Metrics question
A new ChatGPT learning feature is highly engaging with students, but educators and researchers believe it may not improve real cognition or achievement. How would you evaluate the conflicting signals, decide whether to iterate, limit, or stop the feature, and determine what to build next?
- OpenAI
- Metrics
- Hard
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What this question tests
Tests the ability to evaluate conflicting engagement and outcome signals and make a responsible ship, limit, or stop decision for an education feature.
How to approach it
- Separate the two signals clearly: student engagement data versus educator and researcher concern about cognition or achievement impact.
- Check whether the engagement data itself is a proxy for real learning, such as completion of practice problems, or just time spent.
- Review the researcher concern's evidence base, is it based on this specific feature or general concerns about AI assisted learning.
- Run or commission a targeted study on actual learning outcomes, not just engagement, if one does not already exist.
- If evidence of harm to genuine learning is credible, limit the feature's use cases, for example restrict it to practice rather than answer generation, rather than fully stopping it.
- If evidence is inconclusive, iterate with instrumentation changes that let you measure real learning outcomes going forward before scaling further.
What a strong answer includes
- Treats high engagement as necessary but not sufficient evidence, insisting on a real learning outcome measure before scaling.
- Proposes a middle path, limiting the feature's use case, rather than a binary ship or kill decision, showing nuanced judgment.
- Distinguishes credible researcher concern based on evidence from general skepticism about AI in education, before overreacting to it.
Common mistakes
- Treating high engagement alone as proof the feature is working and safe to scale.
- Killing the feature entirely on unverified concern without first checking if the evidence is feature specific and credible.
Likely follow-up questions
- What study design would you use to measure real cognitive impact rather than just engagement?
- How would you communicate a decision to limit the feature to the team that built it for engagement?
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More questions from OpenAI
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop