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Back to Sciences (Social, Health, Biological, Physical)

All Work Published on Sciences (Social, Health, Biological, Physical)

Contextualizing Meaningful Social Interactions and Psychological Well-Being in Everyday Life
Mahnaz Roshanaei, Sumer S. Vaid, Andrea L. Courtney, Serena J. Soh, Gabriella Harari, Jamil Zaki
Jun 28, 2024
Research
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Using three large-scale longitudinal datasets collected from a cohort of university students over the span of 3 years (total N = 2,896 participants; ecological momentary assessments = 129,414), we found that engagement in meaningful social interactions with peers was associated with lower momentary loneliness and greater affective well-being. We also examined the role of four contextual factors (interaction partners, communication channels, places, and co-occurring activities) in explaining the relationships between meaningful social interactions and momentary well-being. Across samples, we found (a) participants reported experiencing greater loneliness and lower affective well-being after engaging in meaningful social interaction via computer-mediated channels (and via direct messaging in particular), compared to face-to-face, and (b) participants reported experiencing lower affective well-being after engaging in meaningful social interactions while dining and studying or working, compared to while resting. Taken together, our findings provide insight into the relationships between meaningful social interactions, momentary well-being, and contextual factors.

Contextualizing Meaningful Social Interactions and Psychological Well-Being in Everyday Life

Mahnaz Roshanaei, Sumer S. Vaid, Andrea L. Courtney, Serena J. Soh, Gabriella Harari, Jamil Zaki
Jun 28, 2024

Using three large-scale longitudinal datasets collected from a cohort of university students over the span of 3 years (total N = 2,896 participants; ecological momentary assessments = 129,414), we found that engagement in meaningful social interactions with peers was associated with lower momentary loneliness and greater affective well-being. We also examined the role of four contextual factors (interaction partners, communication channels, places, and co-occurring activities) in explaining the relationships between meaningful social interactions and momentary well-being. Across samples, we found (a) participants reported experiencing greater loneliness and lower affective well-being after engaging in meaningful social interaction via computer-mediated channels (and via direct messaging in particular), compared to face-to-face, and (b) participants reported experiencing lower affective well-being after engaging in meaningful social interactions while dining and studying or working, compared to while resting. Taken together, our findings provide insight into the relationships between meaningful social interactions, momentary well-being, and contextual factors.

Sciences (Social, Health, Biological, Physical)
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Research
Marissa Reitsma
Assistant Professor of Health Policy
Person

Marissa Reitsma

Assistant Professor of Health Policy
Sciences (Social, Health, Biological, Physical)
Ethics, Equity, Inclusion
Person
Stanford Study: AI Experts Are Optimistic About AI. The Rest of Us… Not So Much
KQED
Apr 13, 2026
Media Mention

Sha Sajadieh, AI Index Lead, comments on HAI's 2026 AI Index findings.

Stanford Study: AI Experts Are Optimistic About AI. The Rest of Us… Not So Much

KQED
Apr 13, 2026

Sha Sajadieh, AI Index Lead, comments on HAI's 2026 AI Index findings.

Workforce, Labor
Sciences (Social, Health, Biological, Physical)
Design, Human-Computer Interaction
Ethics, Equity, Inclusion
Media Mention
How Culture Shapes What People Want From AI
Chunchen Xu, Xiao Ge, Daigo Misaki, Hazel Markus, Jeanne Tsai
May 11, 2024
Research
Your browser does not support the video tag.

There is an urgent need to incorporate the perspectives of culturally diverse groups into AI developments. We present a novel conceptual framework for research that aims to expand, reimagine, and reground mainstream visions of AI using independent and interdependent cultural models of the self and the environment. Two survey studies support this framework and provide preliminary evidence that people apply their cultural models when imagining their ideal AI. Compared with European American respondents, Chinese respondents viewed it as less important to control AI and more important to connect with AI, and were more likely to prefer AI with capacities to influence. Reflecting both cultural models, findings from African American respondents resembled both European American and Chinese respondents. We discuss study limitations and future directions and highlight the need to develop culturally responsive and relevant AI to serve a broader segment of the world population.

How Culture Shapes What People Want From AI

Chunchen Xu, Xiao Ge, Daigo Misaki, Hazel Markus, Jeanne Tsai
May 11, 2024

There is an urgent need to incorporate the perspectives of culturally diverse groups into AI developments. We present a novel conceptual framework for research that aims to expand, reimagine, and reground mainstream visions of AI using independent and interdependent cultural models of the self and the environment. Two survey studies support this framework and provide preliminary evidence that people apply their cultural models when imagining their ideal AI. Compared with European American respondents, Chinese respondents viewed it as less important to control AI and more important to connect with AI, and were more likely to prefer AI with capacities to influence. Reflecting both cultural models, findings from African American respondents resembled both European American and Chinese respondents. We discuss study limitations and future directions and highlight the need to develop culturally responsive and relevant AI to serve a broader segment of the world population.

Design, Human-Computer Interaction
Sciences (Social, Health, Biological, Physical)
Your browser does not support the video tag.
Research
Emma Lundberg
Associate Professor of Bioengineering and of Pathology
Person

Emma Lundberg

Associate Professor of Bioengineering and of Pathology
Sciences (Social, Health, Biological, Physical)
Person
Inside the AI Index: 12 Takeaways from the 2026 Report
Shana Lynch
Apr 13, 2026
News

The annual report reveals a field hitting breakthrough capabilities while raising urgent questions about environmental costs, transparency, and who benefits from the technology.

Inside the AI Index: 12 Takeaways from the 2026 Report

Shana Lynch
Apr 13, 2026

The annual report reveals a field hitting breakthrough capabilities while raising urgent questions about environmental costs, transparency, and who benefits from the technology.

Economy, Markets
Education, Skills
Energy, Environment
Ethics, Equity, Inclusion
Finance, Business
Generative AI
Healthcare
Regulation, Policy, Governance
Workforce, Labor
Sciences (Social, Health, Biological, Physical)
Robotics
News
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