Browse 8 peer-reviewed papers from Delft University Of Technology spanning Artificial Intelligence, Computer Science (2021–2025). Research powered by Prolific's high-quality participant data.
This page lists 8 peer-reviewed papers from researchers at Delft University Of Technology in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: N Aldahoul, H Ibrahim, M Varvello, A Kaufman
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: Delft University of Technology, University of Pennsylvania, New York University, King Abdullah University of Science and Technology, Massachusetts Institute of Technology, University of Texas at Austin
Research Area: Artificial Intelligence, Computer Science, Political Science
Discipline: Artificial Intelligence, Social Science
The study finds that Large Language Models (LLMs) exhibit extreme political views on specific topics despite appearing ideologically moderate overall, and demonstrate a persuasive influence on users' political preferences even in informational contexts.
Methods: Compared 31 LLMs' political biases against benchmarks (legislators, judges, representative voter samples) and conducted a randomized experiment to measure their persuasive impact in informational interactions.
Key Findings: Ideological consistency, political extremity, and persuasive effects of LLMs in information-seeking contexts.
Citations: 7
Sample Size: 31
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Authors: V Robbemond, O Inel, U Gadiraju
Year: 2024
Published in: ... of the 30th ACM Conference on ..., 2022 - dl.acm.org
Institution: V Robbemond: Delft University of Technology, O Inel: Delft University of Technology, U Gadiraju: Delft University of Technology
Research Area: Explanation Modality in AI-assisted Decision Making
Discipline: Human-Computer Interaction
The study explores the role of explanation modalities in AI-assisted credibility assessment tasks, finding that combined modalities (text and/or audio with graphics) enhance accuracy, trust, and usability compared to single-modality approaches.
Methods: A between-subjects experiment was conducted with six explanation modalities to evaluate their influence on user performance, trust, and usability in credibility assessments.
Key Findings: The effects of different explanation modalities on decision accuracy, system trust, and usability in an AI-assisted credibility assessment system.
DOI: 10.1145/3503252.3531311
Citations: 47
Sample Size: 375
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Authors: KD Wang, Z Chen, C Wieman
Year: 2024
Published in: ... of the 14th Learning Analytics and ..., 2024 - dl.acm.org
Institution: Delft University of Technology, University of Queensland
Research Area: Crowdsourcing for Educational Research
Discipline: Educational Research, Computer Science
Citations: 8
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Authors: JD Lomas, W van der Maden
Year: 2024
Published in: arXiv preprint arXiv ..., 2024 - arxiv.org
Institution: Delft University of Technology, Microsoft Research
Research Area: Affective Computing, Human-AI Interaction, Image Generation
Discipline: Artificial Intelligence
DOI: https://doi.org/10.48550/arXiv.2405.18510
Citations: 5
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Authors: J de Winter, D Dodou, YB Eisma
Year: 2024
Published in: Discover, 2024 - repository.tudelft.nl
Institution: Delft University of Technology
Research Area: Psychology
Discipline: Psychology
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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
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Authors: J Oppenlaender, T Abbas, U Gadiraju
Year: 2022
Published in: ... of the ACM on Human-Computer ..., 2024 - dl.acm.org
Institution: University of Oulu, University of Tübingen, Aston University, Delft University of Technology
Research Area: Crowdsourcing, Research Best Practices, Human-Computer Interaction
Discipline: Human-Computer Interaction
The paper shows that pilot studies are widely used but poorly reported in crowdsourcing and HCI research, making studies harder to interpret, replicate, and trust. It proposes clear reporting guidelines to improve transparency, rigor, and data quality in human-subject experiments.
Citations: 6
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Authors: S Qiu, A Bozzon, MV Birk, U Gadiraju
Year: 2021
Published in: ... of the ACM on Human-Computer ..., 2021 - dl.acm.org
Institution: Delft University of Technology, Eindhoven University of Technology
Research Area: Crowdsourcing, Human-Computer Interaction (HCI) Design
Discipline: Human-Computer Interaction
DOI: https://doi.org/10.1145/3476063
Citations: 34
