Date
Publisher
arXiv
Pre-service teachers play a unique dual role as they straddle between the
roles of students and future teachers. This dual role requires them to adopt
both the learner's and the instructor's perspectives while engaging with
pedagogical and content knowledge. The current study investigates how
pre-service elementary teachers taking a physical science course prompt AI to
generate representations that effectively communicate conceptual ideas to two
distinct audiences. The context involves participants interacting with AI to
generate appropriate representations that explain the concepts of wave velocity
to their elementary students (while casting themselves as teachers) and the
Ideal Gas Law to their English teachers (while casting themselves as students).
Emergent coding of the AI prompts highlight that, when acting as teachers,
participants were more explicit in specifying the target audience,
predetermining the type of representation, and producing a broader variety of
representations compared to when they acted as students. Implications of the
observed 'exploratory' and 'prescriptive' prompting trends across the two roles
on pre-service teachers' education and their professional development are
discussed.
Study at a glance
- Question: How do future elementary teachers prompt AI to create science explanations when acting as teachers versus students?
- Setting and data: Pre-service elementary teachers in a physical science course; sample size not stated in the abstract.
- Method: Qualitative analysis of AI prompts (Emergent coding).
Why it matters
Pre-service teachers play a unique dual role as students and future teachers. This requires them to adopt both learner and instructor perspectives when prompting AI to generate representations that communicate conceptual ideas to distinct audiences.
Key findings
- In a physical science course, elementary pre-service teachers specified their target audience more explicitly when prompting AI as teachers than as students.
- Pre-service teachers predetermined the type of representation more often when prompting AI as teachers compared to when acting as students.
- Pre-service teachers generated a broader variety of representations when prompting AI as teachers than when acting as students.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
