MSN, recently known as Microsoft Start, is a news and content platform that provides users with news articles, entertainment, lifestyle content, and more.

organization

Microsoft AI



(Windows, Bing, Copilot, MSN)

role

Design, Development,

Research

collaborators

Developers, Project managers,

UX researcher

timeline

2 months

MSN, recently known as Microsoft Start, is a news and content platform that provides users with news articles, entertainment, lifestyle content, and more.

organization

Microsoft AI



(Windows, Bing, Copilot, MSN)

role

Design, Development,

Research

collaborators

Developers, Project managers,

UX researcher

timeline

2 months

MSN, recently known as Microsoft Start, is a news and content platform that provides users with news articles, entertainment, lifestyle content, and more.

organization

Microsoft AI



(Windows, Bing, Copilot, MSN)

role

Design, Development,

Research

collaborators

Developers, Project managers,

UX researcher

timeline

2 months

overview

Designing for Trust and Engagement in an AI-Powered Commenting Experience

In this project, I partnered closely with product and engineering teams to refine the discussion experience within a financial platform, aiming to increase user participation and build a stronger sense of community around AI-generated content (AIGC). As part of a broader initiative to integrate AI-driven insights into real-time financial conversations, our goal was to craft a UX that felt both trustworthy and dynamic.

The challenge was to design a space where users wouldn’t just passively consume AI-generated summaries, but feel confident and motivated to share their own perspectives. This meant rethinking the way information was structured, how polls and discussions were surfaced, and how the system could better invite engagement—whether through a pop-up card, clearer content signals, more visible community activity, or frictionless comment entry points.

The challenge was to design a space where users wouldn’t just passively consume AI-generated summaries, but feel confident and motivated to share their own perspectives. This meant rethinking the way information was structured, how polls and discussions were surfaced, and how the system could better invite engagement—whether through a pop-up card, clearer content signals, more visible community activity, or frictionless comment entry points.

The challenge

When Users Read, But Don’t Respond

Despite the platform offering timely financial summaries and polls, user engagement with discussion threads was relatively low.

We identified two key opportunities: (1) make it easier and more inviting for users to read AIGC insights and join the conversation, and (2) create a stronger sense of community by showing historical discussion stats and offering smart, guided ways to start commenting.

My role

As the UX designer, I led the end-to-end re-design across four core initiatives:

  • Discussion Page and Pop-up Card Enhancements: Introducing a clearer hierarchy with AI insights, poll data, and discussion stats to drive transparency and encourage exploration.

As the UX designer, I led the end-to-end re-design across four core initiatives:


  • Discussion Page and Pop-up Card Enhancements: Introducing a clearer hierarchy with AI insights, poll data, and discussion stats to drive transparency and encourage exploration.

As the UX designer, I led the end-to-end re-design across four core initiatives:


  • Discussion Page and Pop-up Card Enhancements: Introducing a clearer hierarchy with AI insights, poll data, and discussion stats to drive transparency and encourage exploration.

  • AI-Generated Comment Suggestions:
    Integrated GPT-powered comment prompts aligned with polls to reduce user friction. Built safety mechanisms for human review to ensure trustworthy, bias-mitigated suggestions.

  • Research Study on AI Labeling: Designed and conducted a study on label placement and wording to improve user understanding and trust in AI-generated content. Insights directly informed key design decisions.

  • Information Density & Discoverability:  Balanced readability and depth by truncating and expanding content with contextual interactions.

problem framing

Understanding the engagement gap

Despite strong engagement with polls, we noticed a significant drop-off when it came to users commenting or continuing the conversation. Users were reading AI-generated market summaries but not contributing their own insights.

Through internal feedback and behavioral data, we identified a few key friction points:

Despite strong engagement with polls, we noticed a significant drop-off when it came to users commenting or continuing the conversation. Users were reading AI-generated market summaries but not contributing their own insights.


Through internal feedback and behavioral data, we identified a few key friction points:

  • Lack of trust in AI content: Users weren’t clear what role AI was playing in the summary or how accurate it was.

  • Unclear calls to action: The transition from reading to commenting lacked a clear bridge.

  • Thin sense of community: Without visible signals of ongoing conversations, the page felt static.

Opportunity: How might we redesign the discussion and pop-up card experience to feel more interactive, credible, and participatory—especially in a space where users may feel uncertain about AI-generated content?

  • Thin sense of community: Without visible signals of ongoing conversations, the page felt static.


Opportunity: How might we redesign the discussion and pop-up card experience to feel more interactive, credible, and participatory—especially in a space where users may feel uncertain about AI-generated content?

Solution: canvas refinements

Through rapid iteration and user research, I transformed initial product hypotheses into UX solutions that increased engagement, strengthened community interactions, and promoted transparency around AI-driven content.