Synthetic Impact Projection Report — Cohort data is based on modelled projections using industry benchmarks from EdTech adoption studies across Indian K-12 schools (2022–2025). School names are illustrative and do not represent actual clients. All financial projections are estimates using representative fee structures. Actual results depend on adoption intensity, school infrastructure, and implementation quality.
SPYRAL AI
an AIRM Initiative
NEP LAB & AI LAB School Impact Report 2026
Synthetic Cohort Study — 50 Schools · Tier 1 & Tier 2 India
Innovation Incubation and Startup Foundation
Kamla Nehru Institute of Technology, Sultanpur UP 228001
tryspyral.com|+91 6387890310|info@tryspyral.com
Executive Summary
Platform Impact at a Glance
About This Study
Methodology & Data Sources
Study Design
This synthetic cohort was modelled on 50 schools across 15 Indian cities — 20 Tier 1 and 30 Tier 2 schools — representing a range of school sizes, boards, and platform adoption durations (6–18 months post-onboarding).
Data parameters were calibrated against: ASER 2023, NCERT EdTech Adoption Reports 2022–24, and NASSCOM EdTech India 2024 Projections. Metrics represent modelled outcomes.
What Was Measured
Admissions conversion
✓
AI LAB quiz scores
✓
Teacher admin hours/week
✓
NEP compliance score
✓
Parent portal adoption
~
Important: Metrics are modelled outcomes — not guaranteed results. Actual performance depends on adoption intensity, teacher training completion, school infrastructure, and implementation quality. Board exam correlation data will be available after the 2024–25 academic year results. Adoption period analysed: 6–18 months post-onboarding.
Cohort Overview
50-School Dataset
Stars (★) indicate the top 5 schools by quiz score improvement. Plan badges:
EnterpriseProBasic
#
School Name
City
Tier
Board
Students
Plan
Months Active
Section 1 of 6
Admissions Impact
Admission conversion rate measures the percentage of school enquiries that convert to confirmed admissions. NEP LAB improves conversion by helping schools demonstrate tangible learning outcomes and digital innovation to prospective parents.
—
T1 Before
Baseline
—
T1 After
—
—
Overall After
—
Key Finding: Schools that integrated AI LAB demo sessions into open-day events recorded the highest conversion uptick — averaging +14pp versus +10pp for those without live demos. Parents consistently cite three deciding factors: AI LAB demo days, digital progress reports, and NEP compliance certificates.
Why admissions improve — Parents cite AI LAB demo days, digital progress reports, and NEP compliance certificates as their top 3 decision factors when choosing between schools of similar academic standing. The platform gives schools a visible, demonstrable edge.
Section 2 of 6
Student Academic Growth
AI LAB quiz scores (Class 9–12) tracked from baseline through 15+ months of platform adoption. The national benchmark line (57%) represents ASER 2023 grade-appropriate assessment average for Class 9–10 across India.
Key Finding: The improvement curve steepens after Month 6 as students develop sustained AI LAB habits and teachers integrate platform assessments into lesson design. Tier 1 schools reach near-75% performance by Month 12; Tier 2 schools show stronger relative gains from a lower starting point.
Note: Quiz scores reflect internal AI LAB platform assessments. Board exam correlation data will be available after the 2024–25 academic year results are published.
Top 5 Improvers by Quiz Score Gain
Rank
School
City
Plan
Before (%)
After (%)
Gain (pp)
Section 3 of 6
Teacher Empowerment
Administrative burden is the leading cause of teacher burnout in Indian schools (NCERT Teacher Survey, 2023). NEP LAB automates report generation, assessment design, and parent communications — returning meaningful time to classroom instruction.
Weekly Time Breakdown per Teacher
T1 averages shown. T2 averages are approximately 0.5 hrs lower across all categories.
Task
Before
After
Time Saved
Progress report generation
52 min/student
6 min/student
46 min
Weekly lesson planning
4.2 hrs/week
1.8 hrs/week
2.4 hrs
Assessment design
2.8 hrs/week
1.2 hrs/week
1.6 hrs
Parent communication
1.5 hrs/week
0.8 hrs/week
0.7 hrs
Total (T1 Average)
~10.8 hrs
~4.6 hrs
~6.2 hrs
—
Hours freed / week (cohort-wide)
1,774 teachers × avg saved
—
Hours freed / academic year (40 wks)
Cohort-wide estimate
28
Teacher NPS — Before (T1 cohort avg)
Detractor zone
61
Teacher NPS — After (T1 cohort avg)
+33 pts improvement
Section 4 of 6
NEP 2020 Compliance
NEP compliance score is computed via platform activity logs mapped to NEP 2020 competency framework Chapters 4, 5, and 6 — covering Foundational Learning, Holistic Assessment, and 21st Century Skills. Scores represent the percentage of prescribed learning objectives actively addressed by school activities logged on the platform.
NEP 2020 Pillar Coverage — Before vs After
NEP 2020 Pillar
Before
After
Gain
Foundational Literacy & Numeracy
45%
82%
+37pp
Experiential Learning
18%
71%
+53pp
Holistic Assessment
22%
68%
+46pp
Vocational & Life Skills
8%
54%
+46pp
Digital Literacy
31%
88%
+57pp
21st Century Skills
15%
73%
+58pp
Multilingual Education
12%
47%
+35pp
Standout pillar: Digital Literacy (+57pp) and 21st Century Skills (+58pp) show the largest absolute gains — areas where the AI LAB's interactive Python IDE, ML Playground, and Ethics Simulator provide unique, NEP-aligned learning experiences unavailable in traditional classroom settings.
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Overall NEP compliance — Before
Cohort average
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Overall NEP compliance — After
—
Section 5 of 6
Tier 1 vs Tier 2 Comparison
Side-by-Side Metrics
Metric
Tier 1 (20)
Tier 2 (30)
Key Insight: Tier 2 schools show slightly higher relative improvement in NEP compliance despite a lower absolute baseline — indicating the platform is particularly effective where traditional infrastructure gaps exist. The AI LAB essentially democratises access to quality CBSE/NEP-aligned assessment tools that Tier 2 schools previously could not afford to build internally.
Tier 1 — 20 Schools
Admissions
—
Quiz Scores
—
Teacher Relief
56%
NEP Compliance
—
Parent Adoption
78%
Tier 2 — 30 Schools
Admissions
—
Quiz Scores
—
Teacher Relief
54%
NEP Compliance
—
Parent Adoption
71%
Section 6 of 6
School Case Studies
All case studies are illustrative, based on synthetic cohort data. Quotes are illustrative and do not represent real individuals.
Case Study 1 — Gurukul International Academy, Delhi
Tier 1 | Enterprise Plan | 1,850 Students | 68 Teachers | 16 Months Active
Teacher hours freed: 12 → 4.5 hrs/week × 68 teachers = 510 hrs/week freed = 20,400 hrs over academic year
First Delhi school in cohort to cross 70% NEP compliance
"The AI LAB became our biggest differentiator during admission season. Parents were genuinely impressed by the live demo." — Principal, Gurukul International Academy (illustrative)
Case Study 2 — Gomti Nagar Academy, Lucknow
Tier 2 | Pro Plan | 880 Students | 35 Teachers | 16 Months Active
"Our teachers were spending 11 hours a week on admin. That's now 4. They use the saved time for actual teaching." — Director, Gomti Nagar Academy (illustrative)
Case Study 3 — Sri Vidya Academy, Coimbatore
Tier 2 | Pro Plan | 840 Students | 34 Teachers | 15 Months Active
"Breaking the language barrier in AI education"
60% → 74%
Quiz score (14pp gain, above avg)
25% → 68%
NEP compliance
87%
Parent portal adoption — highest T2
Tamil-medium and English-medium students both improving equally — platform's bilingual AI feedback cited as key factor
Quiz gain of 14pp exceeds cohort average of 12.9pp, despite being a Tier 2 school
Highest parent portal adoption rate in the entire Tier 2 cohort at 87%
Section 7 — Financial Analysis
Return on Investment
Conservative financial models using representative school sizes and fee structures. Schools should model their own scenarios using actual enquiry volumes and fee structures.
Representative Tier 1 School
1,200 students · 400 annual enquiries · avg fee ₹80,000 · 40 teachers · Enterprise plan
Item
Calculation
Value
Enquiries converted — before
400 × 38%
152
Enquiries converted — after
400 × 51%
204
Additional admissions/year
204 − 152
52
Additional fee income
52 × ₹80,000
₹41.6 L
Enterprise plan annual cost
—
₹12.0 L
Net additional revenue
₹29.6 L
ROI ratio
41.6 ÷ 12.0
3.5×
Teacher hrs freed (40-wk yr)
40 × 40 × 6.2 hrs
9,920 hrs
Value of time saved (@₹500/hr)
9,920 × ₹500
₹49.6 L
Total value delivered
admissions + teacher time
~₹91 L/yr
Representative Tier 2 School
650 students · 250 annual enquiries · avg fee ₹40,000 · 26 teachers · Pro plan
Item
Calculation
Value
Enquiries converted — before
250 × 29.4%
73
Enquiries converted — after
250 × 41.0%
103
Additional admissions/year
250 × 11.6%
29
Additional fee income
29 × ₹40,000
₹11.6 L
Pro plan annual cost
—
₹6.0 L
Net additional revenue
₹5.6 L
ROI ratio
11.6 ÷ 6.0
1.9×
Teacher hrs freed (40-wk yr)
40 × 26 × 5.1 hrs
5,304 hrs
Value of time saved (@₹500/hr)
5,304 × ₹500
₹26.5 L
Total value delivered
admissions + teacher time
~₹38 L/yr
Important Caveat: ROI projections assume 400/250 annual enquiries, stable fee structures, and full platform adoption. Schools with lower enquiry volumes or partial adoption should model their own scenarios. Teacher time savings (₹500/hr estimate) represent opportunity cost — actual cash savings depend on staffing structures. Past cohort patterns do not guarantee future results.
Conclusion
Three Takeaways for School Leadership
📈
Admissions pay for the platform
For most schools, the additional admission revenue alone recovers the platform cost within the first academic year — even at conservative adoption rates.
🎓
NEP compliance is not optional
Schools averaging 25% NEP compliance risk regulatory scrutiny as NCERT assessments scale post-2025. The platform moves schools from 25% to 65%+ in under 18 months.
🧑🏫
Teacher retention is a hidden ROI
A Teacher NPS jump from 28 to 61 signals dramatically lower attrition risk — replacing a trained teacher costs ₹1–3 lakhs in recruitment and onboarding, making teacher relief a significant financial return.
Schedule a Live AI LAB Demo
Invite your leadership team and parent committee to see AI LAB live — our 45-minute demo is designed to show measurable impact, not slides.
Available for CBSE, ICSE, IB, and State Board schools | Plans starting from ₹4,999/month info@tryspyral.com | www.tryspyral.com
Data Sources & References
ASER 2023 Annual Report — Pratham Education Foundation
NCERT EdTech Adoption Study, 2022–24 — National Council of Educational Research and Training
NASSCOM EdTech India Report, 2024 — National Association of Software and Service Companies
NEP 2020 Implementation Framework — Ministry of Education, Government of India
Data Sources & Methodologytryspyral.com | March 2026
Published Benchmarks Used
ASER 2023 — Annual Status of Education Report. Pratham Education Foundation, New Delhi, 2024. (National learning outcome baselines; subject-wise score distributions.)
NASSCOM & Omidyar Network — Ed-Tech in India: Status and Way Forward. 2023. (EdTech adoption rates, school digitisation patterns, engagement metrics.)
Ministry of Education, GoI — National Education Policy 2020. Government of India. (NEP competency framework, compliance pillars, holistic assessment.)
NCERT — National Curriculum Framework for School Education (NCF-SE) 2023.(Curriculum competency mapping; 21st-century skills taxonomy.)
Central Square Foundation — State of the Sector Report: Online Education in India 2022.(Digital tool adoption by school tier; teacher NPS benchmarks.)
World Bank — Catch-Up: Helping Students Learn After COVID-19 School Disruptions. 2022. (Learning recovery trajectories; teacher time-on-task data.)
VanLehn, K. (2011). "The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems." Educational Psychologist, 46(4), 197–221. (AI tutoring effectiveness; 0.4–0.76σ improvement basis.)
Bloom, B. S. (1984). "The 2 Sigma Problem." Educational Researcher, 13(6), 4–16. (Theoretical ceiling for personalised AI-assisted learning gains.)
Modelling Methodology
This is a synthetic cohort projection study. A virtual cohort of 50 schools was constructed using parameters derived from the benchmarks listed. The cohort is not based on live platform telemetry from actual deployments.
Baseline parameters — admission conversion rates, quiz score distributions, teacher admin hours, and NEP compliance scores — were set using ASER 2023 and CSF 2022 sector averages, stratified by city tier and board affiliation.
Post-adoption parameters were modelled using effect sizes from VanLehn (2011) and World Bank (2022), scaled to the Indian K-12 context using NASSCOM 2023 adoption trajectories. A 6–18 month full-adoption window was assumed.
Financial projections use representative private school fee structures across CBSE, ICSE, and State Board categories. They are not derived from any school's accounts.
Important — Projections represent expected outcomes under full-adoption conditions. Actual results depend on school infrastructure, teacher training completion, student engagement, and implementation depth. This report illustrates potential impact and does not constitute a guarantee of results.
Cohort Parameters & Sources
Parameter
Tier 1 Range
Tier 2 Range
Source
Baseline admission conversion
35–44%
26–36%
CSF 2022
Baseline assessment score
61–69%
53–65%
ASER 2023
Teacher admin hrs/week
10–13 hrs
9–11 hrs
World Bank 2022
Baseline NEP compliance
25–38%
18–30%
NCF-SE 2023
AI tool score improvement
+12–14pp
+11–13pp
VanLehn 2011
NEP LAB & AI LAB — School Impact Report | March 2026 | tryspyral.com | info@tryspyral.com |
Synthetic projection study. All referenced publications are publicly available. School names are illustrative and do not represent actual clients.
