Work-in-Progress: Integrating Generative AI with Evidence-based Learning Strategies in Computer Science and Engineering Education

Abstract

Generative AI assistants are AI-powered applications that can provide personalized responses to user queries or prompts. A variety of AI assistants have recently been released, and among the most popular is OpenAI's ChatGPT. In this work-in-progress in innovative practice, we explore evidence-based learning strategies and the integration of Generative AI for computer science and engineering education. We expect this research will lead to innovative pedagogical approaches to enhance undergraduate computer science and engineering education. In particular, we describe how ChatGPT was used in two computing-based courses: a Junior-level course in database systems and a Senior-level class in mobile application development. We identify four evidence-based learning strategies: well-defined learning goals, authentic learning experiences, structured learning progression, and strategic assessment. We align these strategies with the two aforementioned courses and evaluate the usefulness of ChatGPT specifically in achieving the learning goals. Combining Generative AI with evidence-based learning has the potential to transform modern education into a more personalized learning experience.

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