The 1st Human–AI Interaction Conference
Human–AI
Interaction
A dedicated venue for the science, design, and practice of how people and AI systems work together.
Human–AI interaction is already studied at CHI, CSCW, and UIST; at NeurIPS, ICML, ICLR, and COLM; at ACL, CVPR, and HRI; and at FAccT and AIES. No venue treats it as the central problem.
HAIC is built for work that examines people and AI systems as collaborators in shared activity — the protocols, representations, and mechanisms that make that collaboration work, and the methods that let us measure whether it does.
Frontier capabilities now move substantially within a single publication cycle. HAIC keeps pace through deep participation from the teams building and deploying frontier systems, a dedicated practice and deployment track, and review timelines calibrated to fast-moving capabilities.
Engagement with frontier AI
The program is designed to track foundation models and agentic systems as they are actually built, and to study collaboration on the most capable systems available rather than yesterday's.
Translational impact
Moving findings into deployed systems across industry, science, healthcare, education, manufacturing, and public services — and bringing deployment lessons back into research.
Topic areas
Full scope →Collaborative Intelligence & Teaming
Division of labor, authority, and decision-making between people and AI; multi-agent and multi-human systems; supervision of long-horizon and safety-critical behavior; deskilling and reskilling.
Ethics, Safety & Societal Impact
Fairness, accountability, agency, and power; workforce transformation; privacy and consent; evidence-based governance and oversight that enables safe, timely deployment.
Interfaces, Techniques & Interactive Systems
Interaction paradigms beyond conversation; hardware, sensing, and context awareness; toolkits and architectures; transparency, controllability, and trust calibration.
Evaluation, Methodology & Theories
Measures and benchmarks for collaboration quality; reproducibility and machine-auditable artifacts; longitudinal, field, and in-the-wild studies; theoretical frameworks.
Human–AI Collaboration in Scientific Discovery
Hypothesis generation to writing; oversight of autonomous experimentation and closed-loop labs; interfaces for scientific foundation models; trust and verification in AI-assisted science.
Design Systems · AI for Science · AI for Education
Dedicated tracks with demos, tutorials, and hands-on sessions.
See the program →What you can submit
| Category | Length | Anonymity | Selection | Notes |
|---|---|---|---|---|
| Full Papers | 10 pages | Double-blind | Reviewed | Completed studies, system designs, theoretical frameworks. |
| Short Papers | 4 pages | Double-blind | Reviewed | Preliminary findings, provocations, late-breaking work. |
| Practice & Deployment Reports | 8 pages | Single-blind | Reviewed | Real-world practice and deployment experience. Negative results welcome. |
| Demos | 4 pages | Single-blind | Curated | Interactive demonstrations of systems and tools. |
| Doctoral Consortium | 4 pages | Single-blind | Curated | In-person mentoring for PhD students. |
Page limits exclude references. Templates and formatting instructions will be posted here.
Submissions · HAIC 2027
Call for Papers
HAIC invites work that examines humans and AI systems as collaborators in shared activity, with an emphasis on formalizing and evaluating the mechanisms that make collaboration effective.
Scope
We are especially interested in research that moves beyond treating AI as a standalone model, tool, or intervention, and instead investigates the computational, interactional, and organizational structures that shape human–AI systems: protocols for coordination, representations of shared state and intent, mechanisms for delegation, steering, oversight, and control, and methods for measuring joint performance, robustness, and reliability under real-world conditions.
Topics of interest include, but are not limited to, the following.
