Empowering Critical Thinking Through AI: The PAIR Model's Impact on Higher Education Excellence
This study investigates the implementation of the PAIR (Problem, AI, Interaction, Reflection) model in undergraduate business courses at Tecnologico de Monterrey, assessing its impact on critical thinking development and student engagement with Artificial Intelligence (AI) tools. A mixed-method approach was employed, involving 83 fifth-semester International Business students across two campuses. Data collection included quantitative surveys, student reflections, and course deliverable analysis. Quantitative results indicated a positive trend towards PAIR model implementation. Students reported significant improvement in critical thinking skills (Mean = 3.82, SD = 0.86) and high engagement with AI interaction activities (Mean = 3.78, SD = 0.89). 87% of students felt AI enhanced their reflection and research skills. Qualitative analysis revealed increased student awareness of the need to verify and contextualize AI-provided information, recognizing critical thinking's importance. Progress was noted in students' ability to select and evaluate multiple information sources, including AI-generated ones. The study concludes that the PAIR model can enhance critical thinking and foster active learning in AI-influenced educational environments. Structured integration of AI tools into curricula may positively impact student learning experiences and critical skill development for academic and professional life. Limitations include the sample being restricted to one degree program across two campuses and the cross-sectional nature, precluding long-term effect evaluation. Future research should explore the model's long-term impact, compare it with other AI integration models in education, and examine its applicability across various disciplines and educational levels.
Authors
- Ana Valdés (ORCID: https://orcid.org/0000-0002-3079-2008)
- Héctor Ramón Rodríguez Maya
Institutions
- Tecnológico de Monterrey (MX)
Publication Details
- Published
- 2024-11-07
- DOI
- https://doi.org/10.1109/ismsit63511.2024.10757186
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.24