Alternative Title
Conversations, Not Commands: Challenging Current Models of Artificial Intelligence (AI) Attribution
Contributor
University of Central Florida. Faculty Center for Teaching and Learning; University of Central Florida. Division of Digital Learning; Teaching and Learning with AI Conference (2025 : Orlando, Fla.)
Location
Sun & Surf III/IV/V
Start Date
29-5-2025 9:00 AM
End Date
29-5-2025 9:25 AM
Publisher
University of Central Florida Libraries
Keywords:
AI attribution; Scholarly communication; Multi-turn interactions; Citation models; Academic documentation
Subjects
Conversation--Research; Artificial intelligence--Social aspects; Authorship--Computer-assisted instruction; Attribution (Social psychology)--Computer simulation; Academic writing--Computer-assisted instruction
Description
Current AI citation guidelines assume simple, one-shot interactions, but academic work involves complex, multi-turn conversations with rich context. Through examples- including the AI conversations that created this presentation- we'll examine how current attribution models fall short in both scholarly work and student assignments. When identical prompts yield different responses, what does meaningful AI documentation look like? Rather than presenting solutions, this interactive session invites participants to challenge existing citation models and explore how we might better document AI's contributions in academic settings, even when traditional goals of replicability are impossible to achieve. [Title and abstract written with help of Claude AI]
Language
eng
Type
Presentation
Format
application/pdf
Rights Statement
All Rights Reserved
Audience
Faculty; Students
Recommended Citation
Tatarian, Allie, "Conversations, Not Commands: Challenging Current Models of AI Attribution" (2025). Teaching and Learning with AI Conference Presentations. 12.
https://stars.library.ucf.edu/teachwithai/2025/thursday/12
Conversations, Not Commands: Challenging Current Models of AI Attribution
Sun & Surf III/IV/V
Current AI citation guidelines assume simple, one-shot interactions, but academic work involves complex, multi-turn conversations with rich context. Through examples- including the AI conversations that created this presentation- we'll examine how current attribution models fall short in both scholarly work and student assignments. When identical prompts yield different responses, what does meaningful AI documentation look like? Rather than presenting solutions, this interactive session invites participants to challenge existing citation models and explore how we might better document AI's contributions in academic settings, even when traditional goals of replicability are impossible to achieve. [Title and abstract written with help of Claude AI]