Performance Evaluation of Prompt Generation Strategies for AI Agents in Online Programming Education
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".