The teaching of large-scale Electronic Design Automation (EDA) software development faces substantial challenges, including massive codebases, complex architectures, and steep algorithm learning curves, which severely constrain students' engineering practice capabilities. While AI programming assistants such as GitHub Copilot or Cursor offer unprecedented opportunities to transform software engineering education, critical questions remain: How can these tools be rationally integrated into complex software development pedagogy? What are the optimal usage patterns that enhance learning without undermining independent problem-solving capabilities? This study systematically investigates the pedagogical pathways and application boundaries of AI programming assistants in EDA software development education.

Grounded in progressive capability construction and intelligent augmentation enhancement principles, this research designed a three-tiered task system: source code analysis (tracing Yosys synthesis flow), algorithm comprehension (reverse engineering ABC's And-Inverter Graph rewriting algorithms), and functional extension (developing Yosys statistical commands). A controlled experiment with 30 undergraduate students stratified by programming proficiency involved three groups: Git Hub Copilot–assisted, Cursor-assisted (with Claude 3.5), and non-AI-assisted control. The four-week experiment assessed pre-and posttest task completion quality, time efficiency, comprehension depth, and subjective experience via Likert-scale questionnaires. AI interaction frequencies were logged to analyze usage patterns.

AI-assisted groups demonstrated substantial improvements: task quality increased from 16.9% (Copilot) to 21.2% (Cursor), with Cursor showing particular advantages in architecture analysis (20.3%) and algorithm comprehension (20.7%). Time efficiency gains were remarkable—Cursor reduced completion time by 71.0% and Copilot by 47.3%. Post-test scores measuring deep comprehension increased by 8.7 points (Copilot) and 10.8 points (Cursor), representing 1.9× and 2.3× improvements over the control group's 4.6-point gain. Subjective metrics showed enhanced self-efficacy and reduced frustration. Critically, correlation analysis identified an optimal usage range of 18–22 interactions; students exceeding this threshold exhibited declining performance, suggesting that excessive reliance undermines independent development capabilities.

This study establishes an empirically validated pathway integrating AI programming assistants into EDA education through progressive task design, moderate AI assistance, and process-based monitoring. The findings reposition AI tools as cognitive scaffolds rather than knowledge providers, offering actionable insights for complex software engineering pedagogy and providing empirical evidence for AI integration in computing education.

The proliferation of specialized demonstrative software colleges has highlighted the growing importance of focusing on the development of large-scale industrial software, particularly in the domain of electronic design automation (EDA) tools. EDA tools are pivotal in chip design, with logic synthesis tools being a critical component within this ecosystem. Despite the increasing emphasis on EDA tools in educational curricula, current courses tend to concentrate on the application of these tools rather than delving into the practical development of logic synthesis tools. This education gap can hinder the cultivation of a new generation of engineers equipped with the skills necessary to innovate in this field. To address this issue, this study aims to bridge the educational divide by introducing a scaffolding-based teaching approach that encompasses the design and implementation of a logic synthesis tool development framework, LoSTDeF, complete with accompanying experimental content and datasets. The aim is to provide students with a deep understanding of hardware design optimization processes and to enhance their practical capabilities in the field of EDA tool development.

The methodology employed in this study involved the design and implementation of the LoSTDeF framework, which was grounded in scaffolding-based teaching principles. This approach was designed to gradually guide students through the complexities of logic synthesis tool development. The framework included a comprehensive set of experimental content and datasets that were carefully curated to facilitate a progressive learning experience. The experimental content was structured to start with fundamental concepts and gradually increase in complexity, allowing students to build a solid foundation before tackling more advanced topics. The datasets provided were real-world scenarios that students must apply their knowledge to, ensuring that the learning was relevant and applicable to industry standards. The teaching approach also involved interactive sessions, where students can engage with the material, ask questions, and receive immediate feedback, fostering a dynamic learning environment.

The innovative results of this study were manifold. First, the LoSTDeF framework was successfully implemented, providing a robust platform for students to learn and practice logic synthesis tool development. The framework’s effectiveness was evidenced by the enhanced understanding of logic synthesis-related knowledge and algorithms demonstrated by the students. Second, the practical skills of the students were significantly improved as they were able to apply theoretical concepts to real-world problems through the use of experimental content and datasets. The teaching practice showed that students not only gained a deeper comprehension of the subject matter but also became proficient in the practical aspects of EDA tool development. This was further validated by the positive feedback from students and the observable improvement in their ability to tackle complex design challenges.

In conclusion, the study successfully demonstrated the importance and necessity of integrating practical development experiences into the education of EDA tools, particularly in the realm of logic synthesis. The LoSTDeF framework, developed as part of this research, proved to be an effective tool for enhancing students’ understanding and practical skills in this area. The results indicated that a scaffolding-based teaching approach, combined with a well-designed experimental curriculum, significantly improved educational outcomes. This study contributes to the field of engineering education by providing a model for developing practical, industry-relevant curricula that prepare students for the challenges of modern chip design and EDA tool development. The success of LoSTDeF underscores the potential for similar frameworks to be applied in other areas of engineering, offering a scalable solution for enhancing educational experiences and outcomes.