Fostering AI Literacy Through Simple Prompting Exercises Using Dall-E

This innovative practice full paper describes an AI prompting exercise to enhance students' AI prompting skills by providing immediate visual feedback.The exercise was executed within an introductory systems development and organizational security course, consisting of approximately 54 students who were enrolled in a cybersecurity program at a large Midwestern university. The exercise was conducted in the classroom setting, followed by a discussion of observed patterns in successful and unsuccessful prompts. This paper delves into the exercise's methodology, offering an in-depth exploration of the prompts generated by the cohort of 50 students, supplemented with a content analysis. Majority of students initiated their prompts with elaborate descriptions of the intended subject, showcasing a clear inclination towards a subject-centric approach. There was also variance around explicitly addressing the task of image generation, with only a minority of prompts articulating the core objective. Spatial details of background features were prevalent, particularly in terms of their positioning relative to the subject or within the overall image composition. Moreover, the comprehensive nature of subject descriptions encompassing elements such as gender, age, physical features, ethnicity, and clothing, was a ubiquitous trend observed across nearly every prompt. We also consider future directions for enhancing and extending the exercise in courses with larger enrollments.

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Publication Details

Published
2024-10-13
DOI
https://doi.org/10.1109/fie61694.2024.10893123
Citations
2
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.82
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article

Fostering AI Literacy Through Simple Prompting Exercises Using Dall-E

Nikha Bhardwaj, Paul Thomas, Tatiana Ringenberg, Nathan Kim
2 citations
Online Learning and Analytics
0.82
article

Fostering AI Literacy Through Simple Prompting Exercises Using Dall-E

Nikha Bhardwaj, Paul Thomas, Tatiana Ringenberg, Nathan Kim
article en
2 citations

Abstract

This innovative practice full paper describes an AI prompting exercise to enhance students' AI prompting skills by providing immediate visual feedback.The exercise was executed within an introductory systems development and organizational security course, consisting of approximately 54 students who were enrolled in a cybersecurity program at a large Midwestern university. The exercise was conducted in the classroom setting, followed by a discussion of observed patterns in successful and unsuccessful prompts. This paper delves into the exercise's methodology, offering an in-depth exploration of the prompts generated by the cohort of 50 students, supplemented with a content analysis. Majority of students initiated their prompts with elaborate descriptions of the intended subject, showcasing a clear inclination towards a subject-centric approach. There was also variance around explicitly addressing the task of image generation, with only a minority of prompts articulating the core objective. Spatial details of background features were prevalent, particularly in terms of their positioning relative to the subject or within the overall image composition. Moreover, the comprehensive nature of subject descriptions encompassing elements such as gender, age, physical features, ethnicity, and clothing, was a ubiquitous trend observed across nearly every prompt. We also consider future directions for enhancing and extending the exercise in courses with larger enrollments.

Purdue University West Lafayette (US)
Quality Education
Openalex Percentile: Top 17%
Online Learning and Analytics
0.82
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