Project Overview
Problem: Generic career guidance fails to identify individual aptitudes and interests.
AI Component: ML models analyze psychometric test results, behavioural signals, and academic background to generate personalized career paths.
Solution: Interactive web app that administers psychometric tests, scores responses, maps traits to career clusters, and recommends learning steps and job roles with confidence scores.
Impact: Improves student career outcomes, reduces mismatch, and democratizes counseling at scale.
Key Features
- Adaptive psychometric questionnaire (fewer questions for confident profiles)
- Trait scoring: cognitive, values, interests, personality, skills
- Career clustering & similarity mapping (industry roles, education paths)
- Actionable plan: courses, internships, soft-skill recommendations
- Admin dashboard: cohort analytics, fairness & bias checks
Privacy: PII encryption, options to opt-out of data sharing, exportable reports for students/parents.
Architecture (high level)
- Frontend: React/Next.js — questionnaires, reports, dashboards
- Backend: Python Flask / Node.js — scoring, recommendation API
- ML: scikit-learn / XGBoost / simple neural nets for scoring & ranking
- DB: PostgreSQL for profiles, Redis for session/caching
- Monitoring: analytics + explainability (SHAP or feature importance)
Explainability: each recommendation includes top contributing traits and suggested evidence (test answers/behaviours).
Sample Dataset — Candidate Profiles
| UserID | Name | Age | Education | Location | LastTest |
|---|---|---|---|---|---|
| U1001 | Aisha R. | 17 | 12th (Science) | Mumbai | 2025-10-05 |
| U1002 | Rahul K. | 20 | B.Com Year 2 | Lucknow | 2025-11-02 |
| U1003 | Meera S. | 22 | BA (English) | Bengaluru | 2025-11-10 |
| U1004 | Vikram P. | 25 | B.Tech (ECE) | Chennai | 2025-11-15 |
Sample Dataset — Psychometric Scores (per user)
| UserID | Cognitive (0-100) | Interests (STEM/ARTS/CS/etc) | Personality (OCEAN) | WorkStyle | Assessment Confidence |
|---|---|---|---|---|---|
| U1001 | 78 | STEM:85 / Analytics:72 | O:62 C:80 E:54 A:70 N:30 | Independent, Analytical | 0.92 |
| U1002 | 64 | Commerce:78 / Management:66 | O:55 C:60 E:48 A:56 N:40 | Structured, Detail-oriented | 0.85 |
| U1003 | 70 | Arts:82 / Communication:75 | O:74 C:58 E:70 A:68 N:35 | Collaborative, Creative | 0.90 |
| U1004 | 88 | Engineering:91 / Embedded Systems:80 | O:60 C:88 E:50 A:72 N:25 | Focused, Problem-solver | 0.95 |
Interpretation: O=Openness, C=Conscientiousness, E=Extraversion, A=Agreeableness, N=Neuroticism.
Sample Dataset — Career Recommendations
| UserID | Top Career Matches | Confidence | Suggested Next Steps |
|---|---|---|---|
| U1001 | Data Scientist; Quantitative Analyst; AI Research Intern | 0.88 | Enroll in Python for Data Science, Statistics basics, Kaggle projects |
| U1002 | Financial Analyst; Business Analyst; Accounts Manager | 0.79 | Take Excel + Financial Modelling course, internship at finance firm |
| U1003 | Content Writer; UX Writer; Communications Executive | 0.84 | Portfolio of writing samples, short UX writing course |
| U1004 | Embedded Systems Engineer; IoT Developer; Firmware Engineer | 0.93 | Hands-on projects with microcontrollers, RTOS basics, GitHub portfolio |
Each recommendation includes an evidence summary (top trait contributors) and links to learning resources.
Sample Dataset — ML Logs & Explainability
| LogID | Model | Action | Time | KeyFactors |
|---|---|---|---|---|
| ML-501 | XGBoost-v1 | Recommend careers for U1001 | 2025-11-15 10:12 | Cognitive=78, STEM interest=85, Conscientiousness=80 |
| ML-502 | Clustering-v2 | Update career clusters | 2025-11-14 03:09 | Cluster: Data & Analytics; Cluster size: 4200 |
| ML-503 | FairnessCheck-v1 | Bias scan cohort B | 2025-11-13 22:01 | Flag: slight skew by region — requires sample balancing |
| ML-504 | Recommender-v3 | Generate learning plan U1004 | 2025-11-16 08:47 | Skill gaps: Embedded C, RTOS |
Explainability outputs (feature importance / SHAP summaries) are stored per recommendation for auditability.
Operational Notes & Ethics
- Consent-first onboarding: explicit consent and option to delete data.
- Fairness & bias tests: implement per-cohort validations and corrective re-sampling.
- Human-in-the-loop: counselors can review & override recommendations.
- Localized guidance: language support and region-specific career infos.
Regulatory: follow data protection laws (e.g., GDPR-equivalents) and education board guidelines where applicable.