Abstract
This article examines how artificial intelligence (AI) is shaping the field of 2D animation, offering high school students a clear view of key technologies, workflows, and skills relevant to today’s animation programs. First, we define core animation terms including keyframe, in-betweening, rigging, and generative AI. Next, we describe how AI tools are being used in 2D workflows—such as automatic in-betweening, scene generation, and style transfer—and provide concrete research examples. Then we look at how these changes affect animators’ roles and what skills are becoming more important. The article draws on recent studies that show real impacts and outlines realistic opportunities and challenges.

Keywords: 2D animation, generative AI, in-betweening, rigging, animation workflow

In 2D animation, a keyframe is a drawing that marks a major point in a motion sequence, such as the start or end of a jump. The drawings between keyframes are called in-betweens. Traditional in-betweening requires animators to draw many frames to ensure smooth motion.

Rigging refers to creating a skeleton or structure for a character that allows parts (like limbs) to move in relation to one another. When rigging is set up, the animator manipulates the rig instead of redrawing every part.

Generative AI means machine learning models—often neural networks—that can create new content (images, frames, motion) based on input data or prompts. When applied to animation, generative AI can suggest or produce frames, backgrounds, or movement patterns.

Understanding these terms helps when we look at how AI is integrated into 2D animation workflows.

AI tools in 2D animation workflows

Research shows that AI is already used in several parts of 2D animation production. For example, an article titled “The state of AI for hand-drawn animation inbetweening” describes how AI systems generate intermediate frames between keyframes. (yosefk.com) This reduces the time animators spend drawing every in-between manually.

Another study, “Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanics”, explains how deep neural networks (DNN) can predict motion for 2D characters using biomechanical data, achieving more lifelike movement while reducing manual work. (eudl.eu)

In pre-production, a paper found that AI supports tasks like storyboarding, asset creation, and idea iteration: “Artificial Intelligence in the Pre-Production Pipeline of 2D Animation” reports that AI lets creators iterate more quickly and focus more on storytelling. (jetir.org)

A practical industry view: a blog from an animation studio states that AI is used to automate repetitive tasks, such as generating backgrounds or frames, while the human animators focus on timing, expression, and story. (motiontheagency.com)

These examples show that AI is becoming a tool in the animator’s toolkit—not fully replacing the human animator yet, but changing the process.

What this means for animators and their skills

As AI takes over more repetitive or technical work, animators will need to adapt their skills. According to the article “Exploring the Integration of Generative AI and the Role of Animators”, multi-modal generative AI (like combining text, image, and motion) changes how animators work by shifting focus toward creative direction and oversight. (jkd.komdigi.go.id)

The trade-off: while AI can speed up production and open new possibilities, animators still need strong skills in storytelling, character design, motion timing, and artistic judgment. As one article states: “AI for animation studios can automate repetitive tasks … but it still can’t replace human creativity.” (motiontheagency.com)

For students considering university animation programs, this means that courses focusing solely on drawing or software may not be enough. Programs that combine art, storytelling, motion theory, and some exposure to AI tools or data workflows will become increasingly relevant.

Opportunities and challenges

One opportunity: studios can produce animations faster and explore more styles or larger volumes of content. For instance, a company reportedly reduced character-rig and production time from 20 days to 2 days using an AI-based tool. (app2top.com)

Another opportunity: tools that enable smaller teams or individual creators to achieve higher production quality with fewer resources. This lowers the barrier for independent animation projects.

Challenges include questions of creative ownership, how to credit human artists when AI tools generate or assist major parts of the work, and ensuring the quality and originality of animation. The Animation Guild formed a task force to research ethical issues around AI and workers in animation. (animationguild.org)

Also, while many claims exist about increased efficiency thanks to AI in animation, some empirical studies (especially focused on 3D) conclude that gains vary by project stage and type. (MDPI)

What students should look for in university programs

When you select a university animation program, check if the curriculum covers:

  • 2D animation fundamentals (drawing frames, keyframes, timing, motion)
  • Software tools for rigging and frame interpolation
  • Exposure to AI or machine-learning tools in creative workflows (e.g., how AI can generate backgrounds, suggest motion)
  • Storytelling, character development, and motion theory (so you can direct or design rather than only execute)
  • Project-based work where you get to apply tools and make complete animations, not just learn software in isolation. Programs that combine art and technology will better prepare you for an animation industry increasingly using AI.