COVID-19 Lockdown Effects: A Paradigm Shift in Indian Edtech

There has been a significant change in the education industry in India in the past couple of years. From syllabus to teaching methods, from enrollment levels to infrastructure available; technology was majorly responsible for this major shift. Educomp, founded in 1994 and one of the earliest Indian edtech, changed academia with multimedia content, computer labs and teacher training. Today, BYJU’S, founded in 2011, is revolutionizing edtech with its m-learning platform. There’s something more that is contributing to the widespread adoption of e-learning platforms.

As the country came to a standstill with a 21-day nationwide lockdown being imposed, online education companies in India sought this as an opportunity to attract students, academicians, schools, colleges, corporates and the media.

Within the first two weeks of the lockdown, many online and edtech players offered their online courses for free — trying to reach as many audiences as possible. The COVID-19 crisis turned out to be an amazing opportunity for edtech to spread its perimeter and reach out to the audience which was earlier ignorant of this sector. But the question is whether the edtech surge is short-term or will it turn out to be a paradigm shift in India.

Why Online Education?

Government awareness programs have shaped the importance of education in people’s minds, which is that education leads to jobs. But the lack of adequate infrastructure, facilities, and teachers have led to decreasing quality of education. Online education is convenient to access, which is why it is gaining popularity amongst the rural population. In places where there is limited infrastructure, many are turning towards online courses, courtesy — access to the internet.

[Also read: What Makes Saas-based Education Technology in India Effective]

While many cannot afford an institutional education, online education has made it monetarily feasible for the population at large. By 2021, $1.96 billion will be the size of the edtech market in India, a KPMG edtech study reveals. In the current crisis where the lockdown has led to massive unemployment, online education concerning skill enhancement has seen an upsurge.

Technology trends in EdTech

From the introduction of hardware such as projectors and computers in the classroom to learning through tabs and laptops at home, the education industry has evolved tremendously. The ideology behind edtech has been to create newer learning experiences keeping with the pace of rapid digitization.

Gamification has gained significant popularity amongst Indian education service providers as it has made the learning process interesting. Many edtech players have started adopting technologies like Artificial Intelligence and Machine learning which enable teachers and policy makers to get better insights about their students and modify learning methods accordingly. A lot of research is going into technologies like Virtual Reality and Augmented Reality to create interactive learning modules for better understanding of complex subject domains. In case of long-form answers, natural-language processing (NLP) can make the assessor’s job easy by giving detailed and formative feedback.

[Also read: Top 25 Disruptive Augmented Reality Use Cases]

Cloud-based data storage provides convenience to students who can access and share data easily. With the on-going lockdown and social distancing, there could be scope for untapped technologies such as wearable devices and virtual labs which can take learning experiences to another level.

For instance, Indian edtech startups like Edureka are very serious about customer experience and are taking AI initiatives for Live Chat Analysis and Career Path Research.

[Read Case Study: Customer experience design in Edureka e-learning mobile app]

Opportunities for Indian EdTech amidst the pandemic

Education can be categorized in different segments such as Primary and Secondary education i.e the K-12 segment, Test Prep, Skill Enhancement, and Higher Education. Schools and colleges have been hit quite a bit due to the lockdown as they remain shut till the situation improves. Even though learning has not stopped as teachers have been taking online lessons, will online education replace a traditional one?

Many edtech experts say that online learning enables students to interact with a larger pool and gives more focus to individual learning. Certainly, technologies can help create innovative and imaginative learning experiences but can they match with learning through human interaction? That is doubtful. However, edtech would be a very powerful aid for teachers to improve the learning process.

A research by McKinsey states that teachers spend around 20 to 40 percent of their time on activities which could be simplified by automating using current technologies. This time could be optimized by spending on relevant activities focused on student learning. Children are the future citizens of the world. Teachers have a pivotal role in grooming them towards successful personal and professional life. In order to adapt in the post-pandemic world, technology alone cannot bring the change. The learning experience brought in by a teacher is equally important.

The economic slowdown has made the youth cognizant of the unemployment that may hit the world. The upside to this is that the online education industry will see more enrollments in skill-enhancement courses from both rural and urban population. The digital education initiatives will see a monetary boost by the government. This lockdown has enabled people to pursue their passions and take up online tutorials such as cooking, teaching, writing, learning a different language, fitness, learning musical instruments, and other art. This could potentially lead to a thriving passion economy driven by budding entrepreneurs.

Probable obstacles to Indian EdTech

Edtech will certainly prove to be a booming sector but there are certain challenges on the way.

Access to internet and bandwidth issues

One of the biggest challenges to the Indian edtech would be accessibility for the population especially in the rural areas. Issues with internet connectivity, bandwidth, hardware might make it difficult to pursue online courses.

Lack of digital literacy

A major part of Indian populace is still digitally illiterate. Especially, the rural population is still not tech-savvy to understand the features of digital devices. Products with simpler UX suitable for the end-user is the need of the hour. Many edtech players still find it difficult to create user-friendly UX that makes technology easy to apply.

Rising competition

EdTech has been making huge progress in the past 10 years and many have recognized its potential to even grow further with the lockdown. The industry is getting crowded with new entrants which makes it difficult for the consumers to remain loyal to one. Subsequently, it is leading to reduced market share for each company.

Investment in advance technologies

This sector definitely has huge potential. However, with the economic slowdown, huge investment in technologies like AI, ML, AR, VR could get affected. There are huge risks in materializing AI projects and might take some time to receive RoI.

The Bottom Line

Edtech has its pros and cons but there is no doubt that the industry is here to thrive in the long run. This lockdown has proved that the virtual learning systems can operate. Many education boards have understood its potential to grow and will start integrating technology into their syllabus.

Furthermore, EdTech could significantly improve the quality of educational content and overall learning experience especially for the rural population. For instance, an edtech initiative — EkStep (a non-profit organization) intended to build an advanced, universal, and collaborative platform for K-12 Education space with a focus on rural India.

Post pandemic, the world will still follow social distancing for some time but the need for human interaction will not diminish but rather see a craving for it. In the short term and the medium term, the edtech industry can reap the benefits of this crisis but to survive in the long run, continuous innovation in technology that does not substitute but rather aid in the classroom learning is needed.

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled

COVID-19 Lockdown Effects: A Paradigm Shift in Indian Edtech

Why Online Education?

Technology trends in EdTech

Opportunities for Indian EdTech amidst the pandemic