近日，我系2022级现代技术专业研究生李腾巨撰写的论文“A Personalized Learning Path Recommendation Method for Learning Objects with Diverse Coverage Levels”被教育技术领域国际顶级学术会议International Conference on Artificial Intelligence in Education（AIED）接收。

AIED，全称为人工智能教育国际会议，是一个专门探讨人工智能技术在教育领域中的应用和研究的国际学术会议，由国际人工智能教育协会（International Artificial Intelligence in Education Society）主办，本年度AIED 是该协会举办的第24届会议，也是其成立第30周年。今年的会议主题为“可持续社会中的教育人工智能”。本次会议旨在促进讨论AI如何塑造了各个领域的教育形态，如何推进AI辅助交互式学习系统的科学与工程，以及如何促进其广泛应用。

在人工智能技术不断渗透到各个领域的今天，AIED会议的研究成果对于推动教育技术的发展具有重要意义。本年度会议论文接收率仅为21.11%。

Tengju Li, Xu Wang, Shugang Zhang, Fei Yang, Weigang Lu*(corresponding author). A Personalized Learning Path Recommendation Method for Learning Objects with Diverse Coverage Levels. 24th International Conference on Artificial intelligence in Education. 2023.

Abstract

E-learning has resulted in the proliferation of educationalresources, but challenges remain in providing personalized learning materials to learners amidst an abundance of resources. Previous personalized learning path recommendation (LPR) methods often oversimplified the competency features of learning objects (LOs), rendering them inadequate for LOs with diverse coverage levels. To address this limitation, an improved learning path recommendation framework is proposed that uses a novel graph-based genetic algorithm (GBGA) to optimize the alignment of features between learners and LOs. To evaluate the performance of the method, a series of computational experiments are conducted based on six simulation datasets with different levels of complexity. The results indicate that the proposed method is effective and stable for solving the LPR problem using LOs with diverse coverage levels.