Banned in the thesis, required on the job: campuses fear cheating, enterprises fear the gap

Use cases: AIGC talent development, curriculum R&D, hands-on teaching, education agents.

A new education paradox has emerged: while colleges worry about students using AI to write essays, enterprises want new hires to arrive already fluent with AI tools. Classrooms are still debating how to prevent AI misuse, while job roles demand that employees use AI to raise efficiency, produce content and coordinate work.

This means AI education cannot be solved by bans or experience-style courses alone. Effective talent development has to put AI literacy, professional tasks, hands-on workflows and outcome assessment into one system. Students need to know when to use AI, how to judge output quality, and how to turn AI results into deliverable work. Teachers and training providers need a curriculum system that can be reused, assessed and continuously updated.

From tool trials to capability training

A lot of AI training stalls at tool demos: text-to-image today, video generation tomorrow, knowledge-base Q&A the day after. It looks lively in the short term but rarely builds stable capability over time. For colleges and training providers, the priority is to put the tools inside real tasks, letting students complete full training around planning, copywriting, visuals, video, digital humans, brand communication and data organization.

SKY CULTURE TECHNOLOGY LIMITED offers education-scene services including AIGC talent development, AIGC curriculum R&D, hands-on teaching programs, education agents and knowledge base development. The company's focus is not a single-point tool, but the full path from curriculum design to hands-on tasks, and from teaching support to outcome assessment.

What kind of AI curriculum do colleges and training providers need

A deployable AI curriculum should have three characteristics. First, the content must connect to a specialty — scenarios such as digital media, film and animation, advertising, cultural tourism promotion and enterprise content production. Second, the course must include real tasks, so students acquire AI capability while completing work. Third, the course must produce reviewable outcomes that teachers, enterprises and project owners can assess together.

In this process, education agents and knowledge bases also become important. They can accumulate course material, project cases, common questions and teaching aids, so teachers do more than deliver one-off lectures — they gradually build AI teaching assets that can be updated over time.

Why enterprises also need AI literacy training

The enterprise side has the same problem in plain sight: many departments have started using AI, but lack a unified method, management rules and business-process design. The result is everyone using their own tools, inconsistent content quality, no accumulation of data and knowledge, and AI projects stuck at the level of individual experience.

Therefore AI training should target not only students but also enterprise teams. By combining curriculum, tools, process and ongoing coaching, enterprises can gradually build their own AI content capability, knowledge management capability and digital collaboration capability.

Who should collaborate

• Colleges looking to build AIGC general-education, specialized or hands-on courses.
• Training providers wanting to develop an AI curriculum system and teaching products.
• Enterprises aiming to raise employee AI literacy and content production efficiency.
• Education programs wanting to introduce agents, knowledge bases and long-term coaching.

What AI education truly needs to solve is not getting students to "play with tools", but helping learners build transferable AI capability through real tasks. Along this direction, SKY will keep connecting AIGC education, hands-on teaching and industrial application.