Careerbuddy.ai
Project Overview
Context: Group Project for GenAI in Education Seminar
Career Buddy AI is an interactive e-learning platform designed to help high school students discover career paths aligned with their interests and skills. The platform combines immersive job simulations with personalized AI guidance to empower students in making informed decisions about their future.
Target Audience: High school students, particularly seniors
Learning Theories:
1. Situated Expectancy-Value Theory (SEVT) is a framework used to understand how individuals make decisions and choices related to achievement, particularly in academic and career contexts.
2. Authentic Learning Environments (ALE) theory suggests that learning is most effective when it's grounded in real-world contexts and activities that reflect how knowledge and skills are used in practical situations.
3. Metacognition theory, pioneered by John Flavell, is the study of how individuals think about their own thinking processes.
Learning Objectives:
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Identify careers that align with personal interests and skills through exploration and assessment
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Experience real-life career simulations to analyze expectations versus personal interest
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Develop personalized career plans with AI-guided support and resource recommendations
Tools & Technologies:
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Figma for UX/UI design
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AI chatbot integration (Claude, Chat GPT)
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AI video generation (Heygen, Veed.io)
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RECAP AI prompting framework for structured guidance
Design Process
Analysis Phase
I began by identifying a critical gap in career exploration for high school students. Through secondary research analyzing literature, case studies, and expert discussions, I discovered three key challenges:
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Lack of Personalization: Existing platforms provide generic career advice without adapting to individual interests
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Limited Hands-on Experience: Traditional career counseling focuses on reading about careers rather than experiencing them
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Low Student Engagement: Students find conventional career exploration activities unrelatable and unmotivating
Design & Development
The solution evolved through multiple iterations guided by the ADDIE model and design thinking principles:
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Initial Architecture Planning: Developed a core structure with self-assessment, career information, reflection, and planning components
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Card Sort Testing: Gathered user feedback revealing needs for:
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Quick access paths for students with established interests
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Platform introduction and overview videos
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Support features within simulations
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Social connection opportunities among users with similar interests
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Interactive Prototype Development:
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Created a Figma prototype incorporating user feedback
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Designed immersive job simulations rather than text descriptions
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Implemented an AI chatbot using the RECAP framework (Reflect, Explain, Compare, Act, Plan) to guide structured reflection
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User Testing & Refinement:
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Conducted quantitative and qualitative testing measuring knowledge gain, navigation efficiency, and user satisfaction
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Added immediate feedback after simulation challenges
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Enhanced accessibility with AI voice features
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Revised AI prompting to include actionable next steps and phased recommendations
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Impact & Results
The platform effectively enhances students' understanding of potential careers while boosting their confidence in identifying and planning career goals. User testing demonstrated:
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Improved Career Knowledge: Students showed measurable improvements in understanding specific career paths after completing simulations
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Positive User Experience: The platform received high marks for engagement and intuitive navigation
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Effective AI Guidance: Users valued the personalized reflection and planning facilitated by the AI chatbot