Design For Digital Future- CONVAI

Aiden is a conversational AI alumni mentor designed to support STA students in navigating Education and Career Guidance (ECG). Our project explores how AI can deliver personalised, relatable and accessible academic guidance through a human-centered conversational experience.

Many STA students experience uncertainty when making academic and career decisions. Existing ECG resources are often perceived as generic or difficult to relate to, leading to hesitation and information gaps during important decision-making stages. This “How Might We” statement reframed our research insights into a focused design challenge. It guided our exploration of how Conversational AI could interpret students’ needs and connect them to relevant ECG support in a structured yet approachable manner.

Survey findings revealed that many STA students enter ECG sessions unsure of what to ask or how to prepare. Common struggles include unclear career direction, difficulty matching jobs to their diploma and uncertainty in building resumes or portfolios. 96% indicated that a pre-session chatbot would help them reflect and feel more prepared.

Meet Aiden, a Conversational AI alumni mentor designed to provide relatable and personalised ECG guidance for STA students. Positioned as an approachable senior figure, Aiden helps students reflect on their goals, structure their thoughts and feel more prepared before engaging in formal ECG sessions.

We mapped the end-to-end user journey to understand how Conversational AI integrates into the ECG experience. The journey highlights student touchpoints, emotions and opportunities from discovering the chatbot to reflecting on their goals and booking an ECG session. This ensured the solution was strategically embedded within the student’s academic pathway.

During ideation, we defined the character’s tone, boundaries and communication principles. Aiden was designed to feel like a supportive senior — friendly, empathetic and concise. The system guides reflection without overwhelming users and avoids replacing formal ECG advice thus positioning itself as a preparatory support tool.

This prototype demonstrates the implementation of Aiden as a conversational AI alumni persona. We defined character background, speaking style and response behaviour to ensure interactions felt supportive, human and relatable. The system was refined through iterative testing to reduce robotic responses and better simulate a peer-like mentoring experience.

We tested real conversational interactions to evaluate tone, clarity and response structure. This stage helped us assess whether Aiden felt supportive and human rather than robotic. Through iterative adjustments to prompts and response style, we refined the system to better simulate a peer-like mentoring conversation. During development, platform constraints limited customisation of the interface background so it remained dark and minimal. In future iterations, we would enhance the visual environment to better reflect the STA context, incorporating warmer tones and a more school-aligned setting to improve approachability and immersion.

User testing highlighted areas for refinement, particularly in tone accuracy and clarity of next steps. Based on feedback, we improved linguistic nuances and proposed automated summaries with clearer ECG booking guidance. These iterations enhanced naturalness, usability and overall user confidence.

The project demonstrates how Conversational AI can serve as a preparatory tool for ECG guidance. With 96% of students open to using a chatbot as a first step, Aiden shows strong potential to enhance reflection and readiness. Future iterations will refine response naturalness, automate summaries and further align the system with institutional support.