Georgia Institute Of Technology: Research Institution — Prolific Citations Library

Browse 2 peer-reviewed papers from Georgia Institute Of Technology spanning Social and Emotional Human-AI Interaction, Psychosocial Effects of AI Chatbot Use (2023–2025). Research powered by Prolific's high-quality participant data.

This page lists 2 peer-reviewed papers from researchers at Georgia Institute Of Technology in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Longitudinal Study on Social and Emotional Use of AI Conversational Agent

Authors: Mohit Chandra, Javier Hernandez, Gonzalo Ramos, Mahsa Ershadi, Ananya Bhattacharjee, Judith Amores, Ebele Okoli, Ann Paradiso, Shahed Warreth, Jina Suh

Year: 2025

Published in: ArXiv

Institution: Georgia Institute of Technology, Microsoft Research, University of Toronto, Microsoft

Research Area: Social and Emotional Human-AI Interaction, Psychosocial Effects of AI Chatbot Use

Discipline: Social Science

This study found that active use of conversational AI tools significantly increased perceived attachment, empathy, and emotional support from AI, while showing the potential for improving social and emotional interactions with proper safeguards.

Methods: Participants were divided into two groups: one group used conversational AI tools actively (AU, n=89), and a baseline group used AI and the internet regularly (BU, n=60). Emotional and social interaction measures were tracked over five weeks.

Key Findings: Perceived attachment towards AI, AI empathy, comfort in using AI for emotional support, stress management, and discussion of personal topics.

Sample Size: 149
Safe RLHF: Safe reinforcement learning from human feedback

Authors: J Dai, X Pan, R Sun, J Ji, X Xu, M Liu, Y Wang

Year: 2023

Published in: arXiv preprint arXiv ..., 2023 - arxiv.org

Institution: Cornell University, Georgia Institute of Technology

Research Area: Reinforcement Learning from Human Feedback (RLHF), Safe AI, Reinforcement Learning

Discipline: Artificial Intelligence, Machine Learning

DOI: https://doi.org/10.48550/arXiv.2310.12773

Citations: 598