What Makes Saas-based Education Technology in India Effective

Published on Oct 1, 2019

Updated on Mar 3, 2020

Worldwide, the education and training industries spend over $4 trillion annually. Out of this, 85% of the expenditure accounts for delivery costs – i.e. the cost of building and infrastructure, teaching staff, etc. In India, the Union Budget 2018 allocated Rs 85,010 crore ($1.2 billion) for education. Despite this expenditure, the current education system is unable to deliver adequate skills to make students job-ready for the industry 4.0 era. Education technology (EdTech) bridges the gap between delivering education and making learning effective. Let’s look at how Education Technology in India can reform learning?

Education Technology: Skills that will be required in Future

Why EdTech?

The 2016 Trading Economics data reveal – 44.85 million people are unemployed in India; despite there being more than 1.6 million schools and 38 thousand colleges. This illustrates a gap in the existing education system and learning resources.

Although the expenditure on education is rising, educational performance is still weak. Cost-effectively improving the quality of education is the need of the hour. And ed-tech is an opportunity for upscaling ‘education’ at the global level.

EdTech is not just redesigning the textbooks in a digital format. The role of education technology also lies in developing applications in incorporating new learning architectures. Apart from revamping the learning architecture, cloud-based EdTech platforms can make learning material accessible to students anytime.

What Makes SaaS-based EdTech Effective?

Education technology encompasses several domains like reading materials, computer-based training, e-learning, and m-learning (learning through computer or mobile), teacher training, curriculum management, and back-office management. People also consider EdTech as Education Management Information System (EMIS). The following are the salient features of the Education Technology platform.

Scalability

In India, the classroom standards in the government-funded schools define the pupil to teacher ratio (PTR) in upper primary classes as 35:1. That is, a typical classroom should not accommodate more than 35 students. It is one of the reasons that many students do not get admission in good schools.

The SaaS-based Education Technology can make courses and learning material available to the students irrespective of their geographical location. EdTech platforms are easy-to-use and don’t require any preliminary training to use the software.

Engaging Learning Platform

To motivate students to learn without fear of grades and punishment is a daunting task. Unless the learning material is engaging, chances for its success is less. EdTech solves this challenge by deploying technology like virtual reality, gamification, and discussions on its platform. To bring the best of education, some education technology platforms also incorporate open learning models or open license content standards.

For example, EkStep – a non-profit initiative has created a collaborative platform to bring equal learning opportunities to every child, especially in rural India. It is an open platform where facilitators can join and create interactive courses for learners.

Education Management

An academic institute requires faculties, infrastructure, and efficient management for its smooth functioning. Technology in education is also making school management easy.

For example, Kreedo – an EdTech startup provides SaaS-based school and learning management system. It helps preschools with curriculum, teachers training, teaching material, child assessment, and other school administration tasks.

Faculty Training

Normally, teaching experience and educational qualifications are the only criteria for faculty recruitment in India. That’s why often faculties lack the skill to convey their knowledge to pupils effectively.

EdTech startups like flipClass track teachers’ performance and advise them on their technology platform. This application provides two types of feedback to faculties at the child’s level and concept level. It also assesses teaching against benchmarks.

Accessibility in Regional Languages

KPMG reports- video content will cover 80% of the global internet consumption. In India, consumers spend 50-60% of their average time on Hindi videos, 35-43% on regional videos, and only about 7% on English videos. Moreover, India homes 22 major languages and 13 different scripts.

To enhance conceptual understanding, materials available in learners’ comfortable language is a bonus. Language translations and curriculum in regional language are the additional benefits of education technology.

What’s the Future of Education Technology in India?

With over 3,500 startups, Education Technology in India is expected to reach $1.96 Bn by 2021.

While tier-1 cities boast of quality classroom education, tier-2 and tier-3 cities suffer a setback due to lack of resources. Ed-Tech can make quality learning accessible to rural regions. It can also help with teachers’ skill development, classroom infrastructure, and school management.

EdTech will make more use of technologies like Augmented Reality (AR), Virtual Reality (VR), Blockchain and STEM (Science Technology Engineering Math) Labs to design interactive learning interfaces.

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

Can AI recognize its own mistakes?
What would “self-awareness” look like in AI without consciousness?
Are there techniques to make AI second-guess itself?
Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

An Overview of Education Technology in India

Why EdTech?