AI & Technology

Measures how often people use an entertainment chatbot (companionship, roleplay, humor, storytelling), what keeps them coming back, and where the experience falls flat — with an AI follow-up that reconstructs a specific memorable conversation to surface what actually made it feel fun, believable, or disappointing. Built for product and content teams shipping character or entertainment-focused AI experiences.

Evaluates how well an airline or travel site's AI chatbot handles real booking, change, and support tasks — covering task completion, trust, and where users bail out to a human. An AI follow-up interview reconstructs exactly what happened in the respondent's most recent chatbot session, not just how they'd rate it in hindsight.

Measures how well an AI scheduling chatbot helps patients book, reschedule, or get answers about medical appointments, covering task completion, trust, and friction points — with an AI follow-up interview that digs into the specific moment the chatbot helped or failed.

Measures how much time and cognitive effort employees spend checking AI agent outputs, where trust is over- or under-calibrated, and what triggers a full manual re-check. An AI follow-up probes the last time output was wrong or nearly acted on unchecked.

A survey studying how research teams evaluate, adopt, and integrate AI tools for data collection, analysis, and reporting. This instrument measures current tool usage, evaluation criteria, adoption barriers, training experiences, data quality perceptions, and team collaboration patterns.

A repeated-measures survey template designed to track how participant comfort, trust, and naturalness perceptions of AI interviewers evolve across multiple sessions. Administer at each study wave with consistent scaling to enable within-subjects change analysis.

A controlled comparison instrument for evaluating interview experiences across different moderator formats. This survey measures pre-interview expectations, embeds an interview session, and captures post-interview evaluations of comfort, quality, depth, trust, and willingness to participate again.

A research survey exploring how faculty and students adopt, perceive, and experience AI tools in higher education settings. Covers current usage patterns, perceived benefits and barriers, institutional policy awareness, training needs, and impact on teaching and learning outcomes.

Captures frontline warehouse worker feedback on safety conditions, throughput changes, role clarity, and operational tools to identify improvement priorities and support OSHA compliance.

Measures VR players' motion sickness susceptibility, symptom frequency, discomfort triggers, and comfort technique preferences to guide UX design decisions for virtual reality games and experiences.

Measures developer experience, tooling preferences, risk perceptions, and adoption intent for synthetic data. Designed for engineering and data science teams evaluating synthetic data readiness and ethical boundaries.

Measures employee AI tool usage patterns, shadow AI risks, policy awareness, and training needs to inform governance and safe-adoption strategies across the organization.

A stimulus-comparison survey for UX researchers and AI product teams to evaluate the clarity, tone, and helpfulness of AI safety and refusal messages. Produces actionable data on user preferences and improvement priorities.

Assesses how users find, customize, and derive value from a shared AI prompt library. Use this to identify discovery friction, reuse patterns, and outcome perceptions to prioritize product improvements.

Measures software developers' perceptions of fairness, bias, and representativeness in their evaluation practices. Ideal for engineering leadership and DEI teams seeking to identify gaps in evaluation methodology and build more inclusive processes.

Measures how AI users experience error reporting workflows and how unresolved issues affect trust and future reporting intent. Designed for product and UX teams seeking to reduce reporting friction and improve AI reliability perceptions.

Measures user experiences with AI errors, recovery preferences, and resulting trust impact. Designed for AI product teams seeking to prioritize reliability improvements and reduce error-driven churn.

Measures consumer trust in generative AI tools, perceived safety risks, transparency expectations, and guardrail preferences to inform responsible AI product design and policy.

Measures consumer awareness, trust, privacy concerns, and adoption intentions regarding on-device AI training. Designed for product, UX, and privacy teams seeking to understand how users perceive and evaluate local AI learning features.

Measures user attitudes toward AI transparency, desired controls, and recourse expectations. Designed for product teams assessing trust gaps and prioritizing AI governance improvements.