---
title: "Discussion on \"Why Recommendation Systems Fail in Education (And What I Built Instead)\" | Hashnode"
description: "Discussion on \"Why Recommendation Systems Fail in Education (And What I Built Instead)\". Standard recommenders violate 81% of curriculum prerequisites. I built PLRS to fix it using knowledge tracing, forgetting curves, and a DAG."
url: "https://hashnode.com/posts/why-recommendation-systems-fail-in-education-and-what-i-built-instead/6a21f583318ee8975e217736"
---

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# [Why Recommendation Systems Fail in Education (And What I Built Instead)](https://clementina-tom.hashnode.dev/why-recommendation-systems-fail-in-education-and-what-i-built-instead?utm_source=hashnode&utm_medium=feed)

Standard recommenders violate 81% of curriculum prerequisites. I built PLRS to fix it using knowledge tracing, forgetting curves, and a DAG.

Jun 4 · 10 min read · clementina-tom.hashnode.dev  
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## Written by

[Clementina Tom](https://hashnode.com/@Clementina-Tom)

@Clementina-Tom · Nigeria

  
[View profile](https://hashnode.com/@Clementina-Tom)

ML Engineer | AIoT Enthusaist

An ML engineer building in public. Looking at the intersection of AI/Ml use in real life. Currently, i write about production MLOps. and AI/ml systems i education. I am the open-source ML.Author of PLRS -- a personalized learning recommendation system

## Responses 1

Markdown supported · ⌘↵ to post

[Mateo Ruiz](https://hashnode.com/@mateoruiz)

[Jun 19](https://hashnode.com/posts/why-recommendation-systems-fail-in-education-and-what-i-built-instead/6a21f583318ee8975e217736/comment/6a3533cb327680574489826b)

This is one of the more interesting education-AI projects I've read lately because it challenges a core assumption from traditional recommender systems: maximizing engagement isn't the same as maximizing learning.

The prerequisite graph + forgetting curve combination makes a lot of sense. In most real-world learning environments, recommending the "next likely click" can actually push students into topics they're not ready for, which creates confusion rather than progress.

The 0% prerequisite violation result is impressive, but what stood out most was the shift in problem framing. Instead of asking "What content will the student consume?", you're asking "What concept should the student learn next?" Those are fundamentally different optimization goals.

I also think the future direction is fascinating. If prerequisite graphs can eventually be discovered automatically from learner behavior rather than manually curated, that could make personalized learning systems far more scalable across subjects and curricula.