2 citations · 4 across the 4 of their papers we have counts for
4 papers
Generative Adversarial Networks for Imputing Sparse Learning Performance
Liang Zhang, Mohammed Yeasin, Jionghao Lin +2
Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring Systems (ITSs) is crucial for tracking and assessing the learners' progress a…
Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
Liang Zhang, Jionghao Lin, Conrad Borchers +4
Intelligent Tutoring Systems (ITSs) have significantly enhanced adult literacy training, a key factor for societal participation, employment opportunities, and lifelong learning. O…
3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems
Liang Zhang, Jionghao Lin, Conrad Borchers +2
Learning performance data (e.g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level. However, the learning performance data c…
NEOLAF, an LLM-powered neural-symbolic cognitive architecture
Richard Jiarui Tong, Cassie Chen Cao, Timothy Xueqian Lee +8
This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents.…