AI-Powered Resume Review – Scalable Career Support System

My role. UX Designer – AI-Driven Product Design for Career Services

Project description. Designed Great Learning’s resume review experience using AI to improve quality, scalability, and learner satisfaction. Conducted research with 15 users across career stages. Introduced persona-based templates, inline feedback, confidence heatmaps, and ATS keyword matching. Reduced internal QA time by 70%, cut review time from 48 hrs to 10 minutes, and achieved 90%+ CSAT. Empowered users with actionable, emotionally aware resume support at scale.

UX Research, UX & UI Design, Generative AI Prompt, Information Architecture, Wireframe

At Great Learning, resume reviews were an essential part of the career support program. These reviews were traditionally conducted by external experts but often lacked consistency, quality, and scalability. Internal teams had to manually audit every review, increasing turnaround times and reducing learner satisfaction.

Our goal was to design an AI-powered, internal resume review system that could support a wide range of learner profiles, reduce operational overhead, and deliver role-relevant, high-quality feedback.

The resume review process was unscalable, inconsistent, and overly dependent on human reviewers. Each resume needed internal QA before being returned to the learner. Learners came from highly diverse backgrounds—freshers, mid-career professionals, career switchers, and senior executives—making a one-size-fits-all solution ineffective. The system had to account for this diversity and improve both quality and speed.

Freshers: Struggled with structure, project descriptions, and ATS formatting

Mid-career: Needed help reframing long job histories and transitions

Senior professionals: Sought clarity around leadership impact and scope

Career switchers: Required guidance on showcasing transferable skills

Tech roles: Needed help presenting stacks, GitHub links, and project outcomes

Business roles: Required clarity, formatting help, and achievement framing

We conducted a structured study with 15 users (5 early-career, 5 mid-career, 5 switchers) through surveys, interviews, and usability testing of a prototype.

Quantitative:

73% had heard of ATS; only 46% understood optimization

87% found keyword suggestions useful

93% acted on at least one tool suggestion

Net Promoter Score: +26

Qualitative:

Users wanted clear, actionable feedback beyond grammar

Feedback needed to feel personalized, not generic

Trust improved with explanation, examples, and visual indicators

Users felt anxious; emotional reassurance was essential

Consistency: Replace subjective human feedback with modular, persona-specific templates

Inline Feedback: Show suggestions directly within resume previews

Clarity: Use simple language and remove technical jargon

Control: Allow version comparisons and iteration

Scalability: Support multiple user types and learning goals

Persona-based templates for resume structure

AI-generated bullet improvements based on role and experience

Confidence heatmaps for resume sections

Job description upload for keyword matching

Internal admin panel for quality review and overrides

This project wasn’t just about automation—it was about creating emotionally intelligent UX that empowered learners to confidently apply for jobs. By combining AI with user-centered design, we delivered scalable, personalized support that made a measurable impact on outcomes.