Bibliographic record
Abstract
Education, as a vital institution, continuously benefits modernization, with artificial intelligence (AI) driving a paradigm shift in teaching and learning. This research presents an AI-powered e-learning system that delivers personalized academic notes tailored to students' needs. Utilizing a Large Language Model (LLM) like Gemini, the system comprehends student queries more effectively, enabling customization of content across diverse subjects and academic levels. Prompt engineering ensures that information remains accessible, accurate, suitable for different age groups. Key features of the system include an AI-based chatbot interface developed using HTML, CSS, and JavaScript, providing user-friendly experience. The back-end integrates with the LLM via an API to process student inputs and generate relevant information in real time. Additionally, light and dark modes usability, making the platform adaptable to user preferences. It also outlines the system’s design, data preparation, prompt engineering methods, and integration of the front-end and back-end. It highlights key challenges, such ensuring response clarity, maintaining student engagement, and managing different learning speeds. Initial testing, with student feedback, revealed high response accuracy, usefulness, and user satisfaction, underscoring AI's potential in enhancing learning quality and engagement. With its adaptability to multiple subjects, the system is positioned as a crucial tool in modern education. This project demonstrates how AI can transform established learning paradigms, delivering personalized education and significantly enhancing learning experiences.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".