SortingHat: Redefining Operating Systems Education with a Tailored Digital Teaching Assistant
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914619054882816 |
|---|---|
| author | Zhang, Yifan Zhao, Xinkui Wang, Zuxin Zhou, Zhengyi Chen, Guanjie Deng, Shuiguang Yin, Jianwei |
| author_facet | Zhang, Yifan Zhao, Xinkui Wang, Zuxin Zhou, Zhengyi Chen, Guanjie Deng, Shuiguang Yin, Jianwei |
| contents | Operating Systems (OS) courses are among the most challenging in computer science education due to the complexity of internal structures and the diversity of running environments. Traditional teaching methods often fail to address the diverse backgrounds, learning speeds, and practical needs of students. To tackle these challenges, we present SortingHat, a personalized digital teaching assistant tailored specifically for OS education. SortingHat integrates advanced AI technologies, including a retrieval augmented generation (RAG) framework and multi agent reinforcement learning (MARL), to deliver adaptive, scalable, and effective educational support. SortingHat features a 3D digital human interface powered by large language models (LLMs) to provide personalized, empathetic, and context aware guidance. It generates tailored exercises based on each student's learning history and academic performance, reinforcing weak areas and challenging advanced concepts. Additionally, the system incorporates a robust evaluation pipeline that ensures fair, consistent, and unbiased grading of student submissions while delivering personalized, actionable feedback for improvement. By combining personalized guidance, adaptive content creation, and automated assessment, SortingHat transforms OS education into an engaging, immersive, and scalable experience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00015 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SortingHat: Redefining Operating Systems Education with a Tailored Digital Teaching Assistant Zhang, Yifan Zhao, Xinkui Wang, Zuxin Zhou, Zhengyi Chen, Guanjie Deng, Shuiguang Yin, Jianwei Human-Computer Interaction Artificial Intelligence Computers and Society Emerging Technologies Operating Systems (OS) courses are among the most challenging in computer science education due to the complexity of internal structures and the diversity of running environments. Traditional teaching methods often fail to address the diverse backgrounds, learning speeds, and practical needs of students. To tackle these challenges, we present SortingHat, a personalized digital teaching assistant tailored specifically for OS education. SortingHat integrates advanced AI technologies, including a retrieval augmented generation (RAG) framework and multi agent reinforcement learning (MARL), to deliver adaptive, scalable, and effective educational support. SortingHat features a 3D digital human interface powered by large language models (LLMs) to provide personalized, empathetic, and context aware guidance. It generates tailored exercises based on each student's learning history and academic performance, reinforcing weak areas and challenging advanced concepts. Additionally, the system incorporates a robust evaluation pipeline that ensures fair, consistent, and unbiased grading of student submissions while delivering personalized, actionable feedback for improvement. By combining personalized guidance, adaptive content creation, and automated assessment, SortingHat transforms OS education into an engaging, immersive, and scalable experience. |
| title | SortingHat: Redefining Operating Systems Education with a Tailored Digital Teaching Assistant |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society Emerging Technologies |
| url | https://arxiv.org/abs/2606.00015 |