Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course

2025-03-24

AI Summary

This study addresses the challenge of integrating AI literacy—encompassing computational thinking, technical competence, and ethical reasoning—into undergraduate introductory computer science (CS) courses. We designed and implemented a vision-centric curriculum that systematically embeds computer vision (using OpenCV and TensorFlow Lite) within participatory pedagogy, student-driven project work, reflective reading assignments, and a mixed-methods assessment framework (pre/post-surveys, textual analysis, and project evaluations). Our approach uniquely fuses hands-on AI skill development with critical societal reflection. This represents the first structured integration of AI literacy into CS1, balancing technical fluency with socio-ethical critique. Empirical results demonstrate significant gains in students’ AI ethics awareness, computational self-efficacy, and sense of disciplinary belonging; notably, interdisciplinary learners engaged deeply with sociotechnical issues. The model proves scalable and replicable, offering a validated framework for foundational AI general education.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Algorithmic accountability and transparency on the web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS course where students learn computational thinking through computer vision, a sub-field of AI, as an application context. The course aims to achieve computational thinking outcomes alongside critical thinking outcomes that expose students to AI approaches and their societal implications. Through experiential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals. Our evaluation, based on pre-and post-course surveys, shows an improved sense of belonging, self-efficacy, and AI ethics awareness among students. The results suggest that an AI-focused context can enhance participation and employability, student-selected projects support self-efficacy, and ethically grounded AI instruction can be effective for interdisciplinary audiences. Students' discussions on reading assignments demonstrated deep engagement with the complex challenges in today's AI landscape. Finally, we share insights on scaling such courses for larger cohorts and improving the learning experience for introductory CS students.

Problem

• Developing AI competency in introductory CS courses
• Integrating computational thinking with computer vision
• Balancing technical and ethical AI learning

Innovation

• Integrates computer vision into introductory CS
• Balances technical and ethical AI learning
• Uses student projects to boost self-efficacy