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Pat Pataranutaporn

Assistant Professor

Massachusetts Institute of Technology

United States

Research Interests

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About

Pat Pataranutaporn is an Assistant Professor at the Massachusetts Institute of Technology in the United States. His research focuses on the development of modular autonomous systems for biological plastics upcycling, as well as the interaction between humans and AI, particularly concerning trust and perceived effectiveness. Recent articles authored by Professor Pataranutaporn explore topics such as the influence of beliefs about AI on human interaction and the implications of overtrust in AI-generated medical advice.

Articles (17)

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2026

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2023

Collaborators (5)

Patricia Maes

Professor

Massachusetts Institute of Technology

UNITED STATES

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Hua Shen

Tenure-track Assistant Professor

NYU Shanghai

CHINA

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Hal Hershfield

UCLA Anderson School of Management

UNITED STATES

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JED BRUBAKER

Associate Professor

University of Colorado Boulder

UNITED STATES

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Brad Myers

Professor and Director

Carnegie Mellon University

UNITED STATES

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Pat Pataranutaporn

Research Interests

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About

Articles (17)

Human-AI Interaction Alignment: Designing, Evaluating, and Evolving Value-Centered AI For Reciprocal Human-AI Futures

Future You: Designing and Evaluating Multimodal AI-generated Digital Twins for Strengthening Future Self-Continuity

Simulating Life Paths with Digital Twins: AI-Generated Future Selves Influence Decision-Making and Expand Human Choice

From Remote Space to Remote Time

Suicide-Related Responses From AI Chatbots Through Application Programming Interfaces and Consumer-Facing Apps: Observational Cross-Sectional Comparative Study

"Death" of a Chatbot: Investigating and Designing Toward Psychologically Safe Endings for Human-AI Relationships

Phylogenetic Tree of Dance: Computational Reconstruction of Movement Lineages Through Motion Capture Analysis

Anthology: AI-Augmented Sensemaking Interface to Reduce Radicalization in Conversations

OceanChat: The Effect of Virtual Conversational AI Agents on Sustainable Attitude and Behavior Change

Personal Validation Effect in LLMs: Positive AI Responses Bias Perceptions of Validity, Reliability, Personalization, and Usefulness of Fictitious Predictions

Seeing Is Not Believing: Realistic AI Videos Disrupt Confidence in Authentic Videos and Perceived Reality

Neural Transparency: Mechanistic Interpretability Interfaces for Anticipating Model Behaviors for Personalized AI

What If AI Lived Inside Your Mind? Simulating “Neural Integration” of Human and AI through Mechanistic Interpretability as Provocation

People Overtrust AI-Generated Medical Advice despite Low Accuracy

Development and flight-testing of modular autonomous cultivation systems for biological plastics upcycling aboard the ISS

Synthetic Human Memories: AI-Edited Images and Videos Can Implant False Memories and Distort Recollection

Influencing human–AI interaction by priming beliefs about AI can increase perceived trustworthiness, empathy and effectiveness

Collaborators (5)

Patricia Maes

Hua Shen

Hal Hershfield

JED BRUBAKER

Brad Myers

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