Can ChatGPT pass the Vietnamese National High School Graduation Examination?
Project Overview
The document examines the role of generative AI, particularly ChatGPT, in education, highlighting its performance on the Vietnamese National High School Graduation Examination (VNHSGE). It reports that ChatGPT successfully passed the exam, demonstrating proficiency in a range of subjects such as mathematics, English, physics, chemistry, biology, history, geography, and literature. These findings indicate the promising potential of AI tools to enhance educational experiences. However, the research also underscores the necessity for further exploration of ChatGPT's effectiveness with more complex questions and its applicability across diverse educational settings. Overall, the document emphasizes the transformative potential of AI in education, suggesting that generative AI can significantly benefit both students and educators by improving learning outcomes and engagement.
Key Applications
ChatGPT used to complete the Vietnamese National High School Graduation Examination (VNHSGE)
Context: High school education in Vietnam; target audience includes students preparing for graduation exams.
Implementation: ChatGPT was evaluated on a dataset specifically created from VNHSGE exam questions, including essays and multiple-choice questions.
Outcomes: ChatGPT passed the VNHSGE with an average score of 6-7, demonstrating proficiency in various subjects.
Challenges: Performance varies by subject; some subjects revealed lower scores, indicating a need for improvement in understanding complex questions.
Implementation Barriers
Technical Limitations
ChatGPT struggles with high-order thinking and analytical skills required for solving complex exam questions.
Proposed Solutions: Further research into enhancing AI capabilities for critical thinking and problem-solving tasks is needed.
Contextual Understanding
ChatGPT's performance can differ significantly based on the complexity of questions and cultural context.
Proposed Solutions: Expand training data to include more contextually rich examples and complex exam questions.
Project Team
Xuan-Quy Dao
Researcher
Ngoc-Bich Le
Researcher
Xuan-Dung Phan
Researcher
Bac-Bien Ngo
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Xuan-Quy Dao, Ngoc-Bich Le, Xuan-Dung Phan, Bac-Bien Ngo
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