Artificial intelligence and computer vision education: Codifying student learning gains and attitudes

Abstract

Artificial Intelligence (AI) and Computer Vision (CV) have rapidly permeated various industries, increasing demand for professionals well-versed in these disciplines. In response to this need, this study evaluates the effectiveness of a novel undergraduate course that aims to equip students with foundational AI and CV knowledge and skills. Unlike existing software-centric courses, this class capitalizes on drones as a practical application area for AI and CV, fostering an active learning environment. The course was conducted over five semesters at a U.S. research institution, with 153 participating students. Data were collected using diverse methods, including formative and summative assessments, surveys of learning gains, focus group interviews, and analysis of capstone project process videos. The course design included Intended Learning Outcomes (ILOs) tailored to meet the requirements of contemporary AI/CV professionals, weekly AI-based curricular modules, unrestricted access to computational resources, and diverse assessment strategies. This study also delves into students’ perceptions of this unique learning approach and challenges, with students from varied academic majors. Initial findings suggest that employing active learning techniques significantly enhances student engagement and understanding of AI/CV concepts, leading to substantial skill gains and positive attitudes toward the subject.

Keywords

• Artificial intelligence education
• Drones in education
• Active Learning
• Computer Vision
• Computer Vision Education