JEE 360 - Empowering JEE Aspirants Through AI-Driven Personalized Learning and Instant Doubt Resolution
Bibliographic record
Abstract
JEE360 is one of the most advanced AI virtual assistants designed for students preparing for the JEE and other competitive engineering examinations in India. The system is trained on advanced Large Language Model (LLM for short) and subsequently customized using key academic materials like NCERT textbooks, JEE guidebooks, and previous examination papers. JEE360 instantly resolves and summarizes complex concepts, and other topic specific questions and provides topicwise personalized context-specific quizzes. The system synthesizes RAG (retrieval-augmented generation) and CAG (cacheaugmented generation) methods to dynamically obtain pertinent information and to efficiently recycle “high-confidence” answers. The JEE360 also uses the LangChain architecture to manage the orchestrated reasoning of document loaders, embeddings, and pipelines, so that the processes of data retrieval and answer generation run in a coordinated and seamless manner. More than all of these more complex features, JEE360 is capable of offering streamlined support to students of all levels and backgrounds in order to provide quality JEE preparation and adaptability to a variety of learners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".