WIP: Active Learning Through Prompt Engineering and Agentic AI Simulation-A Pilot Project in Computer Networks Education
Xiaoguang Ma, Jing Wang
Abstract
This work-in-progress paper introduces the AIca-demic system, an innovative framework employing Agentic AI and Agile methodologies to enhance learning in complex domains such as computer networks. Positioned within the topic of AI and Machine Learning Tools to Enhance Instruction, it aims to revolutionize the learning experience and outcomes for the intricate subject matter. This system emphasizes active, adaptive learning experiences through AI generated or AI improved educational materials with multiple iterations of feedback and improvement cycles. Utilizing fine tuned large language models (LLM), the AIcademic system assembles an interactive AI team, e.g. AIcademic Professor, Student, and Instructional Designer. Each AI agent is uniquely configured with our POISE prompt engineering model to analyze and simulate real-time classroom interactions from multiple viewpoints. Active learning pedagogy is embedded into the system through prompt engineering during the creation of each agent. Agile methodology is employed to organize collaborations of the AI agents for complex task planning and implementation, feedback integration, output con-tinuous improvement, and agent self-enhancement. A suite of AI tools is explored to dynamically create tailored educational materials aligned with the educator's teaching preferences and students' needs. Preliminary results from a pilot implementation of teaching the transport layer in computer networks demon-strated improvements in student engagement and comprehension over previous materials. This AIcademic framework presents a promising and scalable paradigm for AI applications in educational environments. While still under development, this research aims to refine and expand these findings, exploring the full potential of integrating Prompt Engineering and Agentic AI for creating active learning environments across complex technical subjects. The implications extend beyond computer network education, offering a blueprint to redefine teaching and learning in a technology-enhanced era. We invite collaboration from the broader academic community to refine the Agent prompt design, automate AI to AI interactions, assess long term impacts, and explore further applications.