Multilingual Performance of a Multimodal Artificial Intelligence System on Multisubject Physics Concept Inventories
Project Overview
The document explores the application of generative AI, particularly GPT-4o, in the field of physics education, emphasizing its capabilities in generating educational materials, providing personalized feedback, and conducting assessments. Research findings indicate that GPT-4o excels in addressing physics concept inventories, outperforming average undergraduate students, though it faces challenges with visual interpretation tasks. Additionally, the implementation of OpenAI's models in educational research demonstrates their potential to assist in answering complex physics questions and highlights their effectiveness across multilingual contexts. The insights derived from AI-generated responses are valuable for identifying student difficulties and refining educational methodologies. Nevertheless, the document also underscores the necessity for students and educators to critically evaluate AI outputs, taking into account issues like language disparities that can affect AI performance. Overall, the integration of generative AI in physics education presents promising opportunities for enhancing learning experiences, while also calling for careful consideration of its limitations and the importance of human oversight in the educational process.
Key Applications
AI-assisted physics concept evaluation and data augmentation
Context: University physics education and high school education, specifically designed for undergraduate students and students in technically-oriented high schools. The AI system has been tested on physics concept inventories, including multiple-choice questions, to simulate real student experiences and improve understanding of physics concepts.
Implementation: Implemented through API calls to AI models like GPT-4o and ChatGPT, which process physics concept inventories and multiple-choice questions, providing structured JSON outputs with reasoning. The system is designed to analyze and interpret content, delivering educational insights effectively.
Outcomes: Demonstrated enhanced accuracy in responses to physics concept inventories, with AI outperforming average undergraduate students in assessments. The implementation offers valuable insights into common difficulties students face with physics concepts, making it a powerful educational tool.
Challenges: Limited ability to interpret visual information and performance disparities across languages. Variability in AI responses due to its probabilistic nature, with potential inaccuracies in non-English responses.
Implementation Barriers
Technological & Language Barrier
GPT-4o struggles with tasks that require visual interpretation and may produce different results across languages. Performance may vary significantly across different languages, affecting correctness.
Proposed Solutions: Enhance multimodal processing components, improve training data diversity, and develop assessments tailored for AI capabilities. Use mixed-language outputs to enhance understanding and accuracy in responses.
Educational Equity
Performance disparities in AI across different languages may reinforce existing inequities in access to educational resources.
Proposed Solutions: Develop AI systems that are trained on diverse language datasets to ensure equitable access.
Technical Barrier
AI's probabilistic nature leads to variability in responses, which may affect reliability.
Proposed Solutions: Multiple independent evaluations of each question to improve reliability of outputs.
Project Team
Gerd Kortemeyer
Researcher
Marina Babayeva
Researcher
Giulia Polverini
Researcher
Ralf Widenhorn
Researcher
Bor Gregorcic
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Gerd Kortemeyer, Marina Babayeva, Giulia Polverini, Ralf Widenhorn, Bor Gregorcic
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai