University Of British Columbia: Research Institution — Prolific Citations Library

Browse 3 peer-reviewed papers from University Of British Columbia spanning Human-AI Collaboration, Creativity (2025). Research powered by Prolific's high-quality participant data.

This page lists 3 peer-reviewed papers from researchers at University Of British Columbia in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (3 of 3)

Unlocking creativity with Artificial Intelligence: Field and experimental evidence on the Goldilocks (curvilinear) effect of human-AI collaboration.

Authors: HCB Huang

Year: 2025

Published in: Journal of Experimental Psychology: General, 2025 - psycnet.apa.org

Institution: University of British Columbia

Research Area: Human-AI Collaboration, Creativity, Experimental Psychology

Discipline: Experimental Psychology

Moderate levels of human-AI collaboration enhance creative performance due to increased knowledge diversity, but excessive or minimal involvement diminishes this effect.

Methods: Two experiments assigned 139 business professionals and 319 working adults to collaborate with ChatGPT at varying levels, and a follow-up survey among 188 creative industry workers was conducted to replicate findings.

Key Findings: The impact of varying degrees of human-AI collaboration on creative performance, evaluated by human judges, entrepreneurs, and AI metrics.

Citations: 3

Sample Size: 646
Data quality in crowdsourcing and spamming behavior detection

Authors: Y Ba, MV Mancenido, EK Chiou, R Pan

Year: 2025

Published in: Behavior Research Methods, 2025 - Springer

Institution: University of Delaware, National Taiwan University, University of British Columbia, Monash University

Research Area: Crowdsourcing, Data Quality, Spamming Behavior Detection, LLM Applications in Behavioral Research

Discipline: Computer Science, Artificial Intelligence, LLM

The paper introduces a systematic method to evaluate crowdsourced data quality and detect spam behaviors through variance decomposition, proposing a spammer index and credibility metrics to improve consistency and reliability in labeling tasks.

Methods: Variance decomposition, Markov chain models, and generalized random effects models were used to assess annotator consistency and credibility; metrics were applied to both simulated and real-world data from two crowdsourcing platforms.

Key Findings: Quality of crowdsourced data, spammer behaviors, annotators’ consistency, and credibility.

Citations: 2
Artificial minds and real beliefs: Perceiving mental states in AI

Authors: O Jacobs

Year: 2025

Published in: 2025 - open.library.ubc.ca

Institution: University of British Columbia

Research Area: Mind Perception in Human-AI Interaction, Anthropomorphism, Psychology

Discipline: Psychology, Human-Computer Interaction (HCI) in AI

This is a University of British Columbia doctoral thesis that investigates how people perceive and attribute mental states (beliefs, intentions, minds) to artificial intelligence systems — exploring the psychological and conceptual underpinnings of mind perception in human–AI interaction.