Integrative AI Practices Across Disciplines: Innovations in Computer Science, Electrical Engineering, and Economics Education

Abstract

This full innovative practice paper describes an interdisciplinary approach leveraging generative AI tools to enhance teaching and learning across computer science, electrical engineering, and economics education. Driven by rapid AI technology development and recognizing that traditional lectures often fail to address individualized learning gaps, educators from these disciplines implemented innovative AI practices, utilizing key tools like ChatGPT, Perplexity, and Google NotebookLM. These approaches support technical, operational, and strategic learning objectives by tailoring experiences, designing dynamic in-class activities, preparing comprehensive quiz reviews, and generating realistic scenarios that bridge theories with practical applications. In Computer Architecture, for instance, instructors utilized the POSED framework (Plan, Outline, Sources, Evaluation, Draft) to efficiently create and refine teaching materials while rapidly incorporating student feedback. At the same time, Google NotebookLM offered personalized support through lecture summarization and quiz generation. Built on prior research in AI-based tutoring and constructivist pedagogies, this framework extends traditional applications by incorporating agentic AI and personalized learning pathways. To evaluate the effectiveness of the integrated AI technology, a survey-based study revealed those integrations increased student engagement and enhanced comprehension of complex topics. However, the evaluation also indicates varied acceptance levels, with concerns regarding the optimal pacing of AI integration, the necessity for robust ethical oversight, and the ongoing challenge of maintaining a balance between AI support and instructor-led guidance. The findings offer actionable strategies for educators to align AI functionalities with pedagogical goals, providing insights into scalable, interdisciplinary AI-enhanced teaching practices. Future work will examine long-term retention and refined assessment frameworks.

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