no code implementations • 12 May 2024 • Gyeong-Geon Lee, Xiaoming Zhai
In this paper, chapter II reviews the development of VQA techniques, which primes with the release of GPT-4V.
no code implementations • 23 Apr 2024 • Xiaoming Zhai
The paper begins with a discussion of the Framework for K-12 Science Education, which calls for a shift from conceptual learning to knowledge-in-use.
1 code implementation • 13 Mar 2024 • Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi, Fan Yang, Tianming Liu, Xiaoming Zhai, Wenlin Yao, Jundong Li, Mengnan Du, Ninghao Liu
Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI.
no code implementations • 9 Feb 2024 • Ehsan Latif, Gyeong-Geon Lee, Knut Neuman, Tamara Kastorff, Xiaoming Zhai
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]).
no code implementations • 7 Jan 2024 • Xiaoming Zhai, Matthew Nyaaba, Wenchao Ma
We compared the performance of ChatGPT and GPT-4 on 2019 NAEP science assessments with students by cognitive demands of the items.
no code implementations • 1 Jan 2024 • Arne Bewersdorff, Christian Hartmann, Marie Hornberger, Kathrin Seßler, Maria Bannert, Enkelejda Kasneci, Gjergji Kasneci, Xiaoming Zhai, Claudia Nerdel
The integration of Artificial Intelligence (AI), particularly Large Language Model (LLM)-based systems, in education has shown promise in enhancing teaching and learning experiences.
no code implementations • 27 Dec 2023 • Gyeong-Geon Lee, Ehsan Latif, Lehong Shi, Xiaoming Zhai
This study compared the classification performance of Gemini Pro and GPT-4V in educational settings.
no code implementations • 26 Dec 2023 • Ehsan Latif, Luyang Fang, Ping Ma, Xiaoming Zhai
We compared accuracy with state-of-the-art (SOTA) distilled models, TinyBERT, and artificial neural network (ANN) models.
no code implementations • 20 Dec 2023 • Gyeong-Geon Lee, Seonyeong Mun, Myeong-Kyeong Shin, Xiaoming Zhai
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) to change epistemic practices in science classrooms.
no code implementations • 10 Dec 2023 • Gyeong-Geon Lee, Lehong Shi, Ehsan Latif, Yizhu Gao, Arne Bewersdorff, Matthew Nyaaba, Shuchen Guo, Zihao Wu, Zhengliang Liu, Hui Wang, Gengchen Mai, Tiaming Liu, Xiaoming Zhai
This paper presents a comprehensive examination of how multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intelligence (AGI) in educational contexts.
no code implementations • 2 Dec 2023 • Ehsan Latif, Xiaoming Zhai
We also have observed that HNN is x2 more efficient in training and inferencing than BERT and has comparable efficiency to the lightweight but less accurate Naive Bayes model.
no code implementations • 30 Nov 2023 • Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu, Ninghao Liu, Xiaoming Zhai
We found a more balanced accuracy across different proficiency categories when CoT was used with a scoring rubric, highlighting the importance of domain-specific reasoning in enhancing the effectiveness of LLMs in scoring tasks.
no code implementations • 21 Nov 2023 • Gyeong-Geon Lee, Xiaoming Zhai
The results of this study show that utilizing GPT-4V for automatic scoring of student-drawn models is promising.
no code implementations • 25 Oct 2023 • Luyang Fang, Gyeong-Geon Lee, Xiaoming Zhai
The average maximum increase observed across two items is: 3. 5% for accuracy, 30. 6% for precision, 21. 1% for recall, and 24. 2% for F1 score.
no code implementations • 16 Oct 2023 • Ehsan Latif, Xiaoming Zhai
In this study, we fine-tuned GPT-3. 5 on six assessment tasks with a diverse dataset of middle-school and high-school student responses and expert scoring.
no code implementations • 21 Aug 2023 • Chen Cao, Zijian Ding, Gyeong-Geon Lee, Jiajun Jiao, Jionghao Lin, Xiaoming Zhai
Our study demonstrates the potential of applying large language models to educational practice on STEM subjects.
no code implementations • 24 Apr 2023 • Ehsan Latif, Gengchen Mai, Matthew Nyaaba, Xuansheng Wu, Ninghao Liu, Guoyu Lu, Sheng Li, Tianming Liu, Xiaoming Zhai
AGI, driven by the recent large pre-trained models, represents a significant leap in the capability of machines to perform tasks that require human-level intelligence, such as reasoning, problem-solving, decision-making, and even understanding human emotions and social interactions.
no code implementations • 27 Jan 2023 • Zhengliang Liu, Xinyu He, Lei Liu, Tianming Liu, Xiaoming Zhai
However, the ideal type of data to contextualize pre-trained language model and improve the performance in automatically scoring student written responses remains unclear.
1 code implementation • 20 Jan 2023 • Xuansheng Wu, Xinyu He, Tianming Liu, Ninghao Liu, Xiaoming Zhai
Developing models to automatically score students' written responses to science problems is critical for science education.
no code implementations • 14 Oct 2022 • Xiaoming Zhai, Joseph Krajcik
Pseudo Artificial Intelligence bias (PAIB) is broadly disseminated in the literature, which can result in unnecessary AI fear in society, exacerbate the enduring inequities and disparities in access to and sharing the benefits of AI applications, and waste social capital invested in AI research.