A Human-Centered Learning and Teaching Framework Using Generative Artificial Intelligence for Self-Regulated Learning Development in Teaching Digital Electronics Course

Abstract

Generative Artificial Intelligence (AI) has emerged as a transformative force in engineering education, offering rapid content generation, intelligent code assistance, and simulation support. In Digital Electronics courses, AI can accelerate circuit design workflows, enhance self-regulated learning (SRL), and support deeper engagement with domain-specific concepts. This paper presents an engineering-adapted Human-Centered Learning and Teaching Framework (HCLTF) that integrates generative AI into the learning process while preserving analytical rigor and human-centered pedagogical values. The framework aligns with SRL's forethought, performance, and self-reflection phases, embedding AI in logic design, HDL development, simulation, and optimization tasks. A case study is presented in which 187 undergraduate students across three institutions design, simulate, and optimize a 4-bit synchronous up/down counter using a combination of traditional design methods and AI-assisted support. The AI was employed for Boolean simplification verification, Verilog code scaffolding, testbench generation, and timing optimization. AI-assisted workflows reduce HDL development time by 40% compared to traditional methods. Synthesis results reveal measurable differences between manual and AI-generated designs, including a 4.2% lower Fmax, 20% higher LUT usage, and a 9.5% increase in dynamic power prior to student-led optimization. Quantitative and qualitative findings indicate improvements in students’ Boolean reasoning, debugging proficiency, and reflective judgment across SRL phases. The study highlights the pedagogical value of guided GenAI integration and provides a scalable model for embedding AI-supported learning within Digital Electronics and broader engineering curricula.