About

John Zimmerman is an Associate Professor with a joint appointment between the School of Design and the Human-Computer Interaction Institute at Carnegie Mellon University. He teaches studios and seminars on interaction design as well as a course on mobile service innovation. His research spans four key areas: the value of digital things, service design and social computing, ubiquitous and mobile computing, and research through design. In the area of digital possessions, he investigates why people often view their digital items as less valuable than traditional objects and explores how changes in form and behavior can enhance their perceived value. His recent projects include an interactive bedroom for teens, an alarm clock for children, and a service that sends postcards from the past. In social computing, Zimmerman studies how social technologies can foster citizen engagement in public service design and planning, exemplified by a crowdsourced real-time transit information system that has collected over 150,000 location traces and earned awards from the FCC and the Intelligent Transportation Society of America. His work in ubiquitous and mobile computing examines how smartphones can serve as intelligent platforms supporting new services, such as systems that learn family routines or detect depression through behavioral changes. Additionally, his research through design investigates how design researchers can create new artifacts to explore speculative futures, and he has co-authored a book on this topic. Before joining Carnegie Mellon, Zimmerman was a senior researcher at Philips Electronics, where he designed interactive television products and intelligent home devices. He holds an MDes in Interaction Design from Carnegie Mellon’s School of Design.

Research topics

• Computer Science
• Cardiology
• Internal medicine
• Medicine
• Computer Security
• Artificial Intelligence
• Psychology
• Public relations
• Internet privacy
• Social psychology
• Data science
• Human–computer interaction
• Surgery
• Knowledge management
• Epistemology

Selected publications

• Group Dynamics in AI Trust Formation: Modeling Attitudinal and Behavioral Trust in Team Decision-Making

  Proceedings of the Human Factors and Ergonomics Society Annual Meeting · 2025-09-01

  articleSenior author

  The increasing integration of Artificial Intelligence (AI) into team-based decision environments necessitates an examination of trust formation that extends beyond the individual user. This context introduces complexities stemming from interpersonal dynamics, heterogeneous individual trust levels, and collective decision processes. This study investigates how group decision-making processes change AI trust dynamics compared to individual settings. We aimed to develop a theoretical framework capturing these multilayered trust dynamics using structural equation modeling. We employed Körber’s Trust in Automation (TiA) scale pre- and post-task. A total of 51 participants, organized into 16 teams, performed a collaborative decision-making exercise (NASA moon survival) using ChatGPT. Structural equation modeling (SEM) was used for analysis. Group trust formation patterns significantly diverged from individual contexts. Attitudinal trust, strongly influenced by collective pe rceptions of AI performance and reliability, was the primary predictor of overall group trust, outweighing behavioral trust (actual usage). Factors like understanding/predictability showed no significant influence in group settings. Group-level dynamics fundamentally alter AI trust formation, challenging individual-centric views. Practical implications include the need for trust-building strategies focused on collective perceptions and experiences. The findings underscore the need for new theoretical models and group-specific trust measurement tools.

  PublisherDOI

• Spatially explicit freshwater eutrophication potential (EP) from water resource recovery facility (WRRF) discharge and mitigation opportunities in the U.S.

  Journal of Cleaner Production · 2025-04-25

  article
  PublisherDOI

• Unremarkable to Remarkable AI Agent: Exploring Boundaries of Agent Intervention for Adults With and Without Cognitive Impairment

  ArXiv.org · 2025-05-20

  preprintOpen access

  As the population of older adults increases, there is a growing need for support for them to age in place. This is exacerbated by the growing number of individuals struggling with cognitive decline and shrinking number of youth who provide care for them. Artificially intelligent agents could provide cognitive support to older adults experiencing memory problems, and they could help informal caregivers with coordination tasks. To better understand this possible future, we conducted a speed dating with storyboards study to reveal invisible social boundaries that might keep older adults and their caregivers from accepting and using agents. We found that healthy older adults worry that accepting agents into their homes might increase their chances of developing dementia. At the same time, they want immediate access to agents that know them well if they should experience cognitive decline. Older adults in the early stages of cognitive decline expressed a desire for agents that can ease the burden they saw themselves becoming for their caregivers. They also speculated that an agent who really knew them well might be an effective advocate for their needs when they were less able to advocate for themselves. That is, the agent may need to transition from being unremarkable to remarkable. Based on these findings, we present design opportunities and considerations for agents and articulate directions of future research.

  PublisherOA PDFDOI

• Emerging Practices in Participatory AI Design in Public Sector Innovation

  2025-04-23 · 3 citations

  articleSenior author
  PublisherDOI

• Can You Keep a Secret? Exploring AI for Care Coordination in Cognitive Decline

  ArXiv.org · 2025-12-14

  preprintOpen accessSenior author

  The increasing number of older adults who experience cognitive decline places a burden on informal caregivers, whose support with tasks of daily living determines whether older adults can remain in their homes. To explore how agents might help lower-SES older adults to age-in-place, we interviewed ten pairs of older adults experiencing cognitive decline and their informal caregivers. We explored how they coordinate care, manage burdens, and sustain autonomy and privacy. Older adults exercised control by delegating tasks to specific caregivers, keeping information about all the care they received from their adult children. Many abandoned some tasks of daily living, lowering their quality of life to ease caregiver burden. One effective strategy, piggybacking, uses spontaneous overlaps in errands to get more work done with less caregiver effort. This raises the questions: (i) Can agents help with piggyback coordination? (ii) Would it keep older adults in their homes longer, while not increasing caregiver burden?

