The 1st Human–AI Interaction Conference
A dedicated venue for the science, design, and practice of how people and AI systems work together.
Why a new conference
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
Collaborative Intelligence & Teaming
How people and AI divide the work and the authority between them, and how a person keeps meaningful oversight of behavior that runs long or carries real risk.
Societal Impact of Human–AI Systems
Who gains and who loses as these systems enter workplaces and institutions, and what kind of oversight lets one be deployed safely without stalling it.
Interfaces, Techniques & Interactive Systems
Interaction past the chat box: direct manipulation, shared representations of state and intent, sensing and context, and the toolkits people build them with.
Evaluation, Methodology & Theories
How to tell whether a human–AI system actually works. Measures for collaboration quality, and studies that run long enough and close enough to real use to find out.
Human–AI Collaboration in Scientific Discovery
Research itself as a site of collaboration, from forming a hypothesis to running an autonomous lab and vouching for what comes out of it.
Call for Papers
HAIC invites work that examines humans and AI systems (including generative and agentic AI) in shared activity, with an emphasis on formalizing and evaluating the mechanisms, interactions, and contexts that make interaction effective.
Important dates
- December 2026Submission portal opens
- January 21, 2027Abstract deadlinePapers and Industry & Practice Track.
- January 28, 2027Submission deadlinePapers and Industry & Practice Track.
- To be announcedDemo submission deadline
- To be announcedDoctoral Consortium application deadline
- March 25, 2027First-round reviews releasedReviews are made available to authors ahead of the rebuttal period.
- March 25 – April 1, 2027Rebuttal periodAuthors respond to reviews.
- April 15, 2027Author notification
- April 29, 2027Camera-ready deadline
- June 27–30, 2027HAIC 2027, Washington DC
Scope
Human–AI interaction is already studied at HCI and ML/AI/NLP venues. No venue treats it as the central problem. HAIC is a dedicated venue for the science, design, and practice of how people and AI systems work together.
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 designs that shape human–AI systems: interaction design and collaboration protocols, representations of shared state and intent, mechanisms for delegation, steering, oversight, and control, and methods for measuring outcomes such as 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
- 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 in human teams
- Learning, skill development, deskilling, reskilling, and expertise in AI-augmented work
- Human oversight and control of multi-step, long-horizon, or safety-critical AI behavior
Societal Impact of Human–AI Systems
- Fairness, accountability, transparency, and safety in human–AI systems
- Human agency, autonomy, dignity, and power in human–AI systems
- Workforce transformation: AI’s impact on the workforce and harms mitigation
- Privacy, security, autonomy, and consent in AI-mediated interactions
- Evidence-based governance and oversight that enables safe and effective deployment, such as impact and risk assessment, post-deployment monitoring, and human oversight
Interfaces, Interaction Techniques & Interactive Systems
- Novel interfaces and interaction techniques for human–AI interaction
- Embodied AI systems: 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 human–AI collaboration quality
- Reproducibility and replication, including open datasets, shared study materials, and machine-auditable artifacts
- Rigorous evaluation of deployed systems: pre-registered, longitudinal, or 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
Special themes
Three themes cut across the topics above and are highlighted in this first edition. Submissions on topics outside these themes but related to human–AI interaction are welcome. Papers submitted to the special themes go through the same reviewing process as all other papers; accepted papers are assigned to special sessions at the conference.
Responsible, Inclusive, Safe, and Empowering AI
AI systems that are trustworthy, safe, inclusive, and aligned with people’s values. Responsible AI development and design that mitigate harms to people and communities. Human–AI interactions that empower people and augment human capabilities.
Human–AI Interaction for Scientific Discovery
Human–AI collaboration across the scientific workflow, from hypothesis generation, analysis, and writing to peer review. Scientific foundation models, autonomous laboratories, AI-mediated collaboration between scientists, and AI-enabled verification all assume researchers who can steer these systems and vouch for their output.
Human–AI Interaction for Learning
Learning, tutoring, assessment, and skill formation when AI is a constant collaborator. With technical talent now central to national research strategy, how people build, keep, and lose expertise alongside capable systems becomes a research question.
