Browse 3 peer-reviewed papers from Aalborg University spanning Human-AI Interaction, Cognitive Forcing (2024–2025). Research powered by Prolific's high-quality participant data.
This page lists 3 peer-reviewed papers from researchers at Aalborg University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: S de Jong, V Paananen, B Tag
Year: 2025
Published in: Proceedings of the ACM on ..., 2025 - dl.acm.org
Institution: Niels van Berkel: Aalborg University, Sander de Jong, Ville Paananen, Benjamin Tag: Monash University
Research Area: Cognitive Forcing, Human-AI Interaction, AI Explainability (XAI), Decision-Making in AI Systems.
Discipline: Human-Computer Interaction (HCI), Artificial Intelligence
Partial explanations encourage critical thinking and reduce user overreliance on incorrect AI suggestions, with performance varying based on individual need for cognition and task difficulty.
Methods: Two experiments were conducted: (1) participants identified shortest paths in weighted graphs, and (2) participants corrected spelling and grammar errors in text, with AI suggestions accompanied by no, partial, or full explanations.
Key Findings: Effectiveness of partial explanations in reducing overreliance on incorrect AI suggestions, and interaction of explanation type with task difficulty and user need for cognition.
DOI: https://doi.org/10.1145/3710946
Citations: 14
Sample Size: 474
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Authors: S Lambiase, G Catolino, F Palomba, F Ferrucci, D Russo
Year: 2025
Published in: ACM Transactions on Software Engineering and Methodology, 2025•dl.acm.org
Institution: University of Salerno, Aalborg University
Research Area: Technology Adoption, Software Engineering Practices, Socio-Technical Research
Discipline: Computer Science, Software Engineering, Human–Computer Interaction (HCI)
The study uses survey data from software professionals and Partial Least Squares Structural Equation Modeling (PLS-SEM) to measure the role of cultural values relative to established predictors like performance expectancy and habitual use in LLM adoption.
Citations: 11
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Authors: A Alami, M Zahedi, N Ernst
Year: 2024
Published in: ArXiv
Institution: Aalborg University, University of Melbourne, University of Victoria
Research Area: Software Engineering, Online Recruitment, Human-AI Interaction
Discipline: Software Engineering
This empirical study investigates the contribution of Pretrained Language Models (PLMs), such as BERT and RoBERTa, to various components of multi-hop Question Answering (QA) tasks, focusing on evidence extraction and information aggregation.
Citations: 14