Congratulations, you are now a certified Stream AI Moderator! Along the way, you’ve built a complete understanding of how to keep communities safe, fair, and thriving. From learning how Stream’s AI moderation works to navigating the dashboard, applying actions, spotting trends, and protecting your mental health, you now have the tools to moderate with confidence.
What You’ve Learned
Over the past lessons, you’ve:
- Understood the system: How AI detects and flags harmful content, and how severity levels and confidence scores guide your review.
- Mastered the tools: Navigating queues, using Channel Explorer, applying actions, filtering, bulk actions, notes, and keyboard shortcuts.
- Put it into practice: Walking through a day-in-the-life workflow, handling notifications, and closing the loop.
- Improved the system: Reporting trends, identifying edge cases, and feeding insights back into rules and policies.
- Built fairness and resilience: Staying consistent with policies, reducing personal bias, and protecting your own well-being.
Together, these skills ensure moderation isn’t just reactive, it’s strategic, consistent, and sustainable.
Moderators are more than gatekeepers; you’re the guardians of community trust. Every decision you make shapes the experience for users and contributes to the long-term health of the platform. When moderation is fair, transparent, and human, communities feel safer, more respectful, and more welcoming.
Moving Forward
As you step into moderation with Stream:
- Stay consistent: Always ground decisions in policies and precedents.
- Stay collaborative: Use notes, tags, and escalations to work as a team.
- Stay reflective: Share patterns, edge cases, and feedback to keep improving the system.
- Stay resilient: Protect your own well-being so you can continue doing this important work effectively.
Moderation is never finished; communities evolve, behaviors shift, and new challenges emerge. But with the foundation you’ve built here, you’re ready to adapt, respond, and help shape healthier spaces at scale.