Human Ai Collaboration: Research Area — Prolific Citations Library

Discover 12 peer-reviewed studies in Human Ai Collaboration (2022–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 12 peer-reviewed papers in the research area of Human Ai Collaboration in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (12 of 12)

When AI-Based Agents Are Proactive: Implications for Competence and System Satisfaction in Human-AI Collaboration

Authors: C Diebel, M Goutier, M Adam, A Benlian

Year: 2025

Published in: Business & Information Systems ..., 2025 - Springer

Institution: Technical University of Darmstadt, University of Goettingen

Research Area: Human-AI Collaboration, System Satisfaction, User Competence

Discipline: Information Systems, Human-Computer Interaction (HCI), Artificial Intelligence

Proactive AI-based agent assistance decreases users' competence-based self-esteem and system satisfaction, especially for users with higher AI knowledge.

Methods: Vignette-based online experiment using self-determination theory as the framework to evaluate user responses to proactive vs. reactive AI assistance.

Key Findings: Impact of proactive vs. reactive AI help on users' competence-based self-esteem and system satisfaction, moderated by users' AI knowledge levels.

DOI: https://doi.org/10.1007/s12599-024-00918-y

Citations: 32
Collaborating with ai agents: Field experiments on teamwork, productivity, and performance

Authors: H Ju, S Aral

Year: 2025

Published in: arXiv preprint arXiv:2503.18238, 2025 - arxiv.org

Institution: Johns Hopkins Carey Business School, MIT Sloan School of Management

Research Area: Human-AI Collaboration, Teamwork, Organizational Productivity

Discipline: Human-AI Interaction

Collaboration with AI agents increases productivity, reshapes communication patterns, and improves text quality while human teams excel in image quality; AI requires fine-tuning for multimodal workflows.

Methods: Large-scale randomized controlled trials using Pairit platform with human-human and human-AI teams performing collaborative marketing tasks.

Key Findings: Productivity, communication patterns, workflow processes, ad quality (text and image), and ad performance metrics.

DOI: https://doi.org/10.48550/arXiv.2503.18238

Citations: 14

Sample Size: 2310
Don't Be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration

Authors: P Spitzer, J Holstein, K Morrison

Year: 2025

Published in: ... Journal of Human ..., 2025 - Taylor & Francis

Institution: Karlsruhe Institute of Technology, Carnegie Mellon University, University of Bayreuth

Research Area: Human-AI Collaboration, Explainable AI (XAI)

Discipline: Human-Computer Interaction (HCI)

Incorrect explanations in AI-assisted decision-making lead to a misinformation effect, negatively impacting human reasoning, procedural knowledge, and collaboration performance.

Methods: A study on human-AI collaboration involving AI-supported decision-making paired with explainable AI (XAI) to assess the effects of incorrect explanations.

Key Findings: Impact of incorrect explanations on human reasoning strategies, procedural knowledge, and team performance in human-AI collaboration.

Citations: 13

Sample Size: 160
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
Imperfections of XAI: phenomena influencing AI-assisted decision-making

Authors: P Spitzer, K Morrison, V Turri, M Feng, A Perer

Year: 2025

Published in: ACM Transactions on ..., 2025 - dl.acm.org

Institution: Carnegie Mellon University, Karlsruhe Institute of Technology, University of Bayreuth

Research Area: Explainable AI (XAI), AI-Assisted Decision-Making, Human-AI Collaboration

Discipline: Artificial Intelligence

The study highlights how imperfect explainable AI (XAI), along with human cognitive styles, affects reliance on AI and the performance of human–AI teams, providing design guidelines for better collaboration systems.

Methods: The researchers conducted a study with 136 participants, analyzing the effects of explanation imperfections and cognitive styles on AI-assisted decision-making and human–AI collaboration.

Key Findings: The impact of incorrect explanations and explanation modalities on human reliance, decision-making, and human–AI team performance, as well as the role of cognitive styles.

Citations: 2

Sample Size: 136
Who" designs better? A competition among human, artificial intelligence and human-AI collaboration

Authors: KHT Vo

Year: 2025

Published in: Design Science, 2025 - cambridge.org

Institution: Indiana University

Research Area: Human-AI Collaboration in Design

Discipline: Human-Computer Interaction (HCI)

This research examines whether a machine, specifically Artificial Intelligence, can be creative by comparing design solutions for a practical competition – a light fixture for a pediatric waiting room – among AI, collaboration efforts and a human designer.

Citations: 1
Improving annotation quality: empirical insights into bias, human-AI collaboration, and workflow design

Authors: J Beck

Year: 2025

Published in: 2025 - edoc.ub.uni-muenchen.de

Institution: Ludwig-Maximilians-Universität München, University of Bayreuth

Research Area: Annotation Quality, Human-AI Collaboration, Behavioral Science, Human-Computer Interaction (HCI)

Discipline: Human-Computer Interaction (HCI)

The study empirically evaluates annotation bias, proposes strategies to reduce its impact, and explores the use of large language models in automated and hybrid annotation workflows.

Methods: Empirical assessments and experimental evaluations involving annotation workflows and large language models.

Key Findings: Annotation bias, annotation quality, and the effectiveness of hybrid workflows integrating human input and AI models.
Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence

Authors: P Hemmer, M Schemmer, N Kühl, M Vössing, G Satzger

Year: 2024

Published in: ArXiv

Institution: Karlsruhe Institute of Technology

Research Area: Human-AI Collaboration, Explainable AI (XAI), Complementarity in Decision Making

Discipline: Human-Computer Interaction (HCI)
Examining Human-AI Collaboration for Co-Writing Constructive Comments Online

Authors: FARHANA SHAHID, MAXIMILIAN DITTGEN

Year: 2024

Published in: ArXiv

Institution: Cornell University

Research Area: Human-AI Collaboration, Constructive Discourse, Online Communication, Human-Computer Interaction (HCI)

Discipline: Human-Computer Interaction (HCI), Artificial Intelligence
Towards Effective Human-AI Collaboration

Authors: G He

Year: 2023

Published in: repository.tudelft.nl

Institution: Delft University of Technology

Research Area: Human-AI Collaboration, AI Systems, Appropriate Reliance on AI Systems, Artificial Intelligence, Computer Science

Discipline: Artificial Intelligence, Computer Science
Uncalibrated models can improve human-ai collaboration

Authors: K Vodrahalli, T Gerstenberg

Year: 2022

Published in: Advances in Neural ..., 2022 - proceedings.neurips.cc

Institution: Columbia University, Princeton University, Intel, Stanford University, Massachusetts Institute of Technology

Research Area: Human-AI Collaboration, Human Behavior Modeling, Decision Making

Discipline: Artificial Intelligence

Citations: 70
Taking advice from (dis) similar machines: the impact of human-machine similarity on machine-assisted decision-making

Authors: N Grgić-Hlača, C Castelluccia

Year: 2022

Published in: Proceedings of the AAAI ..., 2022 - ojs.aaai.org

Institution: École Polytechnique Fédérale de Lausanne (EPFL), Inria

Research Area: Human-Computer Interaction (HCI), Algorithmic Decision-Making, Human-AI Collaboration

Discipline: Artificial Intelligence

DOI: https://doi.org/10.1609/hcomp.v10i1.21989

Citations: 22