Collaborative Intelligence & Teaming
- Human–AI teaming in professional, creative, educational, scientific, and civic domains — including healthcare, education, advanced manufacturing, robotics and physical-world work, and public services
- Division of labor, authority, perception, cognition, and decision-making between humans and AI
- Multi-agent and multi-human–AI systems
- Cognitive and social dynamics in AI-augmented collaboration
- Learning, skill development, deskilling, reskilling, and expertise in AI-augmented work
- Human supervision of multi-step, long-horizon, or safety-critical AI behavior
- Alignment of AI behavior with human preferences, values, goals, and institutional constraints
Ethics, Safety & Societal Impact
- Fairness, accountability, and safety in human–AI systems
- Human agency, autonomy, dignity, and power in AI-mediated systems
- Workforce transformation: an AI-ready and skilled technical workforce, reskilling, job displacement
- Privacy, security, autonomy, and consent in AI-mediated interactions
- Evidence-based governance and oversight that enables safe, effective, timely deployment — sandboxed and staged evaluation, post-deployment monitoring, human oversight as an enabler of adoption
- Designing AI systems that work for all users, including rural, older, disabled, and low-resource populations
Interfaces, Interaction Techniques & Interactive Systems
- Novel interfaces and interaction techniques for human–AI collaboration
- Hardware and devices: wearables, AR/VR, haptics, robotic embodiments
- Sensing, input techniques, and context awareness for capturing human state, intent, and attention
- Systems, architectures, toolkits, and development infrastructure
- Interaction paradigms beyond conversation: direct manipulation, mixed-initiative, and shared-representation interfaces for generative and agentic AI
- Transparent, explainable, and controllable interactive systems
- Mental models, trust calibration, and over- or under-reliance
- Accessibility and inclusive design for AI-augmented tools
Evaluation, Methodology & Theories
- Measures, metrics, and benchmarks for collaboration quality
- Reproducibility and replication, including open datasets, shared study materials, and machine-auditable artifacts
- Rigorous evaluation of deployed systems: pre-registered, longitudinal, and post-deployment studies
- Mixed-methods, longitudinal, field-based, participatory, and in-the-wild studies
- Experimental, computational, ethnographic, design-based, and critical methods
- Datasets, tools, and infrastructure for collaborative research
- Theoretical frameworks for collaboration, agency, trust, responsibility, and sociotechnical systems
Human–AI Collaboration in Scientific Discovery
- Collaboration across scientific workflows, from hypothesis generation to analysis and writing
- Human oversight, steering, and verification of autonomous experimentation and closed-loop laboratories
- Interfaces and interaction paradigms for scientific foundation models and AI research assistants
- Reproducibility, verification, and trust in AI-assisted science
- Collaboration in the institutions of science itself: peer review, publication, and credit attribution
- Measuring the productivity, reliability, and epistemic impact of AI-augmented research
Submission categories
| Category | Length | Anonymity | Selection | Description |
|---|---|---|---|---|
| Full Papers | 10 pages | Double-blind | Reviewed | Original research contributions presenting completed studies, system designs, or theoretical frameworks. |
| Short Papers | 4 pages | Double-blind | Reviewed | Focused contributions including preliminary findings, provocations, or late-breaking work. |
| Practice & Deployment Reports | 8 pages | Single-blind | Reviewed | Case studies and experience reports on human–AI systems in real-world practice and deployment, authored by or with industry and application-domain teams. Negative results and lessons learned are explicitly welcome. |
| Demos | 4 pages | Single-blind | Curated | Interactive demonstrations of systems or tools, and visual presentations of emerging research. |
| Doctoral Consortium | 4 pages | Single-blind | Curated | Applications for structured, in-person mentoring for PhD students working at the intersection of HCI and AI. |
All page limits exclude references. Templates and detailed formatting instructions will be posted here before the portal opens.
Review and selection
Papers and reports
Full papers, short papers, and practice & deployment reports undergo peer review.
Papers are evaluated on novelty and significance of contribution, clarity and quality of presentation, methodological rigor or design quality, and relevance to the conference scope. They are submitted anonymized for double-blind review.
Practice & deployment reports are evaluated on the significance, credibility, and transferability of the reported experience rather than research novelty. Because organizational identity is often intrinsic to these reports, they are submitted non-anonymously for single-blind review.
Demos and doctoral consortium
Demos and doctoral consortium applications are curated by the respective chairs rather than peer-reviewed, and are submitted non-anonymously for single-blind consideration.
Demos are selected for the interest and quality of the system or artifact and its suitability for live demonstration.
Doctoral consortium participants are selected on the quality and promise of the student’s research and on what the student stands to gain from the mentoring program.
At least one author of each accepted submission must register and present at the conference.
Schedule · HAIC 2027
Important dates
The timeline below is proposed and subject to confirmation by the organizing committee. Deadlines are anchored to fall after CHI notification.
- LATE JAN / EARLY FEB 2027Submission portal opens · full submission deadlineFull papers, short papers, practice & deployment reports, demos, and doctoral consortium applications.
- TBDRebuttal periodAuthors respond to reviews.
- TBDAuthor notification
- TBDCamera-ready deadline
- JUNE 27–30, 2027HAIC 2027, Washington DCHopkins Bloomberg Center, 555 Pennsylvania Avenue NW.
Dates not yet final
Everything between the portal opening and the conference is still being set. Sign up for the mailing list to be notified when the full timeline is published.
HAIC 2027
Program & tracks
Alongside the main technical program, HAIC runs special tracks, a practice and deployment track, and an in-person doctoral consortium.
Special tracks
Design Systems for Human-Centered AI
Design principles, patterns, and systems for building human–AI interaction, with demos, tutorials, and hands-on workshops that put them to work.
AI for Science
Human–AI collaboration across the scientific workflow, from hypothesis generation through autonomous experimentation to peer review.
