10 citations · 35 across the 8 of their papers we have counts for
14 papers
Combining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Support
Devika Venugopalan, Ziwen Yan, Conrad Borchers +2
Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student aca…
Do Tutors Learn from Equity Training and Can Generative AI Assess It?
Danielle R. Thomas, Conrad Borchers, Sanjit Kakarla +5
Equity is a core concern of learning analytics. However, applications that teach and assess equity skills, particularly at scale are lacking, often due to barriers in evaluating la…
Does Multiple Choice Have a Future in the Age of Generative AI? A Posttest-only RCT
Danielle R. Thomas, Conrad Borchers, Sanjit Kakarla +5
The role of multiple-choice questions (MCQs) as effective learning tools has been debated in past research. While MCQs are widely used due to their ease in grading, open response q…
RAMO: Retrieval-Augmented Generation for Enhancing MOOCs Recommendations
Jiarui Rao, Jionghao Lin
Massive Open Online Courses (MOOCs) have significantly enhanced educational accessibility by offering a wide variety of courses and breaking down traditional barriers related to ge…
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…
GPTutor: Great Personalized Tutor with Large Language Models for Personalized Learning Content Generation
Eason Chen, Jia-En Lee, Jionghao Lin +1
We developed GPTutor, a pioneering web application designed to revolutionize personalized learning by leveraging the capabilities of Generative AI at scale. GPTutor adapts educatio…