Exploring the Impact of ChatGPT on Student Interactions in Computer-Supported Collaborative Learning
Project Overview
This document explores the integration of generative AI, specifically ChatGPT, within educational contexts. The research focuses on the impact of ChatGPT on student interactions during asynchronous computer-supported collaborative learning (CSCL), particularly during group brainstorming sessions. Findings reveal that incorporating ChatGPT significantly alters the dynamics of student engagement, leading to a shift in active learning from student-to-student interactions towards interactions with the AI. While the study acknowledges the potential benefits of generative AI in education, it also emphasizes the challenges associated with this shift, highlighting the need for careful consideration of how AI tools are implemented to maximize educational outcomes and maintain the crucial elements of collaborative learning.
Key Applications
ChatGPT as a question-answering agent within a CSCL environment
Context: Graduate-level product engineering course, asynchronous group brainstorming sessions
Implementation: Students in Type A cohorts could tag ChatGPT in their messages. The system used ChatGPT's OpenAI API to retrieve and post responses.
Outcomes: ChatGPT shifted active learning from student-student interactions to student-ChatGPT interactions. Student-ChatGPT messages exhibited a higher level of active learning compared to student-student interactions.
Challenges: ChatGPT diminished the level of active learning interactions between students.
Implementation Barriers
Factual Accuracy/Hallucination
Concerns about factual accuracy and the potential for ChatGPT to generate incorrect or misleading information.
Proposed Solutions: The study suggests that for activities focused on analyzing, evaluating, or creating new ideas, the issues of factual accuracy are less critical.
Academic Integrity
Concerns about the potential for students to use ChatGPT to circumvent the learning process and compromise academic integrity.
Proposed Solutions: Not specifically addressed in the text, but the focus on creative tasks may mitigate this concern.
Project Team
Han Kyul Kim
Researcher
Shriniwas Nayak
Researcher
Aleyeh Roknaldin
Researcher
Xiaoci Zhang
Researcher
Marlon Twyman
Researcher
Stephen Lu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Han Kyul Kim, Shriniwas Nayak, Aleyeh Roknaldin, Xiaoci Zhang, Marlon Twyman, Stephen Lu
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gemini-2.0-flash-lite