Career Buddy AI is an interactive e-learning platform designed for high school students to explore potential careers that align with their interests and skills. The platform offers real-world job simulations, allowing students to experience what different careers are like in practice.
By leveraging an AI-powered chatbot, Career Buddy AI provides personalized guidance through reflective prompts. The AI system tailors career development plans, suggesting actionable next steps for skill-building. It also connects students to resources available in schools, communities, and online courses to help them pursue their goals.
Career Buddy AI empowers students to make informed decisions about their future by combining immersive experiences with personalized, actionable support.
Project Overview
Target Audience: High school students, expecially seniors
Tools & Technology:
1. Figma
2. AI chatbot (Chat GPT, Claude)
3. AI video generator (Heygen, Veed.io)
Design Model & UX Methods:
1. ADDIE model
2. Design thinking model
3. UX research method- Card-Sort
4. AI Prompting Framework- RECAP
Learning Objectives:
Student will be able to...
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Identify careers that align with their interests and skills through exploration and assessment.
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Explore real-life career simulations to analyze career expectations and personal interest.
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Develop a personalized career plan by interacting with an AI chatbot and utilize relevant resources for next steps in career preparation.
Analyze
1. Identifying the Problem
Career exploration is a crucial process for high school students, yet many struggle to find clear guidance on career options aligned with their interests and skills. Traditional career counseling services often lack personalized and engaging resources, leaving students uncertain about their future paths.
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2. Research Methodology
To identify the specific needs of high school students in career exploration, I conducted a secondary research analysis, gathering data from:
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Existing literature on career guidance challenges in K-12 education
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Case studies of successful career guidance programs
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Online discussions and articles from educators, students, and career counselors
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3. Key Findings
Through the research, I identified several gaps in existing career exploration methods:
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Lack of Personalization: Many platforms provide generic career advice without adapting to students' unique interests and skills.
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Limited Hands-on Experience: Traditional career counseling focuses on reading about careers rather than allowing students to simulate real-world job tasks.
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Low Student Engagement: Students often find career exploration activities dull and unrelatable, reducing their motivation to actively participate.
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Design
In the design phase, my team structured the module into four key components:
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Self-Assessment: An interest and skill-matching questionnaire to personalize career suggestions.
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Career Simulations: Text-based interactive scenarios where students engage in day-to-day tasks of selected professions.
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Reflection & AI Chatbot Interaction: Students reflect on their experience and receive AI-guided feedback on skill development.
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Career Planning Guide: A downloadable report suggesting relevant courses and extracurricular activities.
Develop
Prototype 1- Information Architecture Plan
After developing the initial plan, we came up with the idea to add a video-based job simulation feature, allowing students not only to get inspired but also to experience what a career is really like. Additionally, with AI chatbots becoming increasingly sophisticated, we decided to incorporate AI-generated videos and use AI as a career guide to help students reflect on their experiences and create tailored development plans.
The information architecture plan after adding job simulation and AI chatbot feature
Test 1- Card Sort
We conduted card sort activities to test our initial information architecture.
Belows are the feedbacks that we got.​
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“The user hopes to get into the student profile page on the home page”
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“The first question for assistants is do you have a job that you prefer already? After that, it is divided into two sections”
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“Have the job compare list”
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“The user hopes to have an overview video of this platform as well as how does the platform work video after they start.”
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“The user thinks the mentor or support feature can be put in the simulation phase for students to ask questions”
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“Users don't need to sign up until they think they need to do”
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“It can create the space for communication between users with the same background or interests”;
Prototype 2 - Figma
Based on the feedback, we added the following key interactions to our information architecture and we designed our first platform prototype.
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We added a brief 60-second welcome video to the very beginning to let users know we are excited to partner with them and to build empathy with their career exploration journey.
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We added a “How to use this platform” video to provide an overview of what to expect and how to interact with the platform.
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If the user doesn’t need to explore career options because they know the role they are interested in, they can jump directly to that role by doing a search or clicking on the most common roles button.
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We created real scenarios instead of text to help users better immersion in the real job role
Initial Figma prototype design created after finalizing the information architecture
Prototype 2 - AI Chatbot
Our AI chatbot prompts are designed using the RECAP framework (Reflect, Explain, Compare, Act, Plan) to guide students through structured reflections on their job simulation experiences. The chatbot encourages students to analyze their learning, compare their expectations with real-world tasks, and identify transferable skills. By prompting students to actively plan their next steps, the AI facilitates the creation of a personalized career plan, helping them set actionable goals for skill development and future career exploration.
Test 2
In our second test, we plan to focus on three main areas:
1. How much knowledge students will gain about the example career of a product manager after using the simulation?
2. Application navigation (including ease of use)
3. Interactions with the AI chatbot
For the application navigation component, we will evaluate it using three key metrics: effectiveness, efficiency, and user satisfaction.
Testing Methodology: Quantitative & Qualitative Approaches
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Pre-Survey (Quantitative)
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Assesses users' baseline knowledge of the product manager role.
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Provides a comparison point for post-test knowledge evaluation.
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Prototype Navigation (Qualitative)
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Users independently explore the Figma prototype.
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Observations and screen recordings capture usability challenges and unclear navigation elements.
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Job Simulation & AI Chatbot Reflection (Qualitative)
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Users complete the job simulation to experience real-world tasks.
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Engage with the AI chatbot for guided reflection, providing insights on chatbot interaction quality.
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Post-Survey (Quantitative & Qualitative)
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Measures improvements in career understanding through knowledge-based questions.
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Collects user feedback on platform usability and AI chatbot effectiveness.
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Survey questions are adapted from the Career Decision and Self-Efficacy Scale, conducted via Google Forms to measure users’ confidence and understanding before and after the simulation.
Prototype 3
Quantitative & Qualitative testing results
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Effectively enhances students' understanding of product management and boosts their confidence in identifying and planning career goals.
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Receive positive feedback for its user experience, engagement, and personalized AI-driven guidance.
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Users suggested adding detailed feedback for each job simulation challenge and incorporating a stronger hook to encourage them to create personalized career plans with AI.
Add feedback after a job simulation challenge.
Added a conclution after job simulation tasks and guide user to the AI reflection part.
Added a speaking function to the AI reflection feature to enhance accessibility.
AI prompt edited
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We also revised the AI prompt to ensure the career path plan includes immediate next steps the user can take, phased recommendations from short-term to long-term goals, and a stop instruction to conclude after 10 questions.
