Human Centered Ai: Research Area — Prolific Citations Library

Discover 2 peer-reviewed studies in Human Centered Ai (2025–2026). Explore research findings powered by Prolific's diverse participant panel.

This page lists 2 peer-reviewed papers in the research area of Human Centered Ai in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Unpacking Human Preference for LLMs: Demographically Aware Evaluation with the HUMAINE Framework

Authors: N Petrova, A Gordon, E Blindow

Year: 2026

Published in: Open review

Institution: Prolific

Research Area: Human-centered AI evaluation, Bayesian statistics, Responsible AI, AI alignment, LLM Evaluation

Discipline: Machine Learning, Artificial Intelligence

The study introduces HUMAINE, a multidimensional evaluation framework for LLMs, revealing demographic-specific preference variations and ranking google/gemini-2.5-pro as the top-performing model with a posterior probability of 95.6%.

Methods: Multi-turn naturalistic conversations analyzed using a hierarchical Bayesian Bradley-Terry-Davidson model with post-stratification to census data, stratified across 22 demographic groups.

Key Findings: Performance of 28 LLMs across five human-centric dimensions, accounting for demographic-specific preferences.

Sample Size: 23404
Human-AI Collaboration in High-Stakes Decision-Making: Work in Progress

Authors: J Zhou, R Aloufi, N van Zalk

Year: 2025

Published in: 38th International BCS Human ..., 2025 - scienceopen.com

Institution: The affiliated institutions for the authors are not available in the provided context or search results.

Research Area: High-Stakes Decision-Making, Explainable AI, User Trust, Human-Centered AI, Interaction Design

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

This study explores how human collaboration and communication dynamics vary when interacting with an AI chatbot versus a human partner in a high-stakes decision-making task.

Methods: One-way between-subjects design using the NASA Moon Survival Task to compare behaviors, linguistic coordination, and perceptions in interactions with AI or human partners.

Key Findings: Collaboration processes, communicative dynamics, outcomes, retrospective interaction experience, partner perception, and linguistic coordination, with user profiling for AI benefit variations.

DOI: doi.org/10.14236/ewic/BCSHCI2025.52

Citations: 1