Slide 4: FEASIBILITY AND VIABILITY
Demands: ( Theoretical Knowledge )
1. Analysis of the feasibility of the idea
2. Potential challenges and risks
3. Strategies for overcoming these challenges
Feasibility (Doable)
Rule-based AI/ML prototype possible with existing tech stack.
Lightweight front-end PWA for students & admin.
Scalable backend (Python/Django or PHP/Laravel + MySQL).
Works with seeded/mock datasets for demo.
Potential Challenges
Lack of real internship data for training/testing.
Ensuring fairness & diversity in allocation.
Handling large-scale performance (thousands of applicants).
User adoption in rural/low digital literacy areas.
Strategies to Overcome
Use synthetic/seeded data to simulate allocation during demo.
Implement quota-based fairness rules in matching engine.
Optimize backend with caching & batch allocation.
Design simple UI with icons & local language for rural users.
Interviewer Might Ask (with quick answers)
Q1: Do you think this can work nationwide with millions of applicants?
Answer: Our hackathon prototype focuses on a smaller scale with seeded data. But the
design is scalable — we can later integrate with cloud infra (AWS/GCP) and optimize
allocation algorithms for large datasets.
Q2: How will you ensure fairness in AI?
Answer: We’ll add a fairness layer with quotas for rural/aspirational districts and categories,
similar to seat allocation in exams. This ensures equal opportunity, not just skill-based
matches.
Q3: What if internship data is not available during the hackathon?
Answer: We’ll use simulated datasets with realistic profiles and openings. This will
demonstrate the engine’s functionality even without live data.
Slide 5: IMPACT AND BENEFITS
Demands
1. Potential impact on the target audience
2. Benefits of the environmental, etc.
Potential Impact (Target Audience)
Students: Faster, fairer access to quality internships.
Government: Transparent allocation, reduced complaints, improved credibility.
Industry: Right candidates → better productivity & satisfaction.
Society: Equal opportunities for rural & underrepresented youth.
Benefits
Fairness & Inclusion: Ensures representation for rural, aspirational districts, and diverse
categories.
Efficiency: Reduces manual delays, speeds up allocation process.
Transparency: Automated system builds trust among students & industry.
Scalability: Can handle thousands of applications with minimal human effort.
Long-term Impact: Increases employability, bridges urban-rural skill gap, supports Digital
India & NEP goals.
Possible Interview Questions
Q1: What is the biggest social impact of this solution?
Answer: It democratizes opportunities — rural and underrepresented students get equal
access to quality internships, not just those from metro cities.
Q2: How does this help the government beyond just allocation?
Answer: It reduces administrative burden, improves policy compliance, and provides
analytics on student participation for planning future schemes.
Q3: What makes this impactful for industry?
Answer: Companies receive better-matched interns, reducing training costs and improving
internship outcomes.
Slide 6 RESEARCH AND REFERENCES
Details / Links of the reference and research work
SIH 2025 Idea Presentation - References
1. IGNOU BCA/MCA Books (via eGyankosh)
- MCS-201 (Programming in C and Python) –
https://egyankosh.ac.in/handle/123456789/35964
- MCS-208 (Data Communication & Networking) –
https://egyankosh.ac.in/handle/123456789/35965
- MCS-220 (Artificial Intelligence & Machine Learning) –
https://egyankosh.ac.in/handle/123456789/35966
- BCSL-033 (DBMS Lab) – https://egyankosh.ac.in/handle/123456789/41026
- MCS-224 (Professional Skills & Ethics) – https://egyankosh.ac.in/handle/123456789/80219
2. Government Resources
- NEP 2020 Official Document: https://www.education.gov.in/nep-new
- PM Internship Scheme Portal (AICTE): https://internship.aicte-india.org/
- Digital India Initiative: https://digitalindia.gov.in/
3. Technical Resources
- Scikit-learn Docs: https://scikit-learn.org/
- Flask Framework Docs: https://flask.palletsprojects.com/
- React Docs: https://react.dev/
- MDN Service Workers API:
https://developer.mozilla.org/en-US/docs/Web/API/Service_Worker_API
- AI Fairness 360 Toolkit: https://aif360.mybluemix.net/
4. Case Studies & Examples
- Digilocker (Gov of India): https://www.digilocker.gov.in/
- Internshala (Internship Platform): https://internshala.com/
- MIT Blockcerts (Blockchain Education Certificates): https://www.blockcerts.org/
