Project Overview
- Problem: Students use AI to plagiarize due to time pressure & lack of skills, harming their learning.
- Contribution: Conducted competitor analysis, brainstormed user flows, created solutions based on project brief goals.
- Contraints: A 3 day time limit resulted in limited user research; relying heavily on competitor product research to create a precise solution.
- Outcome:Designed a dual-interface AI integrity tool that detects AI generated content, promotes academic integrity, & empowers educators with ethical decision-making tools.
Project Goals
- Promote academic integrity through a transparent AI-powered plagiarism detection system.
- Empower educators with explainable tools that support fair decision-making.
- Support student learning with constructive feedback & clear AI detection explanations.
- Establish trust in AI tools by providing ethical, non-invasive content analysis for both teachers & students.
Challenges
- Problem: Existing AI detectors lack transparency, making it hard for educators to justify decisions.
- Impact: Students feel targeted by “black box” AI models, eroding trust in academic institutions.
- Time Constraint: 3-day sprint limited user testing; relied heavily on competitor research & rapid iteration.
Solutions
- Educator Features: Line-by-line plagiarism probability scores, override controls, source linking, & in-line commenting.
- Student Tools: Interactive feedback modals, revision guidance, & conversation threads with educators.
- Design Principles: Dual-dashboard UX (teachers & students), ethical AI framing, visual clarity, & trust-driven UI patterns.
Process
- Competitor Audit: Reviewed Copyleaks, PanGram, and Turnitin to identify UX gaps in explainability and interaction.
- Flow Design: Mapped user journeys for both educator & student accounts to ensure intuitive interaction across devices.
- Prototyping: Built prototypes with high-fidelity UI aligned to the Reality AI brand system.
- Collaboration: Worked with a team of 5 designers, aligning on goals, feedback loops, & component consistency.
Key Results
- Competition Win: First place at the Reality AI hackathon for creativity & UI/UX execution.
- Feature Completeness: Delivered two fully-interactive dashboards with key functions prototyped.
- User Alignment: Solutions based directly on educator needs, student concerns, & gaps in competitor offerings.
Impact
- Trust & Transparency: Built a system that demystifies AI detection and prioritizes open educator-student communication.
- Future Potential: Concept could evolve by integrating the detection tool into popular learning management systems.
Design Question
How might we design an AI-powered integrity dashboard that not only detects AI-generated content but also empowers educators with clear, explainable, and ethical decision-making tools?
