24 citations · 43 across the 17 of their papers we have counts for
8 papers · 1 filter
Addressing Situated Teaching Needs: A Multi-Agent Framework for Automated Slide Adaptation
Binglin Liu, Yucheng Wang, Zheyuan Zhang +5
The adaptation of teaching slides to instructors' situated teaching needs, including pedagogical styles and their students' context, is a critical yet time-consuming task for educa…
Personalized Learning Path Planning with Goal-Driven Learner State Modeling
Joy Jia Yin Lim, Ye He, Jifan Yu +7
Personalized Learning Path Planning (PLPP) aims to design adaptive learning paths that align with individual goals. While large language models (LLMs) show potential in personalizi…
Learning in Context: Personalizing Educational Content with Large Language Models to Enhance Student Learning
Joy Jia Yin Lim, Daniel Zhang-Li, Jifan Yu +7
Standardized, one-size-fits-all educational content often fails to connect with students' individual backgrounds and interests, leading to disengagement and a perceived lack of rel…
Handling Students Dropouts in an LLM-driven Interactive Online Course Using Language Models
Yuanchun Wang, Yiyang Fu, Jifan Yu +7
Interactive online learning environments, represented by Massive AI-empowered Courses (MAIC), leverage LLM-driven multi-agent systems to transform passive MOOCs into dynamic, text-…
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style
Debdeep Sanyal, Agniva Maiti, Umakanta Maharana +4
Effective teaching requires adapting instructional strategies to accommodate the diverse cognitive and behavioral profiles of students, a persistent challenge in education and teac…
LecEval: An Automated Metric for Multimodal Knowledge Acquisition in Multimedia Learning
Joy Lim Jia Yin, Daniel Zhang-Li, Jifan Yu +8
Evaluating the quality of slide-based multimedia instruction is challenging. Existing methods like manual assessment, reference-based metrics, and large language model evaluators f…
LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models
Shangqing Tu, Yucheng Wang, Daniel Zhang-Li +8
Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle to generate coherent outputs beyond 1,000…
Which Type of Students can LLMs Act? Investigating Authentic Simulation with Graph-based Human-AI Collaborative System
Haoxuan Li, Jifan Yu, Xin Cong +6
While rapid advances in large language models (LLMs) are reshaping data-driven intelligent education, accurately simulating students remains an important but challenging bottleneck…