AI for Education
Learning, tutoring, assessment, and the classroom as a site of human–AI collaboration.
Doctoral Consortium
The HAIC Doctoral Consortium provides structured, in-person mentoring for PhD students working at the intersection of HCI and AI — a stage at which research creativity is often highest but institutional support is weakest.
US-based students at this boundary currently have few in-person mentoring venues at this scale. Building a strong consortium is a founding priority of the conference, not an add-on.
What the consortium commits to
- Recruiting students from institutions across all states and regions, not only major research hubs, with decisions based on the quality and promise of the student's research
- Pairing students with mentors from academia and industry
- Dedicated sessions on research careers and funding pathways across academia and industry
- Travel support for students with financial need, subject to sponsorship
Practice & deployment
A dedicated track for teams shipping human–AI systems. Reports are evaluated on the significance and transferability of their insights rather than research novelty, and may be submitted non-anonymously where organizational identity is intrinsic to the account.
Special sessions convened with agencies, foundations, and industry run alongside the technical program.
Negative results welcome
Deployments that did not work, and what the team learned, are as useful to this community as the ones that did.
Organizers · HAIC 2027
Committee
The HAIC 2027 committee is being formed across the human–computer interaction and AI research communities, in academia and industry. Names will be announced as roles are filled.
Organizing committee
General Chairs
Paper Chairs
Practice & Deployment Chairs
Late-Breaking Work Chairs
Demo Chairs
Doctoral Consortium Chairs
Sponsorship Chairs
Local Chairs
Web & Design Chairs
Student Volunteer Chairs
Accessibility Chair
Social Media Chair
Steering committee
Program committee
The program committee reviews submissions to the paper and report tracks. Members will be announced alongside the call for papers.
Washington, DC · HAIC 2027
Venue & travel
HAIC 2027 is hosted at the Hopkins Bloomberg Center, on Pennsylvania Avenue between the White House and the Capitol.
555 PENNSYLVANIA AVENUE NW, WASHINGTON, DC 20001
The Hopkins Bloomberg Center is Johns Hopkins University’s Washington home for research, teaching, and public convening. HAIC is hosted there with logistical support from Johns Hopkins Data Science and AI.
The main venue seats up to 400 participants, with additional space for pre- and post-conference workshops and smaller gatherings nearby.
The location puts the conference within walking distance of the agencies, foundations, and policy institutions whose participation the program depends on.
Getting there
Metro
The venue sits in the middle of the Metro network. Five stations are within a short walk:
- Archives–Navy Memorial–Penn QuarterGreen · Yellow4 MIN WALK
- Judiciary SquareRed8 MIN WALK
- Gallery Place–ChinatownRed · Green · Yellow12 MIN WALK
- Federal TriangleBlue · Orange · Silver13 MIN WALK
- L’Enfant PlazaBlue · Orange · Silver · Green · Yellow13 MIN WALK
By air
- Ronald Reagan National (DCA)Metro Blue or Yellow line, or a short taxi, Uber, or Lyft ride20 MIN
- Dulles International (IAD)Metro Silver line, or taxi, Uber, or Lyft~1 HOUR
- Baltimore/Washington (BWI)MARC or Amtrak to Union Station, then Metro~1 HOUR
DCA is the closest airport and the simplest arrival for most attendees.
By train
Amtrak, MARC, and VRE all serve Union Station, roughly a 20-minute walk from the venue, with Metro and bus connections for the last leg.
By bus, car, and bike
Several Metrobus routes stop nearby. Taxis, Uber, and Lyft serve the area throughout the day. Paid street parking is available and several garages are within walking distance, though Metro is usually faster. Capital Bikeshare docks are on nearby blocks.
Accommodation
More details coming soon.
Attend · HAIC 2027
Registration
More details coming soon.
Partners · HAIC 2027
Sponsorship
HAIC welcomes sponsorship from industry, foundations, and other organizations working at the frontier of human–AI collaboration.
Sponsorship tiers
Placeholder amounts — not final- Premier logo placement on the website, program, and on-site signage
- Keynote or plenary session hosting
- Exhibit space in the main hall
- Eight complimentary registrations
- Recruiting table and access to the career session
- Named support of a doctoral consortium cohort
- Logo placement on the website, program, and signage
- Sponsored session or reception
- Exhibit space
- Five complimentary registrations
- Recruiting table
- Logo placement on the website and program
- Exhibit space
- Three complimentary registrations
- Logo placement on the website and program
- One complimentary registration
- Named travel awards for students with financial need
- Named support of the consortium program and mentoring sessions
- Named award across the paper and demo programs
Custom packages available. A detailed prospectus is available on request.
Institutional support
