DEV Community: Zayn Levesque

Figma Made a Huge Step Forward in AI Design - (April 2026)

Zayn Levesque — Tue, 07 Apr 2026 01:53:33 +0000

What if the tools that write your code could also design your interface?

For years, product development has followed a fairly structured pipeline: designers build the interface in design software, developers translate those designs into code, and teams spend countless hours ensuring both sides stay synchronized.

But what if that separation disappeared entirely?

Figma recently introduced a new capability that allows AI agents to design directly on the Figma canvas. Instead of simply generating UI suggestions or writing frontend code, these agents can now interact with real design files, components, and design systems.

The goal is simple: allow AI to participate directly in the design workflow rather than sitting outside of it.

Before diving deeper, here are some important terms to understand.

Key Terms

Figma Canvas: The workspace where designers create and edit UI layouts, components, and prototypes inside Figma.

AI Agent: An AI system capable of performing tasks autonomously, such as generating code, modifying files, or executing workflows.

Design System: A structured set of reusable UI components, styles, variables, and design guidelines used to maintain consistency across a product.

MCP Server (Model Context Protocol): A system that allows AI agents to interact with external tools and environments while maintaining context about how those tools work.

Design Tokens: Reusable design variables (like colours, spacing, and typography) shared between design and development environments.

Skills: Instruction files written in Markdown that teach AI agents how to perform specific workflows within Figma.

How AI Agents Work Inside Figma

Traditionally, AI tools interacting with design systems lacked access to the detailed context that designers rely on daily. This often resulted in designs that felt generic or inconsistent with an existing product.

Figma’s new MCP server changes this by allowing AI agents to operate directly on the canvas.

Instead of generating isolated design ideas, agents can now:

Create UI components using existing design systems
Modify layouts directly in Figma files
Access variables like color palettes and spacing rules
Generate or refine design assets alongside developers

In practice, this means AI can now participate in the same workspace as human designers.

Two core tools enable this interaction.

1. Generate Figma Design

This tool converts HTML from a live application or website into editable layers inside Figma.

If developers change a UI component in code, the design can quickly be recreated inside Figma so designers can iterate on it again.

2. Use Figma

This tool allows AI agents to create or modify design elements directly on the canvas using the existing design system.

Instead of producing mockups outside the workflow, the AI works inside the same structure that designers already use.

Together, these tools allow teams to move fluidly between code and design.

The Role of “Skills”

One of the most important ideas introduced with this system is the concept of skills.

Skills are instruction sets written in Markdown that define how AI agents should behave while working in Figma.

They provide guidance such as:

Which steps to follow when generating designs
How to apply design system rules
What spacing conventions to use
Which components should be reused

In other words, skills give agents access to the intent behind a design system, not just the assets themselves.

This makes AI behaviour far more predictable.

Because AI models are inherently non-deterministic (the same prompt can produce different results), encoding workflow instructions into skills ensures that generated designs remain consistent with the team’s standards.

Several example skills were released alongside the feature, including:

Generating components from a codebase
Creating new UI screens from existing components
Applying hierarchical spacing automatically
Synchronizing design tokens between code and Figma
Running multi-agent workflows to build designs in parallel

Anyone can create a skill without needing to build a plugin or write traditional software code.

Self-Healing Design Iteration

Another interesting capability introduced with this system is self-healing iteration.

When an AI agent generates a design screen, it can:

Take a screenshot of the result
Compare it with the expected output
Automatically adjust the design if something does not match

Because the AI is working with real components, variables, and layout systems instead of static images, these adjustments interact with the underlying design structure.

This means the AI is not just editing visuals; it's modifying the actual system that defines how the interface works.

What the Authors and Developers Claim

According to the teams developing this system, integrating AI directly into the design canvas has several benefits:

First, it removes the gap between design and development.

Instead of translating between tools, teams can iterate within a shared environment where code and design remain synchronized.

Second, it allows AI to work with real product context.

Design agents can now access:

existing components
layout rules
typography systems
accessibility specifications

Finally, encoding workflows into skills makes AI outputs more consistent and predictable, which is critical when building real production software.

What This Means for Mobile Designers

For mobile designers, the introduction of AI agents directly into the design canvas could significantly change how interfaces are created, iterated on, and maintained.

