collaborative capstone projects rooted in real world datasets. A mixed methods evaluation including pre- and post-course surveys, student reflections, and project analyses—demonstrates significant gains in ML interpretability, ethical reasoning, and cross disciplinary engagement. We argue that embedding RAI principles across technical workflows rather than as standalone ethics modules enables deeper learning and transferable insight. This study indicates promising gains in both technical and ethical domains; however, these findings should be interpreted as indicative rather than definitive until further statistical validation and broader replication are achieved.