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Found 18 papers, 2 papers with code
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
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.
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.
