26 citations · 106 across the 11 of their papers we have counts for
11 papers
Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring
Xuansheng Wu, Padmaja Pravin Saraf, Gyeonggeon Lee +3
Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans a…
Realizing Visual Question Answering for Education: GPT-4V as a Multimodal AI
Gyeong-Geon Lee, Xiaoming Zhai
Educational scholars have analyzed various image data acquired from teaching and learning situations, such as photos that shows classroom dynamics, students' drawings with regard t…
G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in German
Ehsan Latif, Gyeong-Geon Lee, Knut Neumann +2
The advancement of natural language processing has paved the way for automated scoring systems in various languages, such as German (e.g., German BERT [G-BERT]). Automatically scor…
Using ChatGPT for Science Learning: A Study on Pre-service Teachers' Lesson Planning
Gyeong-Geon Lee, Xiaoming Zhai
Despite the buzz around ChatGPT's potential, empirical studies exploring its actual utility in the classroom for learning remain scarce. This study aims to fill this gap by analyzi…
Gemini Pro Defeated by GPT-4V: Evidence from Education
Gyeong-Geon Lee, Ehsan Latif, Lehong Shi +1
This study compared the classification performance of Gemini Pro and GPT-4V in educational settings. Employing visual question answering (VQA) techniques, the study examined both m…
NERIF: GPT-4V for Automatic Scoring of Drawn Models
Gyeong-Geon Lee, Xiaoming Zhai
Scoring student-drawn models is time-consuming. Recently released GPT-4V provides a unique opportunity to advance scientific modeling practices by leveraging the powerful image pro…
Collaborative Learning with Artificial Intelligence Speakers (CLAIS): Pre-Service Elementary Science Teachers' Responses to the Prototype
Gyeong-Geon Lee, Seonyeong Mun, Myeong-Kyeong Shin +1
This research aims to demonstrate that AI can function not only as a tool for learning, but also as an intelligent agent with which humans can engage in collaborative learning (CL)…
Multimodality of AI for Education: Towards Artificial General Intelligence
Gyeong-Geon Lee, Lehong Shi, Ehsan Latif +10
This paper presents a comprehensive examination of how multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intellige…
Applying Large Language Models and Chain-of-Thought for Automatic Scoring
Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu +2
This study investigates the application of large language models (LLMs), specifically GPT-3.5 and GPT-4, with Chain-of-Though (CoT) in the automatic scoring of student-written resp…
Using GPT-4 to Augment Unbalanced Data for Automatic Scoring
Luyang Fang, Gyeong-Geon Lee, Xiaoming Zhai
Machine learning-based automatic scoring faces challenges with unbalanced student responses across scoring categories. To address this, we introduce a novel text data augmentation…
Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning
Chen Cao, Zijian Ding, Gyeong-Geon Lee +3
This study explores the integration of generative artificial intelligence (AI), specifically large language models, with multi-modal analogical reasoning as an innovative approach…