Some of the most useful changes include:

AI-Assisted UI Generation
Designers can generate full UI screens using existing components and design tokens, allowing early prototypes or layout explorations to be created much faster.

Design System Enforcement
Because agents work directly with components, variables, and tokens, designs automatically follow the standards defined in the design system. This helps maintain consistency across large mobile applications where design-drift is common.

Automated Design Refinement
AI agents can iterate on their own output by analyzing screenshots of generated designs and adjusting spacing, layout, or components until the design better matches expected results.

Accessibility Integration
Some AI skills can generate screen reader specifications or accessibility metadata directly from UI designs, making it easier to incorporate accessibility considerations early in the design process.

Collaborative Design Workflows
AI agents can potentially work alongside designers as assistants, generating components, applying spacing rules, or organizing layouts while the designer focuses on higher-level creative decisions.

Overall Significance

The most important idea behind this feature is not simply that AI can generate designs.

It’s that AI can now work inside the design environment itself.

Instead of generating UI concepts externally, AI agents are becoming participants in the design process. Operating within the same structure, systems, and constraints that human designers use.

This shifts AI from being a design suggestion tool to a collaborative design partner.

My Thoughts:

After using Figma for a couple of years now, I can confidently say that this is a change that designers have been salivating over since the introduction of Figma Make.

Any and all code created by Figma Make would not only be unusable in most cases, but would also be so incredibly bloated that it would not be worth trying to use for anything development-wise.

Most of the work comes from not including every detail possible in the first prompt, and there is almost no way that it will reach exactly what you want it to be without purchasing a subscription to get more tokens in Figma Make.

I can now say goodbye to almost all of my front end development time.

Thank you Figma! 🫡

Agents, Meet the Figma Canvas | Figma Blog

Starting today, you can use AI agents to design directly on the Figma canvas. And with skills, you can guide agents with context about your team’s decisions and intent.

figma.com

Co-Creative Storytelling: The Future of Educational AI - (March 2026)

Zayn Levesque — Mon, 02 Mar 2026 05:12:40 +0000

How would you teach children about basic concepts such as literacy in 2026?

The most popular idea has been simple: build an AI tutor/instructor. A one-to-one automated teacher that mimics the classroom. But let’s be real for a moment, even in real classrooms, lectures are usually not the most engaging.

Researchers at Emory University wondered if there was another path forward. Instead of building an AI that teaches children, what if they built one that creates with them?

The result was Tinker Tales, a co-creative storytelling system designed to explore whether structured collaboration with AI could improve engagement, narrative development, and emotional reasoning in children.

Some key terms to become familiar with before diving into the research:

NFC: a technology that allows devices to exchange information when they are close together (think tapping a credit card)
LLM: a type of AI trained on massive amounts of text so it is able to understand and generate human-like language
Scaffolding: a teaching method that gradually guides students with structured questions to help learn or complete a task
Chain Question: a question that encourages cause-and-effect thinking (“Why did that happen?”)
Primitive Question: a simple question that encourages adding a new event (“What happened next?”)
Social-Emotional Learning (SEL): an educational approach that helps children understand emotions, empathy, and improve social skills
Applebee’s Narrative Development Model: the progression of narrative skills in children from ages 2 to 17, based on research by Arthur N. Applebee

How Tinker Tales Actually Works

Tinker Tales isn’t just a chatbot. It’s a mobile application built around three core components:

1. Physical NFC Story Tokens

Children scan physical tokens representing characters, places, emotions, or objects. Scanning one instantly adds that element into the story world.

2. Voice-Based Interaction

Children speak naturally. Speech-to-text converts their voice into text, and the AI responds using text-to-speech.

3. Scaffolded Conversational AI

This is where things get interesting.

Instead of asking open-ended questions like, “Would you like to add something?”, the AI uses structured prompts grounded in:

Social-Emotional Learning (SEL)
Applebee’s narrative development model

It guides children through story stages (beginning, journey, climax, ending) while encouraging both event-building and emotional reasoning.

Important to note, the AI does not control the story content at all. The child does. The AI simply structures the experience.

What the Researchers Found

The study involved children ages 6–8 participating in multiple storytelling sessions. During those sessions, the AI alternated between scaffolded prompts and generic open-ended prompts.

The difference was dramatic.

Narrative Engagement

Primitive questions → 90% of children added new story events
Chain questions → 100% added cause-and-effect reasoning
Generic open-ended questions → Only 37% contributed ideas

Simply changing how the question was framed nearly tripled engagement.

Emotional Depth

When children were prompted with social-emotional scaffolds:

62% added emotional reasoning
Only 12% added emotional content without scaffolding

In other words: if you ask children to think about feelings in a structured way, they do.

If you don’t, they often won’t.

Perception of the AI

All children reported high enjoyment.

Many described the AI as:

A friend
A helper
A teacher

They emphasized the feeling of “building together,” suggesting they perceived the system as collaborative rather than instructional.

What the Authors Concluded

The researchers made several key claims:

Scaffolding reduces cognitive burden
Open-ended prompts are difficult for young children
AI responsiveness must persist across an entire session, not just turn-by-turn
Effective AI systems require both structure and flexibility

This wasn’t about smarter AI, it was about smarter interaction design.

What This Means Beyond the Study

Now let’s zoom out.

Because this research isn’t just about storytelling apps, it has broader implications for educational AI and mobile development as a whole.

The takeaway is clear:

Structured AI prompts significantly outperform generic chatbot prompts.

This has implications for:

AI writing tools for children
SEL development apps
Literacy-building platforms
Hybrid toy–app ecosystems

Overall Significance

This study shows something subtle but powerful:

AI effectiveness depends less on raw generative capability and more on interaction structure.

It reframes mobile AI development from:

“Add AI to the app.”

“Design collaborative systems where AI and users build together.”

Structured AI systems outperform open-ended chatbots. Educational grounding increases engagement.

My thoughts:

As a long-time advocate of getting major restructuring to education, this excites me to no end and gives me hope for future generations.

Up until this point, I'd only ever interacted with AI storytelling sites such as AI Dungeon or Talefy. And, while rough, showed much promise for interesting use cases such as running DnD campaigns without the need for a dedicated DM (Dungeon Master).

Not only could this be incorporated in schools to improve learning but the children who can't keep their eyes glued to paper for more than 30 seconds will have an alternate avenue to learn properly.

I believe this is a great step in the right direction and only time will tell if this can be applied to other subjects such as Math and Science.

References:

Nayoung Choi (Emory U), Peace Cyebukayire (Emory U), Ikseon Choi (Emory U), Jinho D. Choi (Emory U), Jiseung Hong (Carnegie Mellon U) (2026, Feb 4). Tinker Tales: Supporting Child–AI Collaboration through Co-Creative Storytelling with Educational Scaffolding

[2602.04109] Tinker Tales: Supporting Child-AI Collaboration through Co-Creative Storytelling with Educational Scaffolding

Artificial intelligence (AI) is increasingly framed as a collaborative partner in creative activities, yet children's interactions with AI have largely been studied in AI-led instructional settings rather than co-creative collaboration. This leaves open questions about how children can meaningfully engage with AI through iterative co-creation. We present Tinker Tales, a tangible storytelling system designed with narrative and social-emotional scaffolding to support child-AI collaboration. The system combines a physical storytelling board, NFC-embedded toys representing story elements (e.g., characters, places, items, and emotions), and a mobile app that mediates child-AI interaction. Children shape and refine stories by placing and moving story elements and interacting with the AI through tangible and voice-based interaction. We conducted an exploratory user study with 10 children to examine how they interacted with Tinker Tales. Our findings show that children treated the AI as an attentive, responsive collaborator, while scaffolding supported coherent narrative refinement without diminishing children's agency.

arxiv.org

Designing Games With AI: Creative Partner or Creative Risk? - (Feb 2026)

Zayn Levesque — Fri, 27 Feb 2026 06:20:16 +0000

AI tools have never been easier for the average person to use. Everywhere we go, AI has been woven into the tools and platforms we interact with daily. Even a simple Google search produces an AI-generated answer before traditional website links appear. Artificial intelligence is no longer experimental, it is embedded in everyday life.

This raises an important question: how are the creative members of society making use of it?

In this post, I will summarize a research paper by Sultan A. Alharthi that explores how professional game designers are leveraging generative AI, and how they perceive its role in the gaming industry.

Before diving into the research, it is important to understand the technology itself.

What is Generative AI?

Generative AI is a form of artificial intelligence that creates new content, text, images, audio, code, or even 3D models, based on patterns it has learned from massive datasets. Unlike traditional AI systems that focus on classification or prediction (for example, identifying whether an image contains a cat), generative AI produces entirely new outputs in response to a text prompt.

In the context of game design, generative AI can:
Create first-draft dialogue for non-playable characters (NPCs)

Generate concept art, textures, and visual assets
Assist in programming and debugging
Produce sound effects or music
Help designers prototype mechanics quickly

Prototyping is especially important in game development. A prototype is a simplified version of a game used to test mechanics and player experience before full production begins. Generative AI dramatically reduces the time required to move from idea to playable concept.

This leads to the core question explored in Alharthi’s paper: does generative AI enhance creativity, or does it risk constraining innovation?

The paper investigates how professional game designers perceive and use generative AI tools in their creative workflows.

Alharthi surveyed and interviewed professional game designers to understand:

What tasks AI is used for
Designers’ perceptions of the benefits and drawbacks
Future intentions on the use of AI

Key Findings

The findings show widespread adoption across the industry:

90.5% of designers used generative AI for ideation and brainstorming.

83% used it for asset creation.

74% used it for programming support.

Over half used it for dialogue generation.

Just under half used it for music and sound design.

It is very likely that most designers had used AI in at least two different workflows.

More than 80% of respondents reported that they plan to continue using AI in future projects.

Reported Benefits

Designers identified several major advantages:

Faster idea generation
Automation of repetitive tasks
Reduced workload
Lower production barriers for indie developers
More room for experimentation

Large studios such as Ubisoft have even developed internal generative AI tools to assist narrative designers in producing first-draft NPC dialogue.

Reported Risks

However, designers also expressed concerns:

Ethical and legal uncertainty due to copyrighted training data
Risk of generic or homogenized game design
Long-term narrative inconsistency
Creative dependency on AI tools
Reduction in entry-level job opportunities

The paper concludes that generative AI is neither purely beneficial nor purely harmful. Instead, its impact depends on how responsibly and intentionally it is integrated into workflows.

How does it impact the mobile development industry?

Although the research focuses on game designers broadly, its implications strongly apply to mobile games and app-based interactive experiences.

Mobile development operates under tight constraints; limited budgets, small teams, fast release cycles, and intense competition in app marketplaces. Generative AI has the potential to reshape this environment significantly.

Mobile developers can leverage generative AI for:

Rapid prototyping of casual games
Procedural level generation for replayability
Personalized dialogue systems
Dynamic in-app storytelling
Automated asset generation for indie mobile studios

For solo developers or small teams, AI-generated assets reduce reliance on large art departments. This lowers financial barriers and enables faster production timelines.

However, the same accessibility could flood app stores with low-effort, AI-generated games. If anyone can create a functional game with minimal effort, discoverability becomes even more difficult, and overall quality standards may decline.

Personal Opinion:

In my opinion, generative AI is currently a slight net positive for game and mobile development.
The removal of repetitive tasks is undeniably beneficial. Few developers enjoy creating placeholder assets or writing boilerplate code. Offloading that work to AI allows designers to focus on higher-level creative decisions.

What excites me most is the empowerment of indie developers. Passionate creators can now build and publish mobile games without massive funding.

However, I am deeply concerned about two issues:

1. Entry-Level Job Reduction

If beginner-level tasks are automated, fewer junior positions will exist. This creates a long-term industry issue. Without hands-on experience, future developers will struggle to build the expertise needed for senior positions. There are enough industries being gutted of their entry-level positions and this is not helping.

2. Market Saturation and Low-Effort Apps

If it only takes a single prompt to generate a functional mobile game, digital stores, such as Steam, could become oversaturated with low-quality content. Discoverability for genuinely innovative projects may become even harder.

Additionally, creative dependency is a real risk. If designers rely too heavily on AI for ideation, originality may gradually decline. Innovation often comes from constraints and deep engagement, not from instant generation.

Overall, generative AI should be treated as a creative collaborator, not a creative replacement. When used responsibly, especially in early prototyping, it enhances productivity and experimentation. When used carelessly, it risks weakening craftsmanship and professional growth.

Its long-term impact on the mobile development industry will depend entirely on how thoughtfully developers integrate it into their workflows.

References:

Sultan A. Alharthi (2025, May 24). Generative AI in Game Design: Enhancing Creativity or Constraining Innovation?
https://www.mdpi.com/2079-3200/13/6/60