Year: 2025
Type: article
Abstract: This paper presents SP-TeachLLM, a novel framework that leverages large language models (LLMs) to deliver intelligent tutoring for computer science education. SP-TeachLLM integrates advanced AI techniques with established educational theories to enable personalized and adaptive learning experiences. Its core innovation lies in a multi-module collaborative architecture that encompasses curriculum decomposition, multi-strategy generation, reflective learning, and memory augmentation. Comprehensive experiments are conducted to evaluate the system’s effectiveness in enhancing knowledge mastery, problem-solving ability, and teaching performance. The results demonstrate that SP-TeachLLM significantly outperforms conventional approaches, providing valuable insights into the application of AI in education and advancing the development of next-generation intelligent tutoring systems.
Source: Information
DOI: 10.3390/info16121045
Authors: Sarah Huang, Yinggang Sun, Xiangzhan Yu
Institutions: Shanghai American School, Harbin Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4416848201