  PublisherOA PDFDOI

• Exploring the Innovation Opportunities for Pre-trained Models

  2025-07-04 · 3 citations

  preprintOpen accessSenior author

  Innovators transform the world by understanding where services are successfully meeting customers' needs and then using this knowledge to identify failsafe opportunities for innovation. Pre-trained models have changed the AI innovation landscape, making it faster and easier to create new AI products and services. Understanding where pre-trained models are successful is critical for supporting AI innovation. Unfortunately, the hype cycle surrounding pre-trained models makes it hard to know where AI can really be successful. To address this, we investigated pre-trained model applications developed by HCI researchers as a proxy for commercially successful applications. The research applications demonstrate technical capabilities, address real user needs, and avoid ethical challenges. Using an artifact analysis approach, we categorized capabilities, opportunity domains, data types, and emerging interaction design patterns, uncovering some of the opportunity space for innovation with pre-trained models.

  PublisherOA PDFDOI

• Making the Right Thing: Bridging HCI and Responsible AI in Early-Stage AI Concept Selection

  2025-07-04 · 4 citations

  articleOpen accessSenior author

  AI projects often fail due to financial, technical, ethical, or user acceptance challenges -- failures frequently rooted in early-stage decisions. While HCI and Responsible AI (RAI) research emphasize this, practical approaches for identifying promising concepts early remain limited. Drawing on Research through Design, this paper investigates how early-stage AI concept sorting in commercial settings can reflect RAI principles. Through three design experiments -- including a probe study with industry practitioners -- we explored methods for evaluating risks and benefits using multidisciplinary collaboration. Participants demonstrated strong receptivity to addressing RAI concerns early in the process and effectively identified low-risk, high-benefit AI concepts. Our findings highlight the potential of a design-led approach to embed ethical and service design thinking at the front end of AI innovation. By examining how practitioners reason about AI concepts, our study invites HCI and RAI communities to see early-stage innovation as a critical space for engaging ethical and commercial considerations together.

  PublisherDOI

• Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Agents to Enhance Young Adults' Career Exploration

  2025-04-24 · 9 citations

  preprintOpen access

  Young adults often encounter challenges in career exploration. Self-guided interventions, such as the letter-exchange exercise, where participants envision and adopt the perspective of their future selves by exchanging letters with their envisioned future selves, can support career development. However, the broader adoption of such interventions may be limited without structured guidance. To address this, we integrated Large Language Model (LLM)-based agents that simulate participants' future selves into the letter-exchange exercise and evaluated their effectiveness. A one-week experiment (N=36) compared three conditions: (1) participants manually writing replies to themselves from the perspective of their future selves (baseline), (2) future-self agents generating letters to participants, and (3) future-self agents engaging in chat conversations with participants. Results indicated that exchanging letters with future-self agents enhanced participants' engagement during the exercise, while overall benefits of the intervention on future orientation, career self-concept, and psychological support remained comparable across conditions. We discuss design implications for AI-augmented interventions for supporting young adults' career exploration.

  PublisherDOI

• Emerging Practices in Participatory AI Design in Public Sector Innovation

  ArXiv.org · 2025-02-25

  preprintOpen accessSenior author

  Local and federal agencies are rapidly adopting AI systems to augment or automate critical decisions, efficiently use resources, and improve public service delivery. AI systems are being used to support tasks associated with urban planning, security, surveillance, energy and critical infrastructure, and support decisions that directly affect citizens and their ability to access essential services. Local governments act as the governance tier closest to citizens and must play a critical role in upholding democratic values and building community trust especially as it relates to smart city initiatives that seek to transform public services through the adoption of AI. Community-centered and participatory approaches have been central for ensuring the appropriate adoption of technology; however, AI innovation introduces new challenges in this context because participatory AI design methods require more robust formulation and face higher standards for implementation in the public sector compared to the private sector. This requires us to reassess traditional methods used in this space as well as develop new resources and methods. This workshop will explore emerging practices in participatory algorithm design - or the use of public participation and community engagement - in the scoping, design, adoption, and implementation of public sector algorithms.

  PublisherOA PDFDOI

• AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development

  ArXiv.org · 2025-02-25

  preprintOpen accessSenior author

  AI systems are often introduced with high expectations, yet many fail to deliver, resulting in unintended harm and missed opportunities for benefit. We frequently observe significant "AI Mismatches", where the system's actual performance falls short of what is needed to ensure safety and co-create value. These mismatches are particularly difficult to address once development is underway, highlighting the need for early-stage intervention. Navigating complex, multi-dimensional risk factors that contribute to AI Mismatches is a persistent challenge. To address it, we propose an AI Mismatch approach to anticipate and mitigate risks early on, focusing on the gap between realistic model performance and required task performance. Through an analysis of 774 AI cases, we extracted a set of critical factors, which informed the development of seven matrices that map the relationships between these factors and highlight high-risk areas. Through case studies, we demonstrate how our approach can help reduce risks in AI development.

  PublisherOA PDFDOI

Recent grants

• CHS: Small: Envisioning and Prototyping Artificial Intelligence Product and Service Innovations with User Experience Designers

  NSF · $532k · 2020–2024

• NRI: FND: Human-Robot Collaboration with Distributed and Embodied Intelligence

  NSF · $732k · 2017–2022

Frequent coauthors

• Jodi Forlizzi

  Carnegie Mellon University

  56 shared

• Anthony Tomasic

  Carnegie Mellon University

  34 shared

• Aaron Steinfeld

  Carnegie Mellon University

  33 shared

• Nevenka Dimitrova

  New York Medical College

  18 shared

• Lalitha Agnihotri

  16 shared

• Marion J. Ball

  15 shared

• William Odom

  Simon Fraser University

  13 shared

• Jason Wiese

  University of Utah

  12 shared

Awards & honors

• Awards from the FCC and from the Intelligent Transportation…