DEV Community: Mahatir Ahmed Tusher

Why AI Will Not Replace Teachers, But It Will Change the Way Students Learn

Mahatir Ahmed Tusher — Wed, 08 Jul 2026 12:36:29 +0000

Artificial intelligence has become one of the most discussed technologies in education. From automated grading systems to AI chatbots capable of answering complex questions, many people wonder whether AI will eventually replace teachers.

The short answer is no.

Education has never been just about delivering information. Great teachers inspire curiosity, understand students' emotions, adapt to different learning styles, and create environments where learners develop critical thinking. These are deeply human abilities that artificial intelligence cannot fully replicate.

However, AI is beginning to solve a different problem: helping students learn independently outside the classroom.

The Problem With Traditional Self-Study

Many students spend hours reading textbooks without truly understanding the concepts. When they encounter a difficult paragraph, they often search the internet, only to find lengthy articles, conflicting explanations, or answers that are either too advanced or completely unrelated to their curriculum.

This creates an inefficient learning process where students spend more time searching than actually learning.

Another common challenge is passive learning. Reading a chapter once often creates the illusion of understanding, but without testing knowledge through questions or applying concepts, much of that information is quickly forgotten.

How AI Can Support Learning

Modern educational AI systems are becoming less like search engines and more like interactive learning companions.

Instead of simply returning search results, these systems can explain concepts in simpler language, adapt explanations to a student's academic level, answer follow-up questions, generate practice quizzes, and even identify areas where additional practice is needed.

This creates a much more personalized learning experience.

Learning From Personal Study Materials

One of the most interesting developments in AI education is the ability to work with a student's own resources.

Rather than relying only on public information, newer AI systems can analyze lecture slides, class notes, PDFs, and textbooks, allowing students to ask questions directly about their own study materials. This makes revision faster and reduces the time spent searching through hundreds of pages.

For university students and competitive exam candidates, this capability can significantly improve productivity.

Mathematics Requires Understanding, Not Just Answers

Mathematics presents another unique challenge.

Students rarely struggle because they cannot find the final answer. They struggle because they cannot understand the reasoning behind each step.

AI-powered mathematical assistants are increasingly focusing on procedural explanations rather than answer generation. By showing every intermediate step, highlighting common mistakes, and explaining why a method works, they encourage conceptual understanding instead of memorization.

Active Recall Matters More Than Reading

Educational research has consistently shown that testing yourself is more effective than repeatedly rereading the same material.

Practice quizzes, flashcards, previous exam questions, and spaced repetition all strengthen long-term memory through active recall.

Artificial intelligence makes this process easier by automatically generating personalized quizzes from learning materials and adapting future practice based on previous performance.

The Rise of AI-Powered Learning Platforms

Around the world, educational technology companies are integrating these capabilities into unified learning platforms. Rather than offering only an AI chatbot, they combine digital textbooks, intelligent tutoring, personalized quizzes, mathematical reasoning, document analysis, and progress tracking into a single ecosystem.

Bangladesh is beginning to see this transition as well. One example is Progga (https://progga.bd/), an AI-powered educational platform designed around the local curriculum. Instead of functioning as a general-purpose chatbot, it integrates textbook learning, AI-assisted explanations, document-based question answering, mathematics support, customizable quizzes, formula libraries, flashcards, and previous examination resources within one platform. These kinds of systems demonstrate how AI can complement traditional education rather than compete with it.

Looking Ahead

Artificial intelligence is unlikely to replace classrooms or teachers anytime soon. What it will replace are many of the repetitive obstacles students encounter every day: searching for explanations, organizing notes, generating practice questions, and reviewing difficult topics independently.

The future of education will probably be a collaboration between human educators and intelligent digital tools.

Teachers will continue to provide mentorship, motivation, and real-world guidance.

AI will provide personalized assistance whenever students need it.

Together, they have the potential to make learning more accessible, engaging, and effective than ever before.

How We Built Progga (progga.bd): A RAG-Based AI Tutor That Actually Understands the Bangladeshi Curriculum

Mahatir Ahmed Tusher — Wed, 08 Jul 2026 12:33:04 +0000

A few months ago, one question kept nagging at our team: what if every student had a tutor that never got tired, never got annoyed, and always had their exact textbook open in front of them?

We didn't want to build another generic chatbot. Ask ChatGPT or Gemini directly to "explain HSC Physics" and you'll usually get something generic — not aligned with the actual curriculum, and often pulling context from the wrong place entirely. We wanted a system that genuinely reads a student's own textbook and answers in that book's language, grounded in that exact chapter's context. That was the starting point for Progga (progga.bd).

In this post, I'll walk through the technical decisions we made, where we got things wrong early on, and how the current architecture came together.

