Enhancing Critical Thinking in Introductory Programming through Artificial Intelligence and Inquiry-based Learning Environment

About This Grant

This project aims to serve the national interest by strengthening critical thinking and independent learning among undergraduate students through the responsible use of artificial intelligence (AI) in computer science classes. As large language models (LLMs) become increasingly integrated into education, there is a pressing need to support students in using these tools thoughtfully, rather than relying on them passively. This Level 1 Engaged Student Learning project will implement and evaluate the Inquiry-based Learning Environment (SMILE), an innovative framework that guides students in developing deeper programming and critical thinking skills by combining structured questioning with metacognitive reflection. Broader impacts include publicly available instructional materials that develop analytical reasoning skills, faculty training, and alignment with workforce needs in an AI-augmented economy. The project plans to investigate the impact of unguided AI use on student critical thinking and compare it to SMILE-guided approaches. Using a mixed-methods design, which includes interviews and structured observational studies, the research examines the SMILE framework's effectiveness in promoting critical thinking and long-term metacognitive development in computer science, advancing the understanding of structured frameworks as an effective AI-driven pedagogy. Expected outcomes include validated rubrics, instructional exercises, and evidence-based guidelines for integrating AI into computing education. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.