Date
Publisher
arXiv
The abilities of Generative-Artificial Intelligence (AI) to produce
real-time, sophisticated responses across diverse contexts has promised a huge
potential in physics education, particularly in providing customized feedback.
In this study, we investigate around 1200 introductory students' preferences
about AI-feedback generated from three distinct prompt types: (a) self-crafted,
(b) entailing foundational prompt-engineering techniques, and (c) entailing
foundational prompt-engineering techniques along with principles of
effective-feedback. The results highlight an overwhelming fraction of students
preferring feedback generated using structured prompts, with those entailing
combined features of prompt engineering and effective feedback to be favored
most. However, the popular choice also elicited stronger preferences with
students either liking or disliking the feedback. Students also ranked the
feedback generated using their self-crafted prompts as the least preferred
choice. Students' second preferences given their first choice and implications
of the results such as the need to incorporate prompt engineering in
introductory courses are discussed.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
