Leveraging Explainable AI to Analyze Researchers' Aspect-Based Sentiment about ChatGPT
Project Overview
The document explores the integration of generative AI, particularly ChatGPT, in education, focusing on its advantages and challenges. It underscores the significance of sentiment analysis in gauging user perceptions of ChatGPT, employing a methodology that incorporates Explainable AI for Aspect-Based Sentiment Analysis (ABSA) to evaluate research articles. The findings reveal a predominantly positive sentiment regarding ChatGPT's contributions to educational environments, highlighting its potential to enhance learning experiences, facilitate personalized tutoring, and streamline administrative tasks. However, the document also addresses ethical considerations and limitations surrounding its use, such as issues of academic integrity and the necessity for responsible implementation. Overall, the analysis suggests that while generative AI like ChatGPT can be a valuable asset in education, careful attention must be paid to its ethical implications and the need for guidelines to ensure its effective and responsible use in academic settings.
Key Applications
Use of ChatGPT in academic settings for collaborative learning, intelligent tutoring systems, automated assessment, and personalized learning.
Context: Higher education, targeting both teachers and students.
Implementation: Sentiment analysis of research papers discussing the impact of AI in education, leveraging models like nlptown and yangheng for sentiment classification.
Outcomes: Positive sentiment towards ChatGPT's applications in education, recognition of its potential benefits, and suggestions for its ethical use.
Challenges: Ethical concerns related to the misuse of AI, the need for safeguards, and the limitations of sentiment analysis models in capturing nuanced sentiments.
Implementation Barriers
Data availability
Lack of labeled datasets for training sentiment analysis models specific to the education domain.
Proposed Solutions: Applying transfer learning techniques and leveraging pre-trained models to improve sentiment analysis performance.
Interpretation complexity
Challenges in accurately interpreting sentiment in long texts where context shifts can occur.
Proposed Solutions: Using Explainable AI techniques, such as SHAP, to visualize sentiment influences and improve model transparency.
Project Team
Shilpa Lakhanpal
Researcher
Ajay Gupta
Researcher
Rajeev Agrawal
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Shilpa Lakhanpal, Ajay Gupta, Rajeev Agrawal
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