AI Interview Training App (AI Features)
A training platform that offers a set of AI-based interview tools to help job seekers ace their interviews.
Role: Product Lead
Problem Statement
Job seekers want immediate feedback on how to improve their interview skills.
Goal Statement
Develop an AI feedback feature to provide users with personalized, real-time insights on their interview performance.
Many interview preparation tools are generic and don’t offer the realism or personalized feedback needed for effective practice.
Plus, there’s a gap when it comes to tracking progress and providing tailored recommendations to help users improve their interview skills.
By offering instant feedback on key areas such as body language, eye contact, and communication style, the aim is to help users quickly identify strengths and areas for improvement.
This feature will empower users to refine their skills, boost their confidence, and feel better prepared for their interviews, creating a more engaging and effective learning experience.
DISCOVER
User Research
We spoke with job seekers to understand their challenges, and learned about three key needs:
Real-time feedback during interview prep to help users track their progress.
Quick coaching to point out areas for improvement and encourage ongoing skill development.
Personalized learning plans tailored to each user's unique goals.
IDEATE
User Scenarios
After conducting interviews, we identified multiple user scenarios and defined key insights, which were then translated into specific user needs.
We then brainstormed AI-powered features tailored to address the unique requirements of each user persona.
How The AI Features Work
The interview training tool leverages natural language processing (NLP) and machine learning (ML) technologies to create an interactive and personalized training experience for users.
Human-AI Interaction Guidelines
Next, I used Microsoft's HAX Guidelines to make sure the AI features followed best practices for Human-AI interaction.
The goal was to create an intuitive, user-friendly experience that builds trust and smooth collaboration between users and AI. This meant aligning the AI with user needs, being transparent about AI decisions, and ensuring the interface provided clear communication and helpful feedback.
Personalized AI Learning Plan
The AI feedback feature was a key part of our gamification strategy, designed to create a personalized and engaging learning experience. It offered instant, AI-powered feedback, acting as both a motivational tool and a reward for completing sections.
This immediate feedback encouraged users to keep going, while also celebrating their progress and reinforcing their achievements.
Actionable Feedback Framework
Next, I applied the Actionable Feedback Framework to ensure the feedback was clear and easy to act on. This 4-step process includes outlining the Situation, the Behavior, the Impact, and the Next Steps.
By following this approach, I was able to deliver instant AI feedback on key areas such as body language, eye contact, and verbal tics, helping users make meaningful improvements right away.
DESIGN
Wireframes
The wireframing process aimed to turn complex AI features into an easy-to-use, intuitive design. I created high-fidelity wireframes that focused on layout, navigation, and displaying feedback results clearly.
These wireframes were tested with stakeholders to refine usability and make sure the design matched both user needs and the app’s goals, setting a solid foundation for the final interface.
UI Design
AI-Powered Feedback and Coaching for Interview Success
The new AI features provide users with instant, detailed insights into their interview preparation progress.
The “AI Feedback Report” highlights specific strengths, showing areas where users excel, as well as identifying opportunities for improvement.
The “Action Plan” translates these insights into personalized, step-by-step coaching, offering clear guidance and practical tips to help users enhance their performance.
Enhanced and Engaging Learning Experience
The updated learning experience combines learning, practice, and real-time feedback, making the process more engaging, relevant, and effective.
Eye Contact Coaching Through Calibration
The “Eye Tracking Calibration” flow is crucial for providing precise and personalized feedback on eye contact during mock interviews. During this process, users follow simple on-screen prompts to help the system understand their gaze and adjust accordingly.
Track Progress with Tailored Insights
The "Analytics" screen monitors users' progress over time, highlighting how their skills have improved. It also provides personalized recommendations to guide further growth and development.
TEST
User Feedback
To gather user insights, we conducted a feedback survey focused on three key areas: Value, User Experience, and Results Accuracy.
Initial testing showed users found the feedback actionable and motivating. Early adopters reported feeling more confident in their interview skills, especially appreciating the clarity and usability of the AI insights.
Career coaches also gave positive feedback, noting the feature’s ability to enhance their guidance.
Impact
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20%
Engagement Rate
After implementing the updated features, engagement rates increased significantly, showing an estimated growth of around 20% over six months.
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12 +
Universities
Expanded the platform’s B2B partnerships, onboarding 12+ universities to assist career centers in helping students land internships and jobs.
Next Steps
Improve Accuracy and Personalization: Enhance the AI’s ability to provide more precise and personalized feedback, including tone of voice and facial expressions.
Add More Feedback Areas: Include feedback on things like posture or confidence to give users a fuller view of their performance.
Allow User Customization: Let users choose specific areas they want feedback on, creating a more personalized learning experience.
Key Learnings
This project taught me how important it is to balance AI capabilities with user-focused design. AI is powerful, but its value comes from providing users with actionable insights and clear communication.
Designing with empathy was also key to making sure users felt supported, not judged.