Enhancing Project-Based Learning With a GenAI Tool Based on Retrieval
Bibliographic record
Abstract
This chapter presents a novel GenAI tool, called PBL Support Chatbot, designed to support project-based learning (PBL) by integrating retrieval-augmented generation (RAG) and knowledge graphs (KGs). The aim is to address common challenges in PBL, such as project complexity and curriculum alignment. The tool provides students with real-time, adaptive support through a chatbot that assists in navigating PBL tasks. To illustrate its application, the authors introduce a scenario involving an introductory computer programming course where students develop a text-based adventure game. By offering personalized guidance, immediate feedback, and accurate answers to student inquiries, the tool aims to enhance critical thinking, learning outcomes, knowledge application, and student engagement in PBL environments. The initial prototype demonstrates the potential to improve the PBL learning experience. This underscores the capability of using RAG to create a dynamic, interactive learning environment that aligns with students' individual learning paths and educational goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".