Investigating Conversational Agents to Support Secondary School Students Learning CSP

2026-04-17

AI Summary

This study addresses the challenge of improving access to adaptive learning resources for high school students enrolled in Advanced Placement Computer Science Principles (CSP). Through a controlled classroom experiment involving 45 students in grades 9–11, it presents the first systematic comparison between general-purpose generative conversational agents (e.g., ChatGPT) and a fixed-response dialogue agent specifically designed for CSP, evaluating their effectiveness in supporting exploratory learning. The findings reveal distinct strengths and limitations of each system in fostering conceptual understanding and student engagement, offering empirical evidence and theoretical insights to inform the design and deployment of conversational technologies in computing education.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Secondary school students enrolled in the AP Computer Science Principles (CSP) course commonly utilize web resources (e.g., tutorials, Q\&A sites) to better understand key concepts in the curriculum. The primary obstacle to using these resources is finding information appropriate for the learning task and student's background. In addition to web search, conversational agents are increasingly a viable alternative for CSP students. In this paper, we study the potential of conversational agents to aid secondary school students as they acquire knowledge on CSP concepts. We explore general purpose, generative conversational agents (e.g., ChatGPT) and custom, fixed-response conversational agents built specifically to aid CSP students. We present results from classroom use by 45 high school students in grades 9-11 (ages 14-17) across six CSP sections. Our main contributions are in better understanding how conversational agents can help CSP students and an evaluation of the effectiveness and engagement of different approaches for CSP exploratory search.

Problem

• Conversational Agents
• Computer Science Principles
• Secondary Education
• Exploratory Search
• Learning Support

Innovation

• conversational agents
• computer science education
• exploratory search
• generative AI
• secondary school learning