Background

The concept of Radical X is to create a personalized, interactive, and engaging platform. The problems faced in this platform is that in personalized learning a lot of online learning is generic and not personalized. Also, enthusiasm tends to burnout certain kids. We actually want to build a gamified platform for AI guided adaptive learning. This will create a reward-driven social learning with simulated workspaces and industry challenges.
Gen-Z wants game like learning. Therefore at Radical X we want a scalable tutoring, dynamic adaptability, Gen-z resonance, and an empathetic AI.

Problem statement

In the dynamic field of UX design, both novices and seasoned professionals encounter substantial barriers in honing their interview skills. The absence of dedicated platforms contributes to uncertainties and difficulties when navigating interview scenarios and presenting skills with confidence. The deficiency in resources providing targeted feedback, industry-specific questioning and an engaging learning environment further compounds the challenges faced by current generation of UX designers, hindering their ability to prepare effectively for the demands of the job market. 7 team members

Solutions and Iterations

Solution

In response to the challenges prevalent in the UX design realm, our solution, aims to revolutionize interview preparation by introducing a comprehensive AI powered platform tailored explicitly for aspiring and experienced professionals. BY offering a dedicated space for honing interview skills. Our solution will provide personalized feedback, simulated industry-specific scenarios and an engaging learning environment. Through this we strive to empower UX designers to confidently navigate interview challenges, bridging the gap between their skills and market demands.

Challenges we faced

Industry knowledge: Staying up to date on industry trends and best practices is crucial in UX design. An interview prep tool can provide the latest information to tools, methodologies, and design principles, helping candidates demonstrate their knowledge during interviews.
Feedback loop: Immediate feedback is crucial for improvement. A prep tool can provide constructive feedback on responses, portfolio presentations, and design exercises, allowing candidates to identify areas for improvement.
Time management: UX designers often face time constraints in real-world projects. Interview prep tools can include timed design exercises to help candidates practice managing their time effectively.
Research methods: Questions may focus on the candidates familiarity with a variety of research methods such as user interviews, surveys, usability test, and competitive analysis. Interviewers may inquire about the candidates rationale for selecting different research methods in different scenarios.

Pain Points and Core features

Pain points

-Lack of specialized preparation resources: Many UX designers struggle to find resources that specifically cater to the unique demands of their fields interview. Rex fills the gaps with tailored content and scenarios.

-Inadequate feedback Mechanism: Traditional interview preparations often lack personalized, constructive feedback, leaving candidates uncertain about their performance. Rex's AI driven feedback provides specific actionable insights.

-One-size- fit's- all approach: Generic interview tool fail to address individual strengths, weaknesses, and learning styles. This soft skills development is catering to the diverse needs of each user.

Neglect of soft skills: Technical skills are often the main focus, while soft skills like communication an presentation are often overlooked. Rex balances technical and soft skill development. Rex balances a technical and soft skill development.

Tracking progress: Often there is no clear way to track improvement or stay motivated. Progress tracking and goal setting are used to keep users engaged and foster growth.

Core features

1. Accessible interview preparation Content.

User onboarding: guiding users through the tool Features. A personalized setup to align with user preferences and skill levels.

Initial Assessment: Diagnostic assessment to gauge the users current skill set and knowledge. Evaluation generating a user preference and skills.

Learning Modules: Advanced UX design concepts. Videos, articles, quizzes and case studies.

Category specific preparation: Technical, situational, and behavioral.

2. Interactive mock interview simulations: Industry specific question bank. Clear and accessible button or link to initiate mock interview. AI conducting mock interview scenarios; technical, situational, background. Option to save and review interview options. Providing users with flexible audio and text responses.

3. Performance review and interview feedback:

AI generated personalized feedback

on interview performance, offering communication clarity, technical accuracy and confidence. Immediate constructive and supportive AI feedback. With feedback provides a suggested answer.

4. Progress tracking and goal setting:

Accessible visual representation of progress i.e. progress bars and graphs. Tracking metrics included completed interviews, feedback scores, and identified areas of improvement. Goal-setting functionality allowing users to monitor achievements.

Paper Wireframe

Low fidelity prototypes (please swipe)

High-fidelity prototypes (please swipe)

Final Team high-fidelity prototype

Case study

Title: Creating an AI generated interview prep tool.

Author: Fatou Jack, UX design intern, and Jackfatou@hotmail.com

Stakeholders: CEO of RadicalX; Talha, Assistant: Parita, Assistant: Vika, UX design interns: Meeghan, Peeki, Fatou, Ecila.

Date: January 10th, 2024

Project background: The CEO Talha wanted to create a platform where youth could practice interviews because sometimes people don;t have mentors to help them practice. Talha wants free access for interview preparation, users who can answer questions compellingly and a product to master. We started out by understanding how people currently prepare for interviews.

