Transformation of Engineering Education in India (2015–2026): A Data-Driven Analysis of Admissions, Employability, Faculty Well-being, and the Rise of AI-Driven Disciplines

Abstract

India\'s engineering education system underwent its most turbulent twelve-year period since liberalisation between 2015 and 2026: undergraduate intake fell from roughly 17 lakh approved seats in 2014-15 to a decade low near 12.5 lakh in 2021-22, before rebounding to about 14.9 lakh in 2024-25 and an approved 15.98 lakh across 5,875 AICTE-recognised colleges for 2025-26, driven almost entirely by demand for computer science, artificial intelligence (AI), and data-science programmes while core branches such as civil and mechanical engineering continued to lose share. Parallel to this reallocation, employability estimates from national skill-assessment surveys rose from roughly one-third of graduates in the mid-2010s to a peak near 71.5% in 2025 before easing slightly to 70.15% in the India Skills Report 2026, with computer-science and IT graduates reporting employability of 80% and 78% respectively and women\'s employability (54%) overtaking men\'s (51.5%) for the first time in five years; independent hiring-outcome studies nonetheless suggest that only a minority of graduates secure engineering-relevant employment in their graduation year, revealing a persistent gap between assessed employability and realised employment. At the same time, mounting evidence of faculty vacancy rates exceeding 40-50% in several states, heavy reliance on ad hoc guest faculty, and emerging work-life-imbalance research signal a well-being crisis among engineering educators that has received comparatively little empirical attention relative to student-facing metrics. This paper proposes a PRISMA-guided systematic literature review combined with an explanatory sequential mixed-methods design to model the structural relationships among admissions capacity, curricular AI-orientation, faculty well-being, and graduate employability across the 2015-2026 period, using nationally representative administrative datasets (AICTE, AISHE, NIRF), placement and skills-assessment reports through the India Skills Report 2026, and a purpose-built faculty/student survey. The analytical plan combines descriptive time-series analysis, multiple regression, exploratory and confirmatory factor analysis, structural equation modelling, and machine-learning forecasting (ARIMA/SARIMA, Random Forest, XGBoost) to both explain past dynamics and forecast enrolment, discipline-mix, and employability trajectories to 2030. We outline expected findings, policy implications for seat-capacity regulation, faculty recruitment, and curriculum reform, and a research agenda addressing regional and gender disparities, teaching-load-linked well-being, and the sustainability of the current AI-disciplinary boom.

Introduction

This paper examines the transformation of Indian technical higher education from 2015 to 2026, focusing on four interconnected areas: engineering admissions, graduate employability, faculty well-being, and the AI-driven shift in academic disciplines. India’s engineering education sector experienced a major decline in the late 2010s, with approved undergraduate seats falling from over 17 lakh in 2014–15 to around 12.5 lakh in 2021–22 due to reduced demand, institutional closures, and regulatory restrictions. However, the sector recovered after 2022, driven mainly by rapid expansion of computer science, artificial intelligence, data science, and related programmes, while traditional branches such as civil and mechanical engineering continued to face weaker demand.

The study highlights a significant gap between reported employability levels and actual employment outcomes. Industry skill assessments indicate rising employability among engineering graduates, especially in computer science and IT fields, with AI-related hiring showing strong growth. However, independent hiring reports suggest that many graduates still struggle to secure jobs or internships. This difference between skill-test performance and real employment outcomes forms a key research problem.

Another major concern is faculty capacity and well-being. Although engineering institutions have expanded and introduced AI-focused curricula, many colleges continue to experience severe faculty shortages, high workloads, recruitment challenges, and burnout. Limited faculty availability may negatively affect teaching quality, student outcomes, and institutional performance.

The paper proposes an integrated framework connecting:

1. Regulatory and capacity changes – AICTE policies, seat approvals, and institutional expansion.
2. Admissions and discipline trends – growth of AI/CSE-related programmes versus decline of core engineering branches.
3. Faculty well-being and institutional capacity – workload, vacancies, burnout, and retention.
4. AI-oriented curriculum transformation – adoption of AI and data science education.
5. Graduate employability and employment outcomes – skills, placements, salaries, and job alignment.

A PRISMA-guided systematic review protocol is proposed to analyse existing research using academic databases, government reports, and industry studies. The research identifies several gaps, including the lack of integrated studies combining admissions, employability, faculty conditions, and AI transformation; limited longitudinal analysis; insufficient investigation of faculty well-being; and inadequate use of advanced statistical and machine-learning forecasting methods.

The proposed research uses a mixed-methods approach combining decade-long administrative data with surveys and interviews. It plans to apply statistical techniques such as regression analysis, factor analysis, and structural equation modelling (SEM), along with machine-learning forecasting methods to predict future engineering education trends up to 2030.

Conclusion

This manuscript scaffold is publication-structured and grounded in verifiable, current administrative and industry reporting (AICTE, AISHE, NIRF, India Skills Report, and recent journalistic/academic coverage of faculty vacancy and work-life-imbalance research). Before submission to a Q1 outlet, the author team should: (1) execute the PRISMA search protocol in Section 3 and populate the flow-diagram counts; (2) field and validate the SEES/FWWS/IPS instruments; (3) acquire and clean the full 2015-2026 AICTE/AISHE/NIRF panel; (4) run and report the full statistical/ML analysis plan with actual results, tables, and figures; and (5) populate the reference list below with the full peer-reviewed citation set identified through the systematic search, ensuring every empirical claim is traceable to a verifiable source per journal requirements.

References

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Copyright

Copyright © 2026 Kirankumar B, Navaneeth I M, Vittal Bhat M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.