AI-Enhanced Think-Pair-Share
This study investigates the integration of artificial intelligence into the Think-Pair-Share (TPS) methodology through a learning analytics lens. Using a mixed-methods quasi-experimental design (N=140), we examined how an AI-enhanced collaborative platform influences creative thinking among computer science undergraduates. The experimental group (n=80) utilized a Google Gemini-powered chatbot for scaffolding, while the control group (n=60) used a standard platform. Through comprehensive learning analytics, we identified optimal AI integration in moderate similarity ranges (0.3-0.7), achieved by 70% of participants. The experimental group demonstrated 30-37% productivity gains and 99% increase in thematic diversity, with moderate lexical standardization effects. Our findings provide empirical evidence for designing educational technologies that balance structured support with creative freedom in AI-enhanced collaborative learning.
Authors
- René Lobo-Quintero (ORCID: https://orcid.org/0000-0003-2989-5357)
Institutions
- Fundación Universitaria Konrad Lorenz (CO)
Publication Details
- Journal
- Journal of Learning Analytics
- Published
- 2025-08-29
- DOI
- https://doi.org/10.18608/jla.2025.8807
- Citations
- 1
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 4.17