AI-generated learning resources integrated within Learning Management Systems significantly improve student achievement and satisfaction in online graduate education.
Objective: The study aimed to investigate the implementation and effectiveness of an AI system integrated within Learning Management Systems for automatically generating student learning resources from faculty-selected materials.
Methods:
- Quasi-experimental design conducted during Summer 2024
- 97 graduate students across four sections at two public universities
- 47 students in experimental group, 50 in control group
- Five-week online graduate course in Curriculum and Instruction
- AI tool (ALRG) embedded within LMS generated study guides, summaries, flashcards, and practice questions
- Assessment through module quizzes, final examination, and course evaluations
- Implementation of cognitive load theory and multimedia learning principles
Key Findings:
- Experimental group consistently outperformed control group across all assessments
- Effect sizes increased from Module 1 (d = 0.68) to Module 5 (d = 1.08)
- Final examination showed significant difference (experimental M = 45.68 vs. control M = 41.24)
- Substantial improvements in student satisfaction with learning materials (d = 1.38 to 1.62)
- No significant differences in instructor effectiveness ratings between groups
- Students averaged 6.8 hours per week using the system
- Study guides were most accessed (32%), followed by practice questions (28%), flashcards (22%), and summaries (18%)
Implications:
- Demonstrates AI-generated resources can effectively support student learning without increasing faculty workload
- Suggests viable path for enhancing online learning without new technological barriers
- Challenges concerns about AI diminishing faculty role
- Shows particular value in intensive online courses
- Provides evidence for successful integration of AI tools within existing LMS platforms
Limitations:
- Focus on single graduate-level course
- Intensive five-week format may affect generalizability
- Potential selection bias as students self-selected into sections
- Limited to specific discipline and population
- Lack of random assignment to experimental conditions
Future Directions:
- Examine long-term impact across different disciplines
- Study effectiveness in traditional semester-length courses
- Investigate impact across diverse student populations
- Consider random assignment in future studies
- Control for potential confounding variables
- Explore implementation in various educational settings
Title and Authors: "Transforming Faculty-Selected Course Materials into Effective Study Tools: The Role of AI in Enhancing Student Learning and Satisfaction" by Zafer Unal
Published On: 2024
Published By: International Journal on E-Learning (2024) 23(3), 223–240
The study represents a significant contribution to understanding how AI can enhance online education through seamless integration with existing learning management systems. It provides empirical evidence for the effectiveness of AI-generated learning resources while maintaining the important role of faculty in the educational process. The research particularly emphasizes the potential of AI to address common challenges in online education, such as the need for consistent student support and the management of faculty workload, while demonstrating measurable improvements in both student achievement and satisfaction.