Abstract
A leading university successfully transitioned to an AI-powered interviewing system in response to the growing operational complexities of managing large-scale admission interviews.
This case study explores how the institution streamlined the assessment of over 13,000 applicants using intelligent adaptive AI interviews.
The transition reduced time and saved cost enhancing fairness, personalization and decision accuracy. It serves as a blueprint for academic institutions aiming to adopt scalable and unbiased admission practices in a multilingual world.
Background
Admission interviews are a cornerstone of candidate selection in higher education, essential for evaluating communication skills, motivation, and program fit. Traditionally, these interviews were conducted in a manual, face-to-face format involving multiple interviewers, scheduling logistics, and weeks of processing.
For an institution with thousands of applicants, this model became increasingly untenable—logistically complex, resource-intensive, and prone to human bias.
The Challenge
Volume: Over 13,000 applicants needed to be interviewed within a tight timeframe.
Resources: Mobilizing and coordinating hundreds of expert interviewers across departments and locations.
Cost: High financial and operational overheads.
Bias: Inconsistent evaluations and interviewer subjectivity.
Candidate Accessibility: Requiring travel and rigid scheduling posed equity and inclusion challenges.
The Transformation: AI-Powered Interview Solution
To address these challenges, the university deployed an AI-driven interview platform that emulated human-like interactions, personalized to each candidate’s background, experience and responses.
Key Features
Profile-Based Personalization: The AI reviewed each candidate’s profile, including work experience, academic background and statement of purpose.
Dynamic Questioning: Follow-up questions were generated based on candidate responses, mimicking natural interview flow.
Real-Time Evaluation: The system assessed competencies like communication skills, personal credibility, motivation, awareness of current trends and critical thinking.
Language Flexibility: The AI could conduct interviews in multiple regional languages (e.g. Hindi, Marathi, Tamil, English, German, Spanish) increasing accessibility.
Documentation and Replay: Every interview interaction was recorded and transcribed for transparency and further review.
Further readings
Impact
| Metric | Before AI Integration | After AI Integration |
|---|---|---|
| Candidates Interviewed | ~6,000 in 3 weeks | 13,000+ in 2 weeks |
| Interview Panel Size | 200+ faculty members | ~30 reviewers |
| Average Cost per Interview | High (travel, coordination) | Significantly Reduced |
| Bias & Subjectivity | High variation | Minimized |
| Candidate Experience | Stressful, time-bound | Flexible, language-inclusive |
See how AI can save costs compared to traditional manual interviews as the number of interviews increases.
AI Interviews: ₹100
Manual Interviews: ₹300
Cost Saving: ₹200
Time Saved: 0 hrs
Role of Subject Matter Experts
Post-interview, faculty and admission reviewers received detailed analytics generated by the AI system, including:
- Skill-based ratings.
- Communication proficiency.
- Motivation and alignment with the program This empowered them to focus on decision-making rather than conducting interviews, allowing better utilization of expert time and judgment.
AI Admission Interview Benefits Snapshot
Preparation: AI was configured with specific rubrics, scoring logic and contextual scenarios.
Discernment: Structured data allowed reviewers to focus on decision-critical insights.
Bias Recognition: Eliminated through standardized AI evaluation protocols.
Judgment: Faculty used AI-generated assessments to make final, high-stakes decisions.
Collaboration: IT, academic departments and AI developers worked in tandem.
Curiosity: Institutional leaders were open to exploring innovative methods.
Self-Confidence: The institution’s successful adoption of cutting-edge tools reinforced internal belief in its innovation capacity.
This real-world case provides an explanatory and descriptive exploration of how automation can impact institutional processes. While this case is unique in scale, its core lessons are analytically generalizable to institutions managing growing applicant pools under resource constraints.
What They Say About Us…
Eklavvya’s AI-powered interview platform has been highly effective for our MBA admissions at Narsee Monjee Institute of Management Studies.
With 13,000+ interviews conducted across multiple panels, Eklavvya offered great flexibility, seamless integration with our admission portal, and reliable, unbiased AI evaluations.
The quality of AI-generated questions was excellent, and we received positive feedback from students, faculty, and invigilators. The Eklavvya team delivered professional and commendable work.
– Dr. Sharad Mhaiskar,
(Pro Vice Chancellor),
SVKM’s Narsee Monjee Institute of Management Studies
Recommendations
Institutions aiming to modernize their admission pipeline should consider AI-driven solutions like Eklavvya AI Admission Interview System, which offers:
- Integrations with existing LMS or ERP systems
- Customizable criteria
- Multilingual support
- Scalable deployment
Explore Deep Dives
- Conduct interviews virtually at your convenience.
- Assess multiple skills with detailed feedback.
- Eliminate bias and errors in assessing candidates.
- Record responses and evaluate them later.
Conclusion
The AI-powered admission interview process marks a significant shift in how educational institutions can evaluate applicants fairly, efficiently and on a scale.
By integrating advanced technology with academic integrity, this leading university not only preserved the essence of personal interviews but also elevated them with structure, reach and objectivity.