The Actual Problem

NCTB textbooks and the popular HSC textbooks from major Bangladeshi publishers mostly exist as PDFs. Feeding these PDFs directly into a vector database causes some very specific problems:

Chapter structure (headings, sub-headings, example boxes) gets destroyed
Tables and formulas turn into garbled text
A single chunk ends up mixing content from two unrelated topics, which drags the wrong context into retrieval

Our first prototype made exactly this mistake — extracting raw text from PDFs, chunking it naively, and ingesting it straight into the vector store. The result: the RAG pipeline frequently pulled answers from the wrong chapter, or stitched together half-formed sentences into an answer that read as confident but was subtly wrong.

The Fix: Converting Every Textbook to Clean Markdown First

We changed course. Every textbook was converted into clean, chapter-by-chapter Markdown documents — preserving headings, sub-topics, worked examples, and figure captions — before any of it touched the embedding pipeline. Three things improved immediately after this change:

Retrieval accuracy jumped noticeably, because chunk boundaries now followed the actual chapter/topic structure instead of an arbitrary character count.
Semantic search became far more context-aware — a query would now pull back a coherent topic unit instead of half a paragraph.
It became much easier to separately tag formulas, examples, and real-world applications — which later became the backbone of our "Formula Sheet" feature.

Looking back, this single decision probably had the highest ROI of anything we did on this project.

Choosing the Vector Database and Embedding Model

For embedding and retrieval, we settled on:

Pinecone as the vector database — it kept ops overhead low as we scaled, and metadata filtering (by grade, subject, chapter) was straightforward to implement.
Jina Embeddings for text embedding. Our textbooks are a mix of English terminology and Bangla explanation, and Jina's performance on that kind of mixed-language content held up noticeably well.

One important trick here: we don't just run a raw similarity search. We first filter by grade + subject + chapter metadata, and then run semantic search within that narrowed subset. This significantly reduces the risk of a topic with the same name from a different grade's textbook surfacing incorrectly.

The Generation Layer: LangChain + Gemini

Once retrieval brings back the right chunks, the next step is turning them into a coherent, grade-appropriate answer. We use LangChain for orchestration, with Google Gemini as the generation model.

A few things were non-negotiable here:

Explicitly passing the student's grade level into the prompt, so the same question gets simplified differently for a Class 6 student versus an HSC student.
Keeping source attribution — tracking exactly which chapter/section an answer came from. This matters a lot for the "Study Workspace" feature, where a student uploads their own notes and chats with them; the system needs to answer strictly from that uploaded material and not from general world knowledge, to keep hallucination in check.
Building honest fallback behavior — when retrieval doesn't surface enough relevant context, the system should say "I'm not confident about this topic" rather than confidently generating a wrong answer.

Backend and Infrastructure

The stack ended up looking like this:

Next.js — the frontend, covering the whole student-facing surface (AI Classroom, Chat, Quiz, Flashcards, etc.)
FastAPI — the backend API layer, hosting the RAG pipeline and business logic
PostgreSQL — user data, progress tracking, quiz history, structured content metadata
Redis — **caching plus session/rate-limit management, particularly to keep latency down on quiz generation and chat responses
**Docker — keeping the whole system containerized for consistent deployment

One reason we picked FastAPI was async support. A single request often involves multiple I/O-bound steps in sequence — vector search, an LLM call, a database read/write — and without an async architecture, latency compounds fast.

What's Built on Top of This Pipeline

Once the core RAG pipeline was stable, we layered feature after feature on top of the same foundation:

AI Classroom — read the digitized textbook and highlight any line to ask the AI about it directly
Progga Chat — a subject- and chapter-scoped conversational assistant
Study Workspace — upload your own notes or slides and get answers grounded strictly in that source material
Math Assistant (Onko Bhaiya) — scan a handwritten or typed math problem and get a step-by-step walkthrough
Quiz and Question Bank — generate unlimited MCQs/CQs from any chapter, and analyze past board exam papers to flag which topics deserve the most attention
Formula Sheet and Flashcards — pulled directly from the structured Markdown, so every formula comes with context and real-world application attached

What We'd Do Differently (or the Same) Next Time

A few lessons that would shape how we'd approach a RAG system from day one:

Never ingest raw PDFs directly. Preserving content structure takes more upfront effort, but the impact on retrieval quality is enormous.
Filter by metadata before running semantic search, not after. Relying purely on embedding similarity leaves you exposed to cross-grade or cross-subject context bleeding into answers.
Treat grounding as a first-class concern, not an afterthought. In an education context specifically, the cost of a confidently wrong answer is high — source attribution and honest fallback behavior need to be part of the initial design, not bolted on later.

Closing Thoughts

We believe AI shouldn't just answer random questions — it should make quality education accessible regardless of where a student lives or whether their family can afford a private tutor. Progga (progga.bd) is our small step toward that.

There's still a lot ahead — multi-agent classroom experiences, 3D simulations, an in-browser coding environment are all in progress. Feedback and questions are always welcome.

🚀 Try Progga: https://progga.bd

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