SortingHat: Redefining Operating Systems Education with a Tailored Digital Teaching Assistant

• URL: http://arxiv.org/abs/2606.00015v1
• Date: Mon, 13 Apr 2026 13:17:54 GMT
• Title: SortingHat: Redefining Operating Systems Education with a Tailored Digital Teaching Assistant
• Abstract summary: Operating Systems (OS) courses are among the most challenging in computer science education.<n>Traditional teaching methods fail to address the diverse backgrounds, learning speeds, and practical needs of students.<n>We present SortingHat, a personalized digital teaching assistant tailored specifically for OS education.
• Score: 41.096524789483205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models [59.693733170193944]
  Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings.<n>Recent reinforcement learning approaches address this limitation but face two critical challenges.<n>We propose the Unidirectional Cognitive Optimization (UCO) method to address these challenges.
  arXiv  Detail & Related papers  (2025-11-12T01:27:02Z)
• Comparative Analysis of STEM and non-STEM Teachers' Needs for Integrating AI into Educational Environments [0.6138671548064355]
  This study explores how educational platforms can be improved by incorporating AI and analytics features.<n>We interviewed 8 K-12 teachers and asked their practices and needs while using any block-based programming (BBP) platform in their classes.
  arXiv  Detail & Related papers  (2025-09-18T16:20:18Z)
• PAPPL: Personalized AI-Powered Progressive Learning Platform [4.427312315598971]
  This paper introduces the Personalized AI-Powered Progressive Learning (PAPPL) platform, an advanced Intelligent Tutoring System (ITS) designed specifically for engineering education.<n>PAPPL integrates core ITS components including the expert module, student module, tutor module, and user interface, and utilizes GPT-4o, a sophisticated large language model (LLM)<n>The system uniquely records student attempts, detects recurring misconceptions, and generates progressively targeted feedback, providing personalized assistance that adapts dynamically to each student's learning profile.
  arXiv  Detail & Related papers  (2025-08-18T00:35:24Z)
• Enhancing tutoring systems by leveraging tailored promptings and domain knowledge with Large Language Models [2.5362697136900563]
  AI-driven tools like ChatGPT and Intelligent Tutoring Systems (ITS) have enhanced learning experiences through personalisation and flexibility.<n>ITSs can adapt to individual learning needs and provide customised feedback based on a student's performance, cognitive state, and learning path.<n>Our research aims to address these gaps by integrating skill-aligned feedback via Retrieval Augmented Generation (RAG) into prompt engineering for Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2025-05-02T02:30:39Z)
• Inclusive STEAM Education: A Framework for Teaching Cod-2 ing and Robotics to Students with Visually Impairment Using 3 Advanced Computer Vision [0.0]
  This paper presents a framework that leverages pre-constructed robots and algorithms, such as maze-solving techniques, within an accessible learning environment.<n>The proposed system employs Contrastive Language-Image Pre-training (CLIP) to process global camera-captured maze layouts.<n>Students issue verbal commands, which are refined through CLIP, while robot-mounted stereo cameras provide real-time data processed via Simultaneous SLAM.
  arXiv  Detail & Related papers  (2025-03-06T17:15:12Z)
• Dynamic Skill Adaptation for Large Language Models [78.31322532135272]
  We present Dynamic Skill Adaptation (DSA), an adaptive and dynamic framework to adapt novel and complex skills to Large Language Models (LLMs)<n>For every skill, we utilize LLMs to generate both textbook-like data which contains detailed descriptions of skills for pre-training and exercise-like data which targets at explicitly utilizing the skills to solve problems for instruction-tuning.<n>Experiments on large language models such as LLAMA and Mistral demonstrate the effectiveness of our proposed methods in adapting math reasoning skills and social study skills.
  arXiv  Detail & Related papers  (2024-12-26T22:04:23Z)
• YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
  This paper introduces YODA, a teacher-student progressive learning framework. It emulates the teacher-student education process to improve the efficacy of model fine-tuning. Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
  arXiv  Detail & Related papers  (2024-01-28T14:32:15Z)
• Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
  This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs) The system is into three inter-connected core processes-interaction, reflection, and reaction. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
  arXiv  Detail & Related papers  (2023-09-15T02:42:03Z)
• Generating Language Corrections for Teaching Physical Control Tasks [21.186109830294072]
  CORGI is a model trained to generate language corrections for physical control tasks. We show that CORGI can (i) generate valid feedback for novel student trajectories, (ii) outperform baselines on domains with novel control dynamics, and (iii) improve student learning in an interactive drawing task.
  arXiv  Detail & Related papers  (2023-06-12T10:31:16Z)
• MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale [103.7609761511652]
  We show how a large-scale collective robotic learning system can acquire a repertoire of behaviors simultaneously. New tasks can be continuously instantiated from previously learned tasks. We train and evaluate our system on a set of 12 real-world tasks with data collected from 7 robots.
  arXiv  Detail & Related papers  (2021-04-16T16:38:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.