Five-item pre-instruction AI perception intake survey

Product or Programme · All products and programmes
Evidence · 3 claims (3 for) · 1 study (1 associational), q2 · 1 of 1 report an effect size · 3 claims rest on one study

Description

A study-developed anonymous Qualtrics survey that measures bioscience trainees' baseline AI perceptions and related intake characteristics before an RCR course.

Components

Claims

• The cohort's baseline perception profile shows high endorsement of critical-thinking risk and training interest and substantially more cautious trust for complex-task use than for accuracy [+W]
• Self-reported LLM usage frequency shows Holm-corrected associations with all five baseline AI perception outcomes, strongest for complex-task trust, accuracy trust, and critical-thinking risk [+W]
• Self-rated LLM familiarity is a weaker secondary intake signal, surviving Holm correction for three of five outcomes with a bottom-anchored pattern [+W]

Related Products and Programmes

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Key Sources

• Yongkyung Oh, Lynn Talton, Alex Bui. (2026). Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction. KDD 2026 AI for Education Day, at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '26). https://kdd2026.kdd.org

Page metadata

Field	Value
type	product
id	five-item-pre-instruction-ai-perception-intake-survey
title	Five-item pre-instruction AI perception intake survey
description	A study-developed anonymous Qualtrics survey that measures bioscience trainees' baseline AI perceptions and related intake characteristics before an RCR course.
status	review
product_kind	assessment
generated	by process:settle-candidates on 2026-10-10
trust	unverified — no one has confirmed this page's content

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