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AI in Edutech

Enhancing Data Accessibility for Edutech Professionals

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01

AI-Driven Diagnostics

Impressive strides in Artificial Intelligence-powered diagnostic technologies.

02

Empowering Personalized Treatment through AI Algorithms

Development of personalized treatment plans for individual diseases leveraging AI algorithms.

03

AI-Powered Data Accessibility and Decision Support Systems

The advantages of AI-driven data accessibility and decision support systems for edutech professionals.

Executive Summary

Introduction to Kloia: Kloia, with its expertise in cloud-native solutions, DevOps, and software modernization, has been at the forefront of driving technological innovation across various sectors. Our commitment to leveraging cutting-edge technologies to solve real-world problems positions us uniquely to undertake transformative projects in healthcare.

Overview of the AI in Edutech Project:

This project aims to develop a AI-powered educational technology (EdTech) application that enables students to capture photos of subject problems and receive step-by-step solutions or interactive guidance to solve them. The application leverages AI-driven handwriting recognition, problem-solving models, and adaptive learning techniques to enhance students’ understanding.

By integrating OCR (Optical Character Recognition), AI-powered subject reasoning, and interactive learning methods, the app will bridge the gap between AI assistance and active student engagement, allowing students to solve problems collaboratively with AI instead of just receiving answers.

The solution supports various learning modes, including:

  • Instant solutions for independent learning

  • Guided problem-solving mode where AI assists the student through each step

  • Performance tracking and personalized practice recommendations

The Problem

No company context
AI tools write code without knowing your codebase, your specs, your stored procedures, your standards. Generic in, generic out.

No company context AI tools write code without knowing your codebase, your specs, your stored procedures, your standards. Generic in, generic out.

Zero audit trail
Nobody knows what changed, by whom, against which spec, at what cost. When something breaks, the log is empty.

Zero audit trail Nobody knows what changed, by whom, against which spec, at what cost. When something breaks, the log is empty.

Knowledge stays siloed
The senior engineer's head is still the source of truth. The pipeline learns nothing. That person leaves, the knowledge walks out.

Knowledge stays siloed The senior engineer's head is still the source of truth. The pipeline learns nothing. That person leaves, the knowledge walks out.

Pilot never becomes default
The shiny demo works. Then adoption stalls. The new way of working never replaces the old one.

Pilot never becomes default The shiny demo works. Then adoption stalls. The new way of working never replaces the old one.

Why AIDLC?

Faster delivery
Tens of dollars per feature, hours of elapsed time. Not weeks.

Built-in compliance
Every action audited. Every dollar attributed to an issue.

Institutional memory
The pipeline learns. The org's knowledge stops walking out the door.

Faster onboarding
New engineers ramp on a pipeline that already knows the codebase. 

A pipeline that improves
Each cycle's retro feeds the next cycle's configuration.

Humans stay in control
Agents propose. Humans approve. Every merge is gated.

Problems

No company context

No company context

Zero audit trail

Zero audit trail

Knowledge stays siloed

Knowledge stays siloed

Pilot never becomes default

Pilot never becomes default

Why AIDLC?

Faster delivery

Faster delivery

Built-in compliance

Built-in compliance

Institutional memory

Institutional memory

Faster onboarding

Faster onboarding

A pipeline that improves

A pipeline that improves

Humans stay in control

Humans stay in control

Scope of Work

scan

Subject Recognition

  • Students can take a photo of any kind of subject using their device’s camera.
  • AI extracts and identifies mathematical expressions, numbers, symbols, and text.
  • Supports both printed and handwritten questions.
Subject Recognition
steps

Subject Solving

  • AI breaks down the solution into logical steps, showing each process transparently.
  • Supports multiple solution methods (e.g., algebraic vs. graphical approaches).
  • Offers interactive learning, where students attempt steps before AI provides guidance.
Subject Solving
signature

Handwriting Recognition & Student Input

  • Supports handwritten problem-solving via Apple Pencil, stylus, or touchscreen.
  • AI analyzes student-written steps and provides real-time feedback on errors.
Handwriting Recognition & Student Input
chart-line-up (1)

Student Progress Tracking & Adaptive Learning

  • Tracks student performance and progress over time.
  • AI recommends practice questions based on past mistakes and weak areas.
  • Integrates a gamified learning experience to increase engagement.
Student Progress Tracking & Adaptive Learning
package

Deliverables

  • AI-powered Subject Recognition
  • Subject Solution Generator
  • Interactive Problem-Solving Mode
  • Handwriting Recognition & Input
  • Support
  • Performance Tracking Dashboard
  • Final Testing & Deployment
Deliverables

Handover Process and Customer Sign Off

file-text
Documentation
  • A comprehensive technical documentation will be provided during the handover process, covering the developed AI model, integration processes, and API usage.
  • The documentation will explain how the model operates, which datasets were used for training, performance criteria, and potential future improvements.
Documentation
chalkboard-teacher
Training
  • Training sessions will be conducted for users and technical teams, detailing AI system usage, API integration, and maintenance processes.
Training
pulse
Operational Readiness & Monitoring
  • Before going live, all system components will be verified to ensure full functionality on AWS.
  • Performance monitoring tools (e.g., CloudWatch, Prometheus) will be configured, and automated alert mechanisms will be enabled.
Operational Readiness & Monitoring
seal-check
Validation & Approval
  • The system will be validated with the customer to ensure it meets the expected performance criteria.
  • Once customer testing is completed, a formal “Handover Acceptance Document” will be signed to close the project.
Validation & Approval
Case Study : Edutech Industry

Eleven Plus Preparation with AI Product Deployment

Read More  → 

FAQ

What is the main objective of Kloia's AI in Edutech project?

The project aims to develop a AI-powered educational technology (EdTech) application that allows students to take photos of subject problems and receive step-by-step solutions or interactive guidance. 

What learning modes does the EdTech application support?

The solution supports three main learning modes: instant solutions for independent learning, guided problem-solving mode where AI assists the student through each step, and performance tracking with personalized practice recommendations. 

Does the application just give students the answers?

No. By integrating OCR, AI-powered subject reasoning, and interactive learning methods, the app bridges the gap between AI assistance and active student engagement, enabling students to solve problems collaboratively with the AI. 

How does the Subject Recognition feature work?

 Students can take a photo of any kind of subject using their device's camera. The AI then extracts and identifies mathematical expressions, numbers, symbols, and text. 

Does the application support handwritten text?

 Yes, it supports both printed and handwritten questions. It also allows handwritten problem-solving input via Apple Pencil, stylus, or touchscreen. 

What kind of problem-solving support does the AI provide?

 The AI breaks down solutions into transparent, logical steps. It supports multiple solution methods (such as algebraic vs. graphical approaches) and offers interactive learning where students attempt steps before the AI provides guidance. 

How does the AI help students improve over time?

The application tracks student performance and progress over time. The AI analyzes student-written steps to provide real-time feedback on errors and recommends practice questions based on past mistakes and weak areas. It also integrates a gamified learning experience to increase engagement. 

What are the main deliverables of this project?

The deliverables include AI-powered Subject Recognition, a Subject Solution Generator, an Interactive Problem-Solving Mode, Handwriting Recognition & Input Support, a Performance Tracking Dashboard, and Final Testing & Deployment. 

What is included in the handover and customer sign-off process?

The handover process includes comprehensive technical documentation covering the developed AI model (how it operates, training datasets, performance criteria, and potential future improvements), integration processes, and API usage. It also includes training sessions for users and technical teams on AI system usage, API integration, and maintenance. 

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