EBS announced a co-production contest for a television animation series that uses generative AI, scheduled for 2026.
The call invites original works for preschool, children, or family TV series and requires creations that have not been commercially released before.
The total fund is 500 million Korean won (roughly $350,000–$400,000), with up to five projects to be selected and aimed for broadcast in December 2026.
Post-production will be handled by EBS, and eligible applicants include individuals, small teams, and independent studios.
"Opening Animation with AI" — Opportunity or Threat?
Project Overview
The core goal is efficiency. However, the contest runs from February 11 to March 31, 2026.
It seeks TV animation concepts that use generative AI as a production tool.
Eligible applicants are individuals, small teams, or independent production companies; broadcasters and large corporations are excluded.
Up to five projects will be chosen. Each winner will receive 80 million won (a 10 million won prize and 70 million won production grant).
Importantly, EBS will provide post-production services—dubbing, music, sound, and mixing—to raise final quality.
Required materials are an application form, a project summary, at least three minutes of footage (a portion of the main episode), and documentation of the AI tools used.
Works already shared on YouTube or social media, or previously entered in other contests, are not eligible.
Schedule and Compensation
The projects target end-of-year broadcast. Meanwhile, submissions are accepted by email at animation@ebs.co.kr.
Winners are expected to be announced in April 2026.
The 500 million won pool covers both prize money and production grants, which can make real production possible even for modest creators.
Also, EBS's post-production support signals institutional expertise in audio and dubbing.
For: Opportunity and Innovation
The entry barrier to creation lowers. Therefore, generative AI can speed up visualization and rough-cut production in the planning stage and improve budget efficiency.
As a result, more individual creators and small teams can attempt TV animation projects.
First, it expands creativity.
AI can automate repetitive, labor-intensive tasks so creators can focus on story and character—areas where human judgment matters most.
Consequently, we may see more diverse and experimental ideas reach production.
Second, it eases access.
Traditional animation required large teams, long schedules, and significant capital.
However, using generative AI tools appropriately can produce early demo reels and concept art faster, helping creators secure investment or persuade a broadcaster.
This practical effect opens doors for emerging talent.
Third, it can reshape the industry.
Small studios with bold formats may gain attention, and a fair contest structure can energize a previously centralized production ecosystem.
Meanwhile, EBS’s handling of post-production helps preserve technical quality for viewers.
A new pathway opens for creators to appear on television.
This channel could also broaden educational and family programming in the years ahead.
Proponents' Examples
International experiments using generative tools are already underway.
Indie studios at home and abroad have used AI-assisted tools to rapidly produce concept art and motion sequences, showing demos to investors and scaling projects based on that momentum.
These examples demonstrate time and cost efficiencies.
In educational content production, AI can quickly generate visuals and stories tailored to individual learners, which aligns with the mission of public broadcasters (public broadcasters are organizations that serve public-interest media).
Thus, proponents argue the program could serve both public goals and industry growth at once.
Against: Concerns and Risks
Jobs may be at risk. On the other hand, if generative AI replaces some tasks, traditional animation workers could face instability.
Those who do background painting, in-between animation, or key art—tasks that are repetitive—may feel the impact first.
First, labor structure could worsen.
When technology leads to cost cuts, studios might reduce payroll, which could shrink opportunities for skilled artists and mid-tier production staff.
Second, creative quality might decline.
AI-generated images and motion sometimes miss subtle emotional detail.
Especially in content for children, emotional nuance and educational depth often require careful human craft and supervision.
Third, copyright and ethics are a concern.
If a generative model’s training data are unclear, questions arise about authorship and possible plagiarism.
Also, biases baked into the model could be reflected in content.
Work produced by technology is not automatically ethically or legally safe.
Therefore, verifiable procedures and transparent data sources are essential.
Counterexamples
In several industries, automation reduced work volume.
Within animation, some studios have reported shrinking outsourcing for specific parts after adopting automation tools, and those transitions revealed problems placing skilled workers elsewhere.
These cases show technological progress can have mixed effects.
There have also been copyright disputes where AI-trained systems produced works similar to existing material, sparking controversy.
Such precedents highlight challenges in judging the originality of entries in a generative-AI contest.
Verification and Institutional Safeguards
Transparency is key.
The contest organizers should require detailed records of AI usage and the sources of training data, and they must establish a process to verify submissions.
First, evidence requirements should be practical.
Collecting model logs, prompt histories, and documentation of licenses for any training data should be mandatory.
These materials become critical tools for fair review.
Second, education and retraining matter.
Government and broadcasters should consider retraining programs and redeployment policies for traditional production workers.
When job losses are possible, complementary measures to protect and reskill workers reduce negative side effects.
Third, industry cooperation helps.
Forming a council that includes major platforms, legacy studios, and the public broadcaster can create shared standards and guidelines.
Joint work on verification tools and ethical rules will support long-term stability for the sector.
Verifiable submissions, retraining programs, and an industry council would make the transition fairer.
Policy Recommendations
A balanced approach is necessary.
Keep the contest’s creative aim, but add institutional safeguards to protect creators and the industry.
Start by clarifying disclosure and judging standards.
Standardize the scope of acceptable AI use and the supporting documents required, and include external technical reviewers to ensure fairness.
Also design follow-up support.
Provide mentoring for promising proposals that were not selected, connect them with funding opportunities, and create partnerships with regional studios to strengthen the whole industry.
Industry Outlook
Possibility and risk coexist.
Generative AI can cut cost and time while enabling a wider range of concepts to be tried.
However, it can also unsettle labor structures and copyright systems.
In the medium to long term, norms and technology must develop together.
When technical verification tools, legal frameworks, and cooperative business models are in place, AI can serve as a supportive tool rather than a disruptive replacement.
Checklist for Citizens and Creators
Preparation is required.
Applicants should prepare AI usage logs, prompts, and copyright agreements carefully, and document the origin and creative process of their work.
(A prompt is the text or input that tells an AI what to make.)
Broadcasters and policymakers should design transparent judging criteria and retraining support.
The industry should immediately set up practical programs to help existing staff transition to new roles.
Conclusion
In short, balance is the answer.
EBS’s contest gives creators a chance but comes with real risks.
Therefore, policy measures that combine technology adoption with worker protection are necessary.
We need a design that keeps both creativity and safety.
Fair verification, retraining support, and industry dialogue are essential.
We ask readers: how will you receive animation made with generative AI?
