Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course
Project Overview
The document explores the integration of generative AI in education, particularly through an introductory computer science course that emphasizes the development of AI competency via computer vision. It underscores the necessity of teaching computational and critical thinking, alongside ethical considerations in AI. The course incorporates hands-on projects that foster engagement and discussions aimed at enhancing students' self-efficacy, sense of belonging, and ethical awareness concerning AI technologies. Initial evaluations indicate positive advancements in these areas, suggesting that students are becoming more confident and ethically aware in their approach to AI. However, the document also points out ongoing challenges in effectively balancing the technical aspects of AI with ethical discussions, highlighting the need for continuous refinement of the curriculum. Overall, the findings suggest that while generative AI can significantly enrich educational experiences, careful attention must be given to ensure a well-rounded approach that prepares students for the complexities of AI in real-world applications.
Key Applications
Introductory course teaching computational thinking using computer vision
Context: Undergraduate students in an introductory computer science course
Implementation: The course included lectures, hands-on programming activities, reading assignments, and a final personal project that allowed students to apply their knowledge in a personally meaningful way.
Outcomes: Improved sense of belonging, self-efficacy, and AI ethics awareness among students.
Challenges: Balancing depth of AI topic coverage with breadth of computational thinking; managing diverse student backgrounds and interests.
Implementation Barriers
Curriculum Design
Existing courses need updates to keep pace with emerging AI tools and real-world applications.
Proposed Solutions: Integrate popular AI topics into introductory courses to enhance engagement and retention.
Student Engagement
Students may have varying interests and backgrounds, making it challenging to create a universally engaging curriculum.
Proposed Solutions: Incorporate project-based learning where students can choose topics that interest them.
Resource Allocation
Limited resources may hinder the scaling of such courses to larger cohorts.
Proposed Solutions: Hire additional teaching staff who have taken the course to support larger class sizes.
Project Team
Tahiya Chowdhury
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Tahiya Chowdhury
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai