Generative AI is becoming an important way to interact with artificial intelligence, but it is not a universal replacement for software interfaces. Chat is useful for open-ended requests and questions; when a person needs to inspect and revise a specific artifact, a canvas, contextual controls, or a combination of interaction styles may work better. The interface should fit the task and leave people able to review and steer the result.
What does it mean to call GenAI an interface?
A generative AI model produces or transforms content. Its interface is the layer people use to explain what they want, supply relevant material, see what the system returns, and guide what happens next. That layer can be a chat window, but it can also be a document editor with AI controls, a visual canvas, or another environment where generated content is part of an ongoing task.
This distinction matters because model capability alone does not determine whether a tool is useful. People also need a workable way to provide context, inspect output, correct errors, and retain control. Google DeepMind’s February 2025 feature on human-computer interaction (HCI) for artificial general intelligence frames the goal as making AI useful and usable for tasks people value, and identifies interaction techniques, interface design, form factors, evaluation, benchmarks, and data collection as areas for HCI work.
GenAI interaction is more than typing prompts
A 2024 survey of generative AI interfaces describes a prompt as the request that asks a system to do a task, and input as the content or information the request acts on. Both can take different forms. The appropriate method depends on what the task requires, not simply on whether a system offers a chat box.
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| Interaction method | What the person does | Where it can help |
|---|---|---|
| Text, visual, audio, or multimodal input | Gives an instruction or supplies one or more kinds of content. | Useful when the task depends on written context, an image, audio, or a combination of inputs. |
| Selection | Chooses one or several items, or marks an area with a lasso or brush. | Directs assistance to a particular object or portion of existing work. |
| System controls | Uses menus, sliders, or explicit feedback. | Makes options or adjustments visible instead of requiring every choice to be described in a prompt. |
| Object manipulation | Drags and drops, connects, or resizes objects. | Lets people change a result through direct action on the work itself. |
These methods can be combined. For example, a person might select a paragraph in a document, ask for a rewrite, then use visible controls or direct editing to adjust the result. Multimodal input expands what a person can provide; it does not by itself guarantee a more usable experience.
Five interface layouts serve different kinds of work
The 2024 survey identifies five layout patterns. They are alternatives and complements, not a ranking from best to worst.
Rank #2
| Layout | What it puts first | Good fit |
|---|---|---|
| Conversational | A prompt or input area alongside a larger space for responses and history, organized around turns. | Asking questions, exploring possibilities, and requesting text or other generated responses. |
| Canvas | The generated artifact—such as an image, document, code, visualization, or audio—with tools arranged around it. | Creating or revising work that needs to remain visible while it is being changed. |
| Contextual | Assistance near the relevant part of a larger application or work surface. | Getting help with a specific passage, object, or step without leaving the broader task. |
| Modular | Separate areas for distinct functions in a workflow. | Tasks with stages or components that benefit from being handled in different places. |
| Simulated environment | A virtual scenario in which a person interacts with the system or its content. | Work that is naturally carried out in a simulated setting. |
In practice, a product can combine patterns. A writing tool might offer a conversational assistant while keeping the draft on a canvas; a contextual action could apply to a selected sentence. The important design question is whether the arrangement makes the work and the available controls easier to understand.
How to choose an interface for a task
There is no standardized score that selects the right layout. A useful choice follows from the task’s structure and the amount of control or review it needs. Consider these questions:
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- Is the work open-ended or step-based? Conversation can suit exploration. Clear steps may benefit from visible controls or modular areas that show the process.
- Is the person asking questions or editing a lasting artifact? A response history can serve question-and-answer work; a canvas keeps a document, image, or other result central while it changes.
- Do people need to see and adjust parameters? Menus and sliders expose choices directly. A prompt can express flexible instructions, but it may not make every setting easy to inspect.
- What inputs and outputs does the task require? Match the interface to the needed text, visual, audio, or mixed material rather than assuming one modality fits all.
- What happens if the output is wrong? Higher-consequence work calls for review and opportunities to intervene. Generated content should not be treated as correct merely because it is fluent or polished.
These are practical design considerations synthesized from the interface patterns and HCI goals described in the 2024 survey and Google DeepMind’s 2025 feature, not a validated scoring rubric.
What current studies do—and do not—show
Studies of particular interfaces offer evidence about those tested systems and tasks; they do not establish that GenAI is ready to design every interface or replace human evaluation.
AI-generated app designs
A May 23, 2025 Chartered Institute of Ergonomics & Human Factors account by Zhenyuan Sun and Chris Baber describes a study that generated burger-ordering app designs using Midjourney, DALL-E 3 on ChatGPT4o, and Stable Diffusion 3 on Stable Assistant. All three tools had problems with legible text and following prompts. With adjusted prompting, DALL-E 3 and Stable Diffusion 3 produced designs the researchers described as viable.
The study compared AI-generated designs with commercial products and work from eight competent human UI designers, using evaluations from 32 participants and the UEQ-S. It found no difference in pragmatic quality and higher hedonic ratings for the AI designs than for the commercial products or human designs in this study. Those sample counts are specific to this evaluation, not estimates about designers or users generally. The account also says the AI tools’ evaluations showed little correlation with human ratings, a reason not to rely on a system’s own judgment as a substitute for people assessing design quality.
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Conversational and voice interaction
An IBM Research publication dated March 18, 2024 reports a user study of conversational control for a semantic automation interface, describing increased engagement and satisfaction, as well as increased trust after participants used the conversational interface. Its summary does not state the participant count or effect sizes, so those outcomes cannot be quantified from that account.
A January 2025 exploratory study in the International Journal of Human-Computer Studies involved 20 participants using a ChatGPT-powered voice assistant for medical self-diagnosis, creative planning, and discussion scenarios. Its indexed summary reports improved intent recognition and proactive handling of assistant breakdowns by the LLM. The study explores breakdowns and design challenges; it is not proof that voice assistants are generally safer or more reliable.
AI in the design workflow
Google Research’s 2024 publication on PromptInfuser describes a Figma widget that connects interface elements with LLM prompt inputs and outputs. It is an example of AI being integrated into a designer’s workflow rather than used only through a separate chat window. Its existence illustrates one design approach; it does not establish that this pattern suits every design team or task.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this means for users and designers
GenAI is best understood as an expanding interface layer, not a single new interface that makes existing controls obsolete. Conversation can make flexible requests feel natural, while canvases, contextual actions, selection, and adjustable controls can keep people anchored to the work they need to change. Well-designed products may combine these approaches so users can ask, inspect, and act without surrendering oversight.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteFor users, the practical test is whether the interface helps accomplish the task and makes it possible to check and correct the result. For designers, the challenge is to put assistance where it supports the work, expose useful controls, and evaluate results with people—not assume that a capable model automatically produces a usable interface.
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