Playlab in Practice: Higher-ed Research (Part 2)
Workshop 2: From Reflections to Insights
Analyzing pre-survey data and preparing for an endline (post-survey) with AI
This session builds directly on Workshop 1: Designing a Baseline Reflection (Pre-Survey) That Actually Matters.
If you didn’t attend the first workshop, you’re still welcome to join—you can review the session description and recording here:
[Link to Workshop 1 Luma description + recording]
Once you’ve collected baseline reflections, the real question becomes: now what?
In this follow-up session, we’ll focus on how educators can move from pre-survey data to actionable insights—using AI to better understand learners, shape instruction, create responsive teaching assets, and intentionally prepare for a strong endline (post-survey) later in the term.
Rather than centering on any single tool, this workshop emphasizes how to think with reflection data. We’ll explore how educators ask meaningful questions of student input, surface themes and patterns, and translate qualitative insights into instructional decisions, mid-course adjustments, and documentation of learning.
What we’ll cover
• How to analyze baseline reflection data responsibly
• Framing strong analytical questions educators can ask of reflection data
• Turning insights into instructional and community-building assets
• Using baseline analysis to intentionally design an endline reflection
Tools & workflows (with clear boundaries)
We’ll demonstrate example analysis workflows using AI tools educators commonly have access to, as well as how Playlab data can be analyzed when appropriate permissions are in place.
As part of this session, Justin Horvath (Tufts University) will share a 15-minute practitioner demo showing how he analyzes Playlab JSON conversation exports using a custom chatbot he built. This segment will focus on how structured data enables deeper qualitative analysis—not on promoting a specific tool.
A short segment will also highlight how structured exports (e.g., JSON) can support deeper analysis and asset creation across different environments.
This session is not advocating for any particular tool. It reflects a reality many educators face: working across multiple systems and learning how to compose tools responsibly in support of learning goals.
Important note on data use
• Do not upload student data into tools that are not approved or covered by your institution or organization
• Some analysis workflows—within Playlab and across other tools—require specific account privileges
• If you do not currently have access to these environments, you are still encouraged to attend with an observational mindset to learn how educators frame questions, interpret reflection data, and design AI-supported analysis workflows responsibly
Who this session is for
This workshop is open to educators across K–12 and higher education who are using—or considering using—pre- and post-surveys or reflective activities and want to analyze learning more intentionally with AI.