Pre-Submission Review for Human-Computer Interaction Papers

HCI reviewers often ask:

• what contribution type is this: empirical, system, design, theory, method, dataset, or critique?
• is the subcommittee or venue the right home?
• are participants, recruitment, context, and consent described well enough?
• does the method match the interaction claim?
• are qualitative, quantitative, design, and artifact evidence integrated rather than stacked?
• are ethics, privacy, anonymity, and potential harms addressed?
• are accessibility and inclusion considered where relevant?
• are materials, code, instruments, or supplementary artifacts available enough for transparency?
• do design implications follow from the evidence?

The manuscript has to tell reviewers how to evaluate it.

In our pre-submission review work, HCI papers most often fail when authors make reviewers guess the paper's contribution category.

Contribution blur: the paper reads like a system, study, and design essay at once, without naming the main contribution.

Participant-context gap: recruitment, setting, demographics, expertise, compensation, accessibility, or consent details are too thin for interpretation.

Interaction evidence mismatch: the paper claims changed use, experience, trust, usability, or collaboration from evidence that only shows feasibility.

Design implication overreach: recommendations sound broader than the study population or design context supports.

Ethics thinness: privacy, sensitive data, workplace power, clinical settings, minors, marginalized groups, or AI-mediated interaction risks are handled too late.

A useful review should identify the first HCI-specific reason a reviewer would downgrade the work.

CHI author guidance emphasizes subcommittee selection, transparency, ethics, accessibility, and supplementary material where appropriate. CHI reviewing guidance asks reviewers to evaluate work using the criteria for the submission type and to consider transparency in different contribution categories. ACM review training also asks reviewers to notice whether methods are detailed enough to evaluate reproducibility and whether ethical issues are present.

Those signals mean HCI readiness is not just "did we run a user study?" It is whether the study, design, artifact, and contribution type align.

Send the manuscript, target venue or subcommittee, study protocol, recruitment materials, consent language, instruments, interview guide, codebook, system screenshots, artifact or demo access if available, analysis plan, ethics approval or exemption notes, supplementary materials, and prior reviewer comments if available.

If the work involves AI, sensitive data, children, patients, employees, disabled users, or marginalized communities, include the risk and consent context.

A useful HCI pre-submission review should include:

• HCI contribution verdict
• venue and subcommittee fit critique
• participant and study-design review
• interaction-evidence check
• ethics, privacy, and accessibility review
• transparency and artifact-readiness note
• submit, revise, retarget, or diagnose deeper call

The review should not only say "add more user evidence." It should identify which interaction claim needs support.

Before submission, authors often need to:

• name the contribution type in the introduction
• align the title and abstract with the target subcommunity
• add participant and setting detail
• separate feasibility, usability, experience, and impact claims
• make ethics and privacy reasoning explicit
• move design implications closer to evidence
• include instruments, codebook, artifact notes, or supplementary materials
• retarget from CHI to CSCW, DIS, UIST, IMWUT, TOCHI, or a domain venue

These fixes help reviewers evaluate the paper on the right terms.

A system paper needs interaction novelty, use case, evaluation, and comparison to existing tools. A design paper needs design rationale, iteration, evidence, and contribution beyond the artifact. An empirical paper needs sampling, method fit, analysis transparency, and interpretation restraint. A human-AI paper needs user task, model role, trust, failure, and harm context. An accessibility paper needs community relevance, inclusive methods, and careful generalization. A CSCW paper needs collaboration, power, organization, and socio-technical context.

The AI manuscript review can flag whether the blocking risk is contribution type, study design, ethics, interaction evidence, or venue fit.

Use this page when the manuscript's submission risk depends on human use, participant evidence, interaction design, usability, qualitative or mixed-methods rigor, ethics, or HCI venue fit. Use ML review when the model or benchmark is the primary contribution. Use AI review when governance, system policy, or broad AI framing dominates.

That distinction keeps the page focused on the HCI buyer's actual problem.

Do not submit an HCI paper if the contribution type is still ambiguous. Reviewers can be generous across methods, but they need to know whether the manuscript is asking to be judged as a system, empirical study, design contribution, theory paper, method paper, or critique.

Also pause if the participant story is thin. HCI claims often depend on who used the system, under what conditions, with what power dynamics, and with what risks. Those details should be visible before the results section.

For human-AI papers, pause again if the manuscript treats model performance and user experience as interchangeable. A model can perform well while creating poor interaction, misplaced trust, new workload, or harm.

For prototype papers, pause if the artifact cannot be inspected well enough for reviewers to understand the interaction. Screenshots, task flows, prompts, study materials, and failure cases often matter as much as the reported effect size.