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

Tahiya Chowdhury

TL;DR

This paper addresses how to cultivate AI competency within an introductory CS curriculum by embedding computer vision as a practical application context. It details a Fall 2023 offering where computational thinking and critical thinking are developed through Python-based programming, CV tooling, readings, and a Personal Project, supplemented by structured reading discussions to address AI ethics and societal impact. Preliminary results from pre/post surveys show increases in sense of belonging and self-efficacy, with ethics awareness remaining high but not significantly altered, and qualitative reading discussions reveal deep engagement with AI-related social issues. The study discusses scalability, staffing, and longer-term evaluation to assess how such a course influences subsequent CS performance, major choice, and career trajectories, highlighting its potential to broaden participation and prepare students for AI-enabled workplaces.

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.

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

TL;DR

This paper addresses how to cultivate AI competency within an introductory CS curriculum by embedding computer vision as a practical application context. It details a Fall 2023 offering where computational thinking and critical thinking are developed through Python-based programming, CV tooling, readings, and a Personal Project, supplemented by structured reading discussions to address AI ethics and societal impact. Preliminary results from pre/post surveys show increases in sense of belonging and self-efficacy, with ethics awareness remaining high but not significantly altered, and qualitative reading discussions reveal deep engagement with AI-related social issues. The study discusses scalability, staffing, and longer-term evaluation to assess how such a course influences subsequent CS performance, major choice, and career trajectories, highlighting its potential to broaden participation and prepare students for AI-enabled workplaces.

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.

Paper Structure

This paper contains 22 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Methodology
  4. Course Context
  5. Course Description
  6. Learning Outcomes
  7. Course Structure
  8. Reading Activities
  9. Lectures and Programming Activities
  10. Projects
  11. Assessment
  12. Data
  13. Data Collection
  14. Data Analysis
  15. Results
  16. ...and 7 more sections

Figures (2)

  • Figure 1: (Left): Radar plot showing aggregate student responses from the four repeated measures over the course. We observed an increase in AI ethics awareness, sense of belonging, and self efficacy.
  • Figure 2: Images of two final project applications: Vision Assistant (left): a low cost reading assistive tool for visually impaired, and Passing Pro (right): a volleyball pass classification and analytics application.