2 citations · 2 across the 8 of their papers we have counts for
8 papers
The Social Cost of an AI Teammate: How an Artificial Teammate Reshapes Human-Human Communication in Small-Team Decision-Making
Nia Nixon, Jaeyoon Choi, Pedro Martins De Bastos +4
Conversational AI is increasingly positioned as a teammate rather than a tool, yet we know little about how its presence reshapes communication among the humans on the team. We exa…
TRAIL: A Platform for Configurable Human--AI Teaming Experiments
Mohammad Amin Samadi, Pedro Martins De Bastos, Jaeyoon Choi +3
An AI teammate's design properties (personality, communication style, when it speaks) can shape a team's trust, coordination, and decisions. Studying this rigorously demands infras…
Personalities at Play: Probing Alignment in AI Teammates
Mohammad Amin Samadi, Nia Nixon
Collaborative problem solving and learning are shaped by who or what is on the team. As large language models (LLMs) increasingly function as collaborators rather than tools, a key…
Multi-Agent Collaborative Framework For Math Problem Generation
Kia Karbasi, Kevin Hong, Mohammad Amin Samadi +1
Automatic question generation (AQG) for mathematics education remains an elusive goal for Intelligent Tutoring Systems and educators. While pre-trained transformer-based language m…
Read the Room or Lead the Room: Understanding Socio-Cognitive Dynamics in Human-AI Teaming
Jaeyoon Choi, Mohammad Amin Samadi, Spencer JaQuay +2
Research on Collaborative Problem Solving (CPS) has traditionally examined how humans rely on one another cognitively and socially to accomplish tasks together. With the rapid adva…
The AI Collaborator: Bridging Human-AI Interaction in Educational and Professional Settings
Mohammad Amin Samadi, Spencer JaQuay, Jing Gu +1
AI Collaborator, powered by OpenAI's GPT-4, is a groundbreaking tool designed for human-AI collaboration research. Its standout feature is the ability for researchers to create cus…
Performance Prediction for Multi-hop Questions
Mohammadreza Samadi, Davood Rafiei
We study the problem of Query Performance Prediction (QPP) for open-domain multi-hop Question Answering (QA), where the task is to estimate the difficulty of evaluating a multi-hop…
Evaluating Sparse Interpretable Word Embeddings for Biomedical Domain
Mohammad Amin Samadi, Mohammad Sadegh Akhondzadeh, Sayed Jalal Zahabi +3
Word embeddings have found their way into a wide range of natural language processing tasks including those in the biomedical domain. While these vector representations successfull…