Shangtouwa:AI Interactive Storytelling Community

Powerful features did not yet form a clear user journey.

The product already offered interactive stories, character conversations, world-building, and AI-assisted creation. However, users still had to understand how these experiences connected.

It was difficult to judge whether content matched personal interests, replaying a story branch could overwrite progress, and the differences between creation modes were not immediately clear.

Model the product as a connected system of content assets.

• Organized the experience around four core assets: stories, worlds, characters, and user-created works.
• Mapped the journey from content discovery and story interaction to creation and long-term asset retention.
• Used product walkthroughs and content review to identify issues in recommendation, branch management, creation guidance, and AI-generated content.
• Proposed saved story branches, a visual storyline timeline, granular preference controls, standardized content cards, and clearer creation-mode guidance.
• Prioritized improvements based on user value, affected audience, and implementation cost.

From scattered observations to an actionable product roadmap.

I delivered an end-to-end analysis covering product positioning, target users, feature architecture, AI capabilities, the content ecosystem, and monetization mechanics.

The work translated fragmented usability findings into a structured set of proposals that could be evaluated, prioritized, and tested by the product team.

A reusable framework for understanding generative AI products.

The analysis reframed Shangtouwa from a collection of AI features into a connected interactive-story experience built around discovery, participation, creation, and retained user assets.

It also strengthened my approach to AI product work: connect model capabilities with whether users can understand, control, and retain the generated results.