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Found 21 papers, 5 papers with code
Artificial Intelligence Bias on English Language Learners in Automatic Scoring
This study investigated potential scoring biases and disparities toward English Language Learners (ELLs) when using automatic scoring systems for middle school students' written responses to science assessments.
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions
Despite substantial progress, open challenges remain in preserving emergent reasoning and linguistic diversity, enabling efficient adaptation to continually evolving teacher models and datasets, and establishing comprehensive evaluation protocols.
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).
Privacy-Preserved Automated Scoring using Federated Learning for Educational Research
Data privacy remains a critical concern in educational research, requiring strict adherence to ethical standards and regulatory protocols.
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).
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.
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.
Object-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots
Classification of different object surface material types can play a significant role in the decision-making algorithms for mobile robots and autonomous vehicles.
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.
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]).
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.
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.
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.
SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems
The availability of accurate localization is critical for multi-robot exploration strategies; noisy or inconsistent localization causes failure in meeting exploration objectives.
Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration
The results demonstrate significantly higher coverage accuracy and efficiency while reducing costs and overlaps even in high packet loss and low communication range scenarios.
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.