Submission categories
| Category | Length | Anonymity | Selection | Archival | Description |
|---|---|---|---|---|---|
| Papers | Up to 15 pages | Double-blind | Reviewed | Yes | Original research contributions: completed studies, system designs, or theoretical frameworks. Paper length should be commensurate with the contribution. |
| Industry & Practice Track | Up to 10 pages | Single-blind | Reviewed | Yes, by default; authors may opt out | 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 | Up to 5 pages | Single-blind | Curated | No | Interactive demonstrations of systems or tools, and visual presentations of emerging research. |
| Doctoral Consortium | Up to 5 pages | Single-blind | Curated | No | 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 are available in the submission template.
Review and selection
Reviewed: Papers and Industry & Practice Track
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.
Industry & practice 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.
Curated: Demos and Doctoral Consortium
Demos and doctoral consortium applications are curated by the respective chairs rather than peer-reviewed, are submitted non-anonymously for single-blind consideration, and are not included in the archival proceedings.
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.
Submission policy
Dual submissions. Papers submitted to HAIC (i.e., those in the review process after the abstract deadline has passed) cannot be previously published or under review for another conference or journal. If your paper was submitted to another venue, it must be withdrawn from that venue prior to the HAIC paper submission deadline. If you want to submit your paper to another venue after HAIC, you must withdraw it from HAIC prior to submitting the full paper to that venue. Concurrent submissions to non-archival workshops (i.e., workshops without proceedings) do not count as double submissions for the purpose of this policy.
Proceedings. All accepted papers will be published in the official conference proceedings, which will be made publicly available through the conference website and will serve as the archival record of the conference. Accepted Industry & Practice Track reports are also included by default, but their authors may opt out. The proceedings will be published independently and will not be included in digital libraries such as the ACM Digital Library or IEEE Xplore. Submissions are included following the completion of peer review and submission of the final camera-ready version.
Use of large language models
HAIC 2027 adopts the COLM 2026 Policy on Large Language Model Usage.
1. Disclose LLM use in both research and reviewing.
- In research, use of LLMs should be disclosed in the submitted paper, except for minor usage in paper writing or code implementation. Must be disclosed: using an LLM to originate research ideas, to write original content in the paper (including references), to generate data or plots, or for evaluation. Okay not to disclose: automated grammar checking, asking an LLM to fix typos or improve the writing of a paragraph, or programming with a tool like Claude, Copilot, or Cursor.
- In reviewing, any use of an LLM should be disclosed, whether cosmetic or not.
2. Authors and reviewers are ultimately responsible for their contributions, following the Code of Ethics that “researchers must not deliberately make false or misleading claims, fabricate or falsify data, or misrepresent results.”
Submission template
The submission template is available at github.com/haic-conference/paper-template.
Submission portal
The submission portal is not open yet. It opens in December 2026.
Program
Alongside the main technical program, HAIC runs a practice and deployment track and an in-person doctoral consortium.
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.
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.
Committee
Organizing committee
General Chairs
Paper Chairs
Practice & Deployment Track Chairs
Panel Chairs
Doctoral Consortium Chairs
Theme Chairs: Human–AI Interaction for Scientific Discovery
Theme Chairs: Responsible, Inclusive, Safe, and Empowering AI
Theme Chairs: Human–AI Interaction for Learning
Proceedings Chairs
Sponsorship Chairs
Student Volunteer Chairs
Accessibility Chair
Publicity Chairs
Steering committee
Nitesh Chawla
University of Notre Dame
Mark Dredze
Johns Hopkins University
Brent Hecht
Microsoft · Northwestern University
Jeffrey Heer
University of Washington
Karrie Karahalios
Massachusetts Institute of Technology
Meredith Ringel Morris
Google DeepMind
Daniel S. Weld
University of Washington · Allen Institute for AI
Program committee
The program committee reviews submissions to the paper and report tracks.
Venue & travel
HAIC 2027 takes place in Washington, DC. Venue and travel details coming soon.
Registration
More details coming soon.
Sponsorship
HAIC welcomes sponsorship from industry, foundations, and other organizations working at the frontier of human–AI collaboration.
Sponsorship tiers
Tier details and pricing coming soon.
Institutional support
