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Master Thesis: AI-Powered Learning Paths using Knowledge Graphs and LLMs

Background
Kollin is developing AI solutions to enhance how university students learn. We're exploring an interesting intersection of Large Language Models (LLMs) and knowledge graphs to create personalized learning experiences. This project aims to advance both the theoretical understanding and practical applications of AI in education.

Goal
Develop an AI system that generates intelligent study paths by combining knowledge graphs with large language models, helping students navigate complex engineering subjects more effectively. You are welcome to come with a suggestion in close connection to the idea above.

Project Description
You'll work on:
- Building a knowledge graph representation of engineering curricula
- Developing methods to combine LLMs with graph algorithms
- Creating evaluation frameworks for AI-generated study paths
- Testing with real engineering students
- Contributing to a system that will be used in practice

Required Competencies
Must have:
- Currently enrolled in a Master's program at KTH (Computer Science, AI, or related)
- Strong programming skills in Python
- Experience with AI/ML frameworks (PyTorch/TensorFlow)

Nice to have:
- Knowledge of knowledge graphs and graph algorithms
- Experience with LLMs and recommendation systems
- Interest in educational technology

What we offer
- Work on meaningful AI applications
- Access to computational resources and real-world datasets
- Possibility for future collaboration
- Close collaboration with the core tech team of Kollin, as it is part of a bigger research project supported by Vinnova.
- 20K SEK upon completion of the research. 30K SEK for a pair of 2 students.

Read more: https://www.vinnova.se/p/konceptbevis-for-ett-adaptivt-larandesystem-for-ingenjorsstudenter/

Kristofer Espinosa  
CEO, Kollin  
kristofer@kollin.io

Interested? Send your CV and personal letter to contact@kollin.io

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