Performance Evaluation of Prompt Generation Strategies for AI Agents in Online Programming Education

Abstract

The integration of artificial intelligence agents in online programming education has revolutionized how students receive instructional support and feedback. This research investigates the performance evaluation of different prompt generation strategies employed by AI agents to assist programming learners. The study examines three distinct prompt generation approaches: rule-based progressive prompting, data-driven adaptive prompting, and hybrid context-aware prompting. Through a controlled experimental design involving 180 undergraduate students enrolled in introductory Python programming courses, we evaluated these strategies across multiple performance dimensions including learning effectiveness, engagement metrics, code completion rates, and student satisfaction. Quantitative analysis revealed that the hybrid context-aware prompting strategy achieved superior learning outcomes with normalized gains averaging 0.51 compared to data-driven (0.42) and rule-based approaches (0.35). The evaluation framework incorporated behavioral analytics, cognitive load measurements, and longitudinal performance tracking over an eight-week period. Results demonstrate significant variations in strategy effectiveness based on student proficiency levels, problem complexity, and learning contexts. This research contributes empirical evidence for optimizing AI agent design in educational technology and provides practical guidelines for implementing adaptive prompting mechanisms in programming learning environments.