How should designers structure human-GenAI interaction systems?
This explores whether a six-dimensional framework—purposes, feedback, control, engagement, domains, and evaluation—provides a useful structure for organizing the scattered design considerations across human-GenAI interaction research.
Reviewing a corpus of 291 papers, the authors of this HCI survey "synthesize the research fields from both the user and GenAI perspectives... into the following dimensions of the design space: 1) Purposes of Using GenAI, 2) Feedback from Models to Users, 3) Control from Users to Models, 4) Levels of Engagement, 5) Application Domains, and 6) Evaluation Strategies." The paper states this is meant to consolidate a literature where "key design considerations, common practices, and future research opportunities are still hidden and scattered across many broad topics," giving researchers a single framework to situate new systems within.
The taxonomy follows a User-Centered Design logic the authors spell out explicitly: a system is designed by "(1) orienting the system through the needs of the end-users... (2) considering the capabilities and functions of the GenAI models... (3) exploring the possible actions of the users, the corresponding results of the actions, and the mapping between the actions and the results... (4) enabling different levels of engagement in the interaction for different tasks... (5) deciding the application domain the system can be generalized into... (6) and evaluating the system via designated metrics." The taxonomy itself emerged from a multi-stage collaborative coding process: each author first tagged a subset of 25 papers, the group iteratively merged and split categories, and three authors then coded the full corpus, opening new discussion whenever a paper resisted the existing categories.
This sits one level above narrower taxonomies in the vault that cover a single slice of the same space. How should generative UI be organized as a design space? proposes audience, constancy, control, and timeframe as axes specifically for generative UI — a framework that maps most directly onto this survey's "Control from Users to Models" and "Levels of Engagement" dimensions, applied to one class of GenAI output. Similarly, Do generated analysis UIs really work better than chat? proposes a three-axis trade-off framework (malleability, specification, interoperability) for one application domain this survey's sixth dimension would file under "Application Domains." Read together, this survey functions as the umbrella structure those narrower, system-specific taxonomies instantiate.
The six dimensions were derived by qualitative consensus among three authors reading and tagging papers, not from a controlled study or a formal ontology-validation method, and workshop proposals, surveys, and literature reviews were deliberately excluded from the final tally and used only as supplementary guidance. The excerpt does not establish which dimension matters most for any particular design decision, nor does it validate that the six dimensions are jointly exhaustive or mutually exclusive — only that they recurred often enough across 291 papers for three readers to agree on them as a shared vocabulary.
Inquiring lines that read this note 16
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
Does generative AI shift knowledge work away from collaborative tasks?- What does generative UI design space split into beyond audience and timeframe?
- Why do narrower taxonomies for specific GenAI systems nest within this broader framework?
- Which evaluation metrics best measure success in human-GenAI interaction systems?
- What are the four dimensions that define generative UI design space?
- What dimensions organize the design space of generative user interfaces?
- Does Atlassian's approach differ from other AI interaction design frameworks?
- Should interface design organize by pattern type or by behavioral phase?
- How do direct manipulation and cognitive load theory guide interface design choices?
- How does vendor-prescribed design doctrine compare to frameworks derived from user research?
- Do Nielsen's three-layer interfaces resolve tension between intent surfaces and direct manipulation?
- How do Amershi's interaction-phase guidelines compare to concern-based AI UX frameworks?
- Why do evaluation scenarios need to reproduce interaction mode dimensions?
- How does feedforward design differ from feedback in human-computer interaction?
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How should generative UI be organized as a design space?
Researchers propose four dimensions for understanding generative UI systems: who they serve (designers or end-users), whether the interface changes, who controls those changes, and how long interactions last. This framework helps HCI practitioners design and evaluate AI-generated interfaces.
a narrower taxonomy for generative UI alone that maps onto this survey's control and engagement dimensions
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Do generated analysis UIs really work better than chat?
TaskArtisan investigates whether putting a GUI into LLM-assisted analysis workflows improves usability and clarity, and what trade-offs emerge when analysts need to modify or reuse generated interfaces.
a domain-specific trade-off framework within the application-domain slice of this survey's design space
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Why do GenAI interfaces favor sliders over voice and gesture?
GenAI research shows a 5-to-1 preference for direct controls like sliders and text over intuitive modalities like voice and gesture. Understanding this gap could reveal barriers to more natural interaction design.
this survey's own empirical finding instantiating its "Control from Users to Models" dimension
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How does GenAI's unpredictability break traditional lab study methods?
How does GenAI's non-deterministic output destabilize the controlled conditions that HCI lab studies depend on? Understanding this gap matters for designing evaluation methods that actually capture how people interact with unpredictable AI systems.
Evidence for A's evaluation dimension: GenAI's non-determinism creates specific methodological challenges in lab-based evaluation
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions
- Evaluating Generative AI in the Lab: Methodological Challenges and Guidelines
- Interaction-Augmented Instruction: Modeling the Synergy of Prompts and Interactions in Human-GenAI Collaboration
- Feedforward in Generative AI: Opportunities for a Design Space
- Ironies of Generative AI: Understanding and mitigating productivity loss in human-AI interactions
- What does Generative UI mean for HCI Practice?
- Prototyping Multimodal GenAI Real-Time Agents with Counterfactual Replays and Hybrid Wizard-of-Oz
- Human-AI agency in the age of generative AI
Original note title
Human-GenAI interaction design space splits into six dimensions — purposes, feedback, control, engagement, domains, and evaluation