128 citations · 262 across the 31 of their papers we have counts for
34 papers
Train4Merge: A Controlled Single-Teacher Study of RL vs. SFT Teachers for OPD-Based Model Merging
Jingyuan Huang, Zuming Huang, Yucheng Shi +4
Domain experts trained from a shared checkpoint can transfer their specialized capabilities to a single student through on-policy distillation (OPD). Existing research primarily fo…
Confidence-Aware Automated Assessment of Student-Drawn Scientific Models
Luyang Fang, Yingchuan Zhang, Jongchan Park +3
Student-generated drawings are widely used in science education to assess learners' conceptual understanding in modeling-based tasks aligned with the Next Generation Science Standa…
Trust the Right Teacher: Quality-Aware Self-Distillation for GUI Grounding
Jingyuan Huang, Zuming Huang, Yucheng Shi +4
Graphical user interface (GUI) grounding requires vision-language models (VLMs) to identify small target elements in high-resolution screenshots and predict precise screen coordina…
Learnable Assessment Skills for LLM-based Automated Scoring: Rubric Construction via Iterative Optimization
Yun Wang, Xin Xia, Xuansheng Wu +2
LLM-based automated scoring approaches near-human performance, but scaling to new tasks remains bottlenecked by the per-item human configuration of upstream stages such as rubric c…
Generative AI as a Design Variable: An Evidence-Centered Framework for Principled Governance in STEM Assessment
Yizhu Gao, Zhongzhou Chen, Min Li +1
Generative Artificial Intelligence (GenAI) presents a governance challenge for STEM assessment. Unrestricted GenAI access enables task outsourcing that undermines the validity of t…
Palette: A Modular, Controllable, and Efficient Framework for On-demand Authorized Safety Alignment Relaxation in LLMs
Qitao Tan, Xiaoying Song, Arman Akbari +7
Current safety alignment of foundation models largely follows a \emph{one-size-fits-all} paradigm, applying the same refusal policy across users and contexts. As a result, models m…
Using Learning Progressions to Guide AI Feedback for Science Learning
Xin Xia, Nejla Yuruk, Yun Wang +1
Generative artificial intelligence (AI) offers scalable support for formative feedback, yet most AI-generated feedback relies on task-specific rubrics authored by domain experts. W…
BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation
Yun Wang, Xuansheng Wu, Jingyuan Huang +3
In the field of educational assessment, automated scoring systems increasingly rely on deep learning and large language models (LLMs). However, these systems face significant risks…
SketchMind: A Multi-Agent Cognitive Framework for Assessing Student-Drawn Scientific Sketches
Ehsan Latif, Zirak Khan, Xiaoming Zhai
Scientific sketches (e.g., models) offer a powerful lens into students' conceptual understanding, yet AI-powered automated assessment of such free-form, visually diverse artifacts…
Artificial Intelligence Bias on English Language Learners in Automatic Scoring
Shuchen Guo, Yun Wang, Jichao Yu +7
This study investigated potential scoring biases and disparities toward English Language Learners (ELLs) when using automatic scoring systems for middle school students' written re…
Efficient Multi-Task Inferencing: Model Merging with Gromov-Wasserstein Feature Alignment
Luyang Fang, Ehsan Latif, Haoran Lu +3
Automatic scoring of student responses enhances efficiency in education, but deploying a separate neural network for each task increases storage demands, maintenance efforts, and r…
Advancing Education through Tutoring Systems: A Systematic Literature Review
Vincent Liu, Ehsan Latif, Xiaoming Zhai
This study systematically reviews the transformative role of Tutoring Systems, encompassing Intelligent Tutoring Systems (ITS) and Robot Tutoring Systems (RTS), in addressing globa…
Privacy-Preserved Automated Scoring using Federated Learning for Educational Research
Ehsan Latif, Xiaoming Zhai
Data privacy remains a critical concern in educational research, requiring strict adherence to ethical standards and regulatory protocols. While traditional approaches rely on anon…
Fine-tuning ChatGPT for Automatic Scoring of Written Scientific Explanations in Chinese
Jie Yang, Ehsan Latif, Yuze He +1
The development of explanations for scientific phenomena is essential in science assessment, but scoring student-written explanations remains challenging and resource-intensive. La…
Human-Centered Design for AI-based Automatically Generated Assessment Reports: A Systematic Review
Ehsan Latif, Ying Chen, Xiaoming Zhai +1
This paper provides a comprehensive review of the design and implementation of automatically generated assessment reports (AutoRs) for formative use in K-12 Science, Technology, En…
Efficient Multi-Task Inferencing with a Shared Backbone and Lightweight Task-Specific Adapters for Automatic Scoring
Ehsan Latif, Xiaoming Zhai
The integration of Artificial Intelligence (AI) in education requires scalable and efficient frameworks that balance performance, adaptability, and cost. This paper addresses these…
Using Generative AI and Multi-Agents to Provide Automatic Feedback
Shuchen Guo, Ehsan Latif, Yifan Zhou +2
This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts, particularly for student constructed responses in sc…
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…
PhysicsAssistant: An LLM-Powered Interactive Learning Robot for Physics Lab Investigations
Ehsan Latif, Ramviyas Parasuraman, Xiaoming Zhai
Robot systems in education can leverage Large language models' (LLMs) natural language understanding capabilities to provide assistance and facilitate learning. This paper proposes…
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
Xuansheng Wu, Haiyan Zhao, Yaochen Zhu +9
Explainable AI (XAI) refers to techniques that provide human-understandable insights into the workings of AI models. Recently, the focus of XAI is being extended toward explaining…
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…
Taking the Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education
Arne Bewersdorff, Christian Hartmann, Marie Hornberger +6
The integration of Artificial Intelligence (AI), particularly Large Language Model (LLM)-based systems, in education has shown promise in enhancing teaching and learning experience…