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Found 35 papers, 3 papers with code
Efficient Multi-Task Inferencing: Model Merging with Gromov-Wasserstein Feature Alignment
The improvements in micro F1 and per-label accuracy were statistically significant compared to GPT-o1-based merging (p=0. 04, p=0. 01).
Interpreting and Steering LLMs with Mutual Information-based Explanations on Sparse Autoencoders
Large language models (LLMs) excel at handling human queries, but they can occasionally generate flawed or unexpected responses.
Self-Regularization with Latent Space Explanations for Controllable LLM-based Classification
In training the classification model, we propose a simple and effective regularizer, by minimizing the similarity between the classifier weights and the identified unintended feature, to remove the impacts of these unintended features toward classification.
Fine-tuning ChatGPT for Automatic Scoring of Written Scientific Explanations in Chinese
These findings demonstrate the effectiveness of LLMs in automatic scoring within a Chinese context and emphasize the importance of linguistic features and reasoning complexity in fine-tuning scoring models for educational assessments.
Efficient Multi-Task Inferencing with a Shared Backbone and Lightweight Task-Specific Adapters for Automatic Scoring
The integration of Artificial Intelligence (AI) in education requires scalable and efficient frameworks that balance performance, adaptability, and cost.
Can OpenAI o1 outperform humans in higher-order cognitive thinking?
For scientific reasoning, it achieved near-perfect performance (mean = 0. 99, SD = 0. 12) on the TOSLS,, exceeding the highest human scores of 0. 85, SD = 0. 13 (z = 1. 78).
Foundation Models for Low-Resource Language Education (Vision Paper)
Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics.
Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation
Our comparison with the zero-shot performance of GPT-4o and LLaMA 3. 1 405B, highlights the significant challenges these models face when translating into low-resource languages.
Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios
Artificial Intelligence (AI) has become essential in modern healthcare, with large language models (LLMs) offering promising advances in clinical decision-making.
Using Generative AI and Multi-Agents to Provide Automatic Feedback
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 science assessments.
Large Language Models for Manufacturing
The rapid advances in Large Language Models (LLMs) have the potential to transform manufacturing industry, offering new opportunities to optimize processes, improve efficiency, and drive innovation.
A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education
This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning.
Transforming Teachers' Roles and Agencies in the Era of Generative AI: Perceptions, Acceptance, Knowledge, and Practices
This paper explores the transformative impact of Generative Artificial Intelligence (GenAI) on teachers' roles and agencies in education, presenting a comprehensive framework that addresses teachers' perceptions, knowledge, acceptance, and practices of GenAI.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring
Specifically, we prompt LLMs to generate analytic rubrics that they use to assign scores and study the alignment gap with human grading rubrics.
Realizing Visual Question Answering for Education: GPT-4V as a Multimodal AI
In this paper, chapter II reviews the development of VQA techniques, which primes with the release of GPT-4V.
AI and Machine Learning for Next Generation Science Assessments
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.
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
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.
G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in German
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]).
Can generative AI and ChatGPT outperform humans on cognitive-demanding problem-solving tasks in science?
We compared the performance of ChatGPT and GPT-4 on 2019 NAEP science assessments with students by cognitive demands of the items.
Taking the Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education
The integration of Artificial Intelligence (AI), particularly Large Language Model (LLM)-based systems, in education has shown promise in enhancing teaching and learning experiences.
Gemini Pro Defeated by GPT-4V: Evidence from Education
This study compared the classification performance of Gemini Pro and GPT-4V in educational settings.
Knowledge Distillation of LLM for Automatic Scoring of Science Education Assessments
We compared accuracy with state-of-the-art (SOTA) distilled models, TinyBERT, and artificial neural network (ANN) models.
Collaborative Learning with Artificial Intelligence Speakers (CLAIS): Pre-Service Elementary Science Teachers' Responses to the Prototype
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.
Multimodality of AI for Education: Towards Artificial General Intelligence
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.
Automatic Scoring of Students' Science Writing Using Hybrid Neural Network
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.
Applying Large Language Models and Chain-of-Thought for Automatic Scoring
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.
NERIF: GPT-4V for Automatic Scoring of Drawn Models
The results of this study show that utilizing GPT-4V for automatic scoring of student-drawn models is promising.
Using GPT-4 to Augment Unbalanced Data for Automatic Scoring
Notably, we found that a varying amount of augmented data (20%-40%) was needed to obtain stable improvement for automatic scoring.
Fine-tuning ChatGPT for Automatic Scoring
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.
Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning
Our study demonstrates the potential of applying large language models to educational practice on STEM subjects.
AGI: Artificial General Intelligence for Education
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.
Context Matters: A Strategy to Pre-train Language Model for Science Education
However, the ideal type of data to contextualize pre-trained language model and improve the performance in automatically scoring student written responses remains unclear.
Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education
Developing models to automatically score students' written responses to science problems is critical for science education.
Pseudo AI Bias
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.
