Student engagement in collaborative learning with AI agents in an LLM-empowered learning environment: A cluster analysis
Project Overview
The document explores the role of generative AI, specifically large language models (LLMs), in enhancing educational experiences, particularly in collaborative learning settings. Through the examination of student interactions with AI agents in a course powered by LLMs, the study categorizes learners into three types: active questioners, responsive navigators, and silent listeners. Each type demonstrates unique engagement patterns with AI, which underscores the necessity for adaptive educational systems that can cater to the varied learning styles of students. The findings suggest that leveraging LLMs can significantly boost student engagement and participation, thereby fostering personalized learning experiences. Ultimately, the research emphasizes the potential of generative AI to transform educational practices by supporting diverse learner needs and enhancing collaborative dynamics in learning environments.
Key Applications
MAIC platform with LLM-driven AI agents
Context: An introductory course titled Towards Artificial General Intelligence, targeted at university students in China
Implementation: Students engaged with multiple LLM agents across six modules, completing coursework and interacting through a messaging interface.
Outcomes: Identification of three distinct learner types; improved understanding of student engagement dynamics; potential for enhanced personalized learning experiences.
Challenges: Limited engagement from some students; challenges in capturing all forms of student interaction and engagement.
Implementation Barriers
Engagement Barrier
Some students exhibit minimal interaction with AI agents, raising questions about their learning engagement.
Proposed Solutions: Design AI interventions that prompt deeper interaction and engagement, such as open-ended questions or gamified elements.
Technical Barrier
Dependence on message length and content as primary measures of engagement may overlook other engagement aspects.
Proposed Solutions: Integrate additional data sources like clickstream analysis, eye-tracking, or neurophysiology data.
Project Team
Zhanxin Hao
Researcher
Jianxiao Jiang
Researcher
Jifan Yu
Researcher
Zhiyuan Liu
Researcher
Yu Zhang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Zhanxin Hao, Jianxiao Jiang, Jifan Yu, Zhiyuan Liu, Yu Zhang
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