AI-Driven Career Navigation System: Leveraging Adaptive Questioning and Satisfaction Feedback for Personalized Guidance
Bibliographic record
Abstract
Many students find it difficult to choose a suitable career path as there is limited guidance and information about available options to choose as a career. To help solve this issue, this work introduces an AI-driven career navigation system designed to offer personalized guidance through interactive dialogue. The system combines Google Gemini’s generative intelligence with a Streamlit web interface to analyze a student’s interests, academic background, and regional opportunities. It begins by asking structured questions and then adjusts the questions dynamically based on user’s response. A feedback feature allows student to accept or reject the suggestions thereby enabling the model to refine its future recommendations. During evaluation, the system showed greater accuracy and engagement in suggesting suitable career paths compared to traditional methods. By integrating adaptive questioning and satisfaction-based feedback, the proposed model delivers more relevant human-like career advice. This study demonstrates how AI helps in informed and confident career decisions for the students in a very practical and accessible way.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".