This proposal describes a 48-hour, 4-credit Certificate Program in UI/UX Design open to students and professionals from any academic background. No prior design experience is required. Participants who complete the program earn the designation Certified UI/UX Designer, issued by REVA University.
The program addresses where UX is heading — not just where it has been. Participants engage with AI-native design workflows, human-machine interaction for automated environments, invisible and Zