AI instructional agent improves student's perceived learner control and learning outcome: empirical evidence from a randomized controlled trial
Project Overview
The document examines the role of generative AI in education, focusing on an AI instructional agent designed to enhance student engagement and academic performance within a lecture-based environment. A randomized controlled trial evaluated three instructional formats: traditional human teaching, self-paced MOOCs with a chatbot, and an AI agent that delivers real-time lectures and interacts with students. Findings reveal that the AI agent significantly enhances students' perceived control over their learning and positively impacts academic outcomes. This approach promotes more efficient learning behaviors, leading to greater engagement and improved performance compared to traditional methods. The results suggest that integrating generative AI into educational settings can transform teaching dynamics and facilitate personalized learning experiences.
Key Applications
AI instructional agent (MAIC system)
Context: Higher education, specifically in a medium demanding course setting.
Implementation: A randomized controlled trial comparing human instruction, MOOC with chatbot support, and AI instructional agent delivery.
Outcomes: Higher perceived learner control, improved academic performance, more frequent interactions, and reduced learning time.
Challenges: Potential limitations regarding generalizability to other instructional methods and long-term retention of knowledge.
Implementation Barriers
Implementation Barrier
The challenge of integrating AI instructional agents into diverse educational contexts and ensuring their effective pedagogical design.
Proposed Solutions: Future studies should focus on developing hybrid instructional systems that combine real-time interaction with structured content delivery.
Generalizability Barrier
Findings may not apply to all course types or pedagogical models that emphasize deeper reasoning or extensive interaction.
Proposed Solutions: Conduct more randomized controlled trials across various subjects and teaching methods to validate effectiveness.
Project Team
Fei Qin
Researcher
Zhanxin Hao
Researcher
Jifan Yu
Researcher
Zhiyuan Liu
Researcher
Yu Zhang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Fei Qin, Zhanxin Hao, 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