Product design question
Design a career counseling app for students in India.
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What this question tests
Product design for a large, diverse user base with real constraints around access, language, and trust.
How to approach it
- Define the target: students in grades 9 to 12 deciding on streams or colleges, across urban and rural India.
- List pain points: limited access to counselors, overwhelming stream and college options, and language barriers outside major cities.
- Segment by access: smartphone-owning urban students versus shared-device rural students, since the design differs sharply.
- Prioritize a low bandwidth, vernacular language chat assistant over a heavy video-based tool for broad reach.
- Design core features: aptitude quizzes, career path explainers, and connection to verified counselors for deeper questions.
- Define success as completion rate of the guidance flow and follow-through on suggested next steps like exam registration.
What a strong answer includes
- Segments explicitly by access level, noting rural students may share one family phone, which shapes a lighter, offline-friendly design.
- Proposes vernacular language support as a first class feature, not an afterthought, given India's language diversity.
- Names a trust mechanism, like verified counselor badges, since career advice from an unknown source faces skepticism.
- Picks a concrete outcome metric, like percentage of students who complete a suggested action, not just app opens.
Common mistakes
- Designing only for urban, English speaking, smartphone-native students.
- Overloading the app with content instead of a simple guided path.
Likely follow-up questions
- How would you reach students in rural areas with limited internet access?
- How would you monetize this without excluding low income students?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop