Generative AI is emerging as a major force in electronic design automation, with the potential to reshape not only how hardware is designed and verified, but also how these skills are taught and transferred to future generations of engineers. In digital design, large language models and related techniques are being explored for tasks such as requirements interpretation, RTL generation, code completion, testbench creation, debugging support, documentation, and design-space exploration. Although many of these capabilities are still evolving, their direction is already clear: generative AI is becoming an increasingly relevant layer in modern design workflows that can augment productivity, accelerate iteration, and make advanced design activities more accessible to less experienced users. At the same time, the growth of open-source EDA ecosystems is creating a strong foundation for experimentation, accessibility, and reproducibility. Open-source toolchains, libraries, educational infrastructures, and cloud-accessible environments enable a broader community to engage with realistic chip-design workflows without the barriers traditionally associated with commercial tools. This convergence offers a practical path toward democratizing advanced design capabilities: AI can lower the cognitive barrier to entry, while open infrastructures can reduce the operational barrier. This special session focuses on that convergence. Its central premise is that generative AI and open-source EDA should be considered together rather than as separate trends. Generative AI becomes more impactful when embedded in runnable, transparent, and reusable workflows; open-source EDA becomes more powerful when paired with intelligent assistants that help users navigate complexity, generate artifacts, explain tool outputs, and connect design intent to implementation and verification. The combination affects how quickly designers can prototype ideas, how effectively students can learn advanced concepts, how instructors can assemble modern curricula, and how institutions can expand training and talent development in semiconductor design. The session will examine concrete value across the digital design stack while emphasizing that correctness, semantic faithfulness, synthesizability, verification coverage, robustness, traceability, and explainability remain essential. Overall, it aims to provide a forward-looking and balanced perspective on how the EDA community can shape this transformation responsibly and productively.