Research goals:

These are the answers chat gpt came up with:

Diverse Skill Set: UX design requires a diverse skill set, including user research, information architecture, interaction design, visual design, and prototyping.
An interview prep tool can help candidates refine and showcase these skills effectively.
Industry Knowledge: Staying updated on industry trends and best practices is crucial in UX design.
An interview prep tool can provide information on the latest tools, methodologies, and design principles, helping candidates demonstrate their knowledge during interviews.
Feedback Loop: Immediate feedback is crucial for improvement. A prep tool can provide constructive feedback on responses, portfolio presentations, and design exercises, allowing candidates to identify areas for improvement.
Time Management: UX designers often face time constraints in real-world projects. Interview prep tools can include timed design exercises to help candidates practice managing their time effectively.

UX interviews from chatgpt:

Past Research Projects:
Interviewers may inquire about the candidate's previous research projects. This involves understanding the scope, methodology, and outcomes of their research efforts.
Candidates might be asked to elaborate on specific challenges they encountered during their research and how they overcame them.
Research Methods:
Questions may focus on the candidate's familiarity with a variety of research methods, such as user interviews, surveys, usability testing, and competitive analysis.
Interviewers may inquire about the candidate's rationale for selecting specific research methods in different scenarios.

User Personas:

Creating user personas is a common UX research task. Candidates might be asked about their experience in developing and utilizing personas to inform design decisions.
Interviewers may want to know how candidates ensure that personas accurately represent the target user base.

Data Analysis:

Candidates may be asked about their approach to data analysis. This could include interpreting qualitative and quantitative data gathered during research.
Interviewers may explore how candidates derive insights from user feedback and translate those insights into actionable design recommendations.

Usability Testing:

Usability testing is a key aspect of UX research. Interviewers may ask candidates to discuss their experience in planning, conducting, and analyzing usability tests.
Questions may also revolve around how candidates iterate on designs based on usability test findings.
How will the results of the research affect your design decisions? The results of my research will affect the way I create steps for the user to be interviewed by AI. During research, if I know some of the pain points, I can avoid those in my design.

What are the questions your research is trying to answer?

User Needs and Pain Points

What are the most common challenges UX/UI designers face while preparing for interviews?

Current Solutions and Practices

What tools or services are currently available for interview preparation in the UX/UI field?

Learning and Improvement

How do UX/UI designers prefer to receive feedback or learning material (e.g., videos, interactive sessions, written content)?

Technology and AI Integration

How can AI technology enhance the interview preparation process for UX/UI designers?

Competitive Analysis

Who are the main competitors offering interview preparation tools or services for UX/UI designers?

How can you measure progress toward the research goals?

KPIs might include:
Time on task: How long it will take for the user to do learning module taks before he or she completes the mock interview. Ideally I want my users to spend little time doing this.
User error rates: viewing the CTA buttons and making sure that users do not press the wrong one to move forward during the learning module or the mock interviews.
Drop-off rates: How many people drop out when they see that the mock interview question is hard and they want to redo the interview.
Conversion rates: Percentage of people who got into the mock interview after completing the learning modules.
System Usability Scale: How easy is the mock interview to use with AI? How many people will recommend this app?

How will you collect data? How will you analyze the data once you get it?

On January 10th 2024, I conducted usability research on 5 individuals. I asked them how they felt about my app and what were some improvements I could make. The way we collected data was through secondary research and looking at existing platforms. We had to be specific and relevant to insight and look into articles. Also, we had to think of the problems and challenges that were being faced. The way I will analyze the data once I get it is by knowing how interview prep works so that I can add it into my design. Also, this information can tell me how people’s experience was when being interviewed and if the interview prep tool works with AI. I will also have the idea of my users' pain points.

The methodology should be detailed so that other researchers can understand what you did, the choices you made, and the limitations of the methods employed to decide if or when further research is needed.

Aspiring UX/UI designers preparing for job interviews.

They are allowed to use screenreaders
2 Experienced designers seeking to polish their interview skills. One has never had a job and the other has just completed 2 internships.
2 Design educators looking for tools to assist their students. One is blind and the other is good.
(A second usability study will need to be performed.)

What questions will you ask study participants?

Intro:
Hello participants, may you please sign this document so that I can have your consent to record? Please be reminded that there are no right or wrong answers. Data is being collected because I wanted to understand the use of an interview platform related to AI. Also, I wanted to find out if preparation was effective for the students during their learning outcomes. Thank you participants for your time, my name is Fatou Jack and I will be interviewing you.

Basic questions:

Where do you live? Town or city?
What school do you go to?
What's your age?
Currently, how do you prepare for interviews?
What country are you studying in?
Who is your mentor or teacher?
Have you used AI to practice for interviews before?

Prompts:

1. Find a platform:

Research interview platform
Select platform

2. Start onboarding and education process

Find a CTA button to start practice.
Follow-up: How easy was it onboard?
Go through onboarding and education process

3. Practice a mock interview

Engage in simulated interview sessions with Rex AI

4. Polish answers based on received feedback to enhance quality

Review feedback
Refine responses for improved quality

5. Prepare thoroughly for real interviews in the job market

Track your progress
Focus on preparedness for actual job interviews