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, 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
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
