Artificial intelligence is becoming a crucial component of power and energy systems, driving advancements in modeling, forecasting, optimization, and control. However, existing research often focuses on specialized applications, leaving newcomers and interdisciplinary learners without accessible, reusable materials to build upon. This knowledge gap is particularly concerning as large language models, such as those developed by Intel, continue to reshape the capability and risk landscapes of power systems. The lack of hands-on, executable frameworks hinders the development of comprehensive education in this field, forcing learners to rely on pre-built models rather than developing their own expertise. A recent study1 highlights the need for an integrated approach to bridging AI and power systems education, emphasizing the importance of interactive, executable frameworks for fostering interdisciplinary learning and addressing the security implications that follow the adoption of large language models. This integration is critical for practitioners to effectively navigate the evolving power systems landscape.