Welcome to the comprehensive guide for Citizenlab's new AI Analysis feature, which is currently in its beta phase. This innovative tool aims to revolutionize the way you understand, categorize, and analyze the plethora of input from residents participating in your community engagement initiatives. Compatible with both ideation methods and surveys, the AI Analysis tool offers a user-friendly interface and a range of functionalities to help you derive actionable insights from community feedback.
Disclaimer: This feature is still in beta testing. Decisions regarding its future pricing and which pricing tiers will have access to it are yet to be finalized.
To access the AI Analysis feature, navigate to the input manager of your project that employs an ideation method. Here, you'll find the option to launch the AI Analysis tool.
For survey-based projects, the AI Analysis feature can be accessed directly from the survey results page. Upon launching the tool, you'll be prompted to select the questions you wish to analyze. You can also add follow-up questions to the initial question for a more holistic analysis.
Note: The AI Analysis tool currently supports only textual input for surveys. Numerical data is not yet compatible.
Process, Interface and Columns
The AI Analysis interface is divided into four main columns, each serving a specific purpose:
1. This is your control panel for creating and managing tags. Tags are essential for clustering inputs and facilitating more nuanced analysis.
2. This column displays a list of all the input you wish to analyze. It serves as your data pool.
3. Here, you can view the input selected and manually add or remove tags as needed.
4. This is where you can generate summaries and ask questions to the AI for further clarification or insights.
Tagging is a crucial part of the analysis process, and our tool offers multiple ways to do it:
1. Fully Automated: Let the system do the work. It will scan through the data and apply common tags automatically.
2. By label: If you have specific criteria in mind, you can create your own tags.
3. By example: You can provide a few manual examples to teach the system how to tag future inputs.
4. Sentiment: For a more nuanced understanding, you can also tag inputs based on sentiment, which will divide them in positive/negative sentiment
5. Language : Language detection
If you prefer complete control, you can opt to tag all inputs manually on each input.
Filtering and Previewing Input
The second & third column help you with exploring the input:
- Use various filters to focus on input from specific time periods, engagement levels, or demographic fields.
- Preview the overall distribution of answers across different demographic groups, giving you a snapshot of community engagement.
Summaries and Questions
The fourth column is your go-to place for summaries and further inquiries:
- Use the 'Summarize' button to generate concise summaries of the selected input.
- Use the 'Ask a Question' button to probe deeper into specific areas.
- The AI's accuracy level is displayed, giving you an idea of how reliable the output will be.
- Remember, the more data you feed into the system, the vaguer the summaries may become. Use tags and filters to maintain the quality of your summaries.
When a summary is generated, it will include clickable references to related ideas, allowing you to delve deeper into the context.
Exporting Summaries & Feedback
As of now, the only way to export a summary is by using the 'copy' function. We are exploring options to add more export functionalities in the future.
Your feedback is crucial for the continuous improvement of this feature. If you find the summary satisfactory or think there's room for improvement, please use the report flag to let us know.