How would you design an AI mock-interview coach on InterviewBit for final-year engineering students?

Introduction

InterviewBit, the coding-practice platform that Scaler's founders built before launching Scaler Academy, is where millions of Indian engineering students practise data structures, algorithms and interview problems. Practice problems help students learn to code, but many still freeze in real interviews: they cannot explain their thinking aloud, manage time, or handle follow-up questions. The prompt asks us to design an AI mock-interview coach on InterviewBit for final-year engineering students. I'll clarify the scope, explain why it is worth building, choose a segment, map pain points, propose and prioritise solutions, and define success metrics.

Step 1

Clarify Questions (3 minutes)

• Is the coach free, paid, or a funnel into Scaler's paid programs?

• Why it matters: Business model shapes how generous the free tier can be, given model inference costs.
• Hypothetical answer: A limited free tier on InterviewBit, with deeper usage as part of Scaler programs.
• Impact: The free experience must be valuable on its own while showing what paid learning adds.

• Which interview types are in scope?

• Why it matters: DSA coding, system design, and HR or behavioural rounds need different designs.
• Hypothetical answer: Start with DSA coding interviews, then add behavioural rounds.
• Impact: Version one centres on live coding with spoken or typed reasoning.

• Voice, text or both?

• Why it matters: Real interviews are spoken, but voice is harder in noisy hostels and on weak networks.
• Hypothetical answer: Both, with text as fallback.
• Impact: Design must work on low-bandwidth connections and in Hinglish.

• What does success look like?

• Hypothetical answer: Students feel more confident and perform better in real interviews; Scaler gains engaged users who may enrol later.
• Impact: North Star should capture improvement, not just sessions.

Propose the Goal

Help final-year students become interview-ready by giving them realistic, repeatable mock interviews with specific, measurable feedback on problem solving, communication and code quality.

Define the Scope

In scope: a web and mobile AI interviewer for DSA rounds, feedback reports and a practice plan. Out of scope: human mock interviews, system design rounds and company-specific question banks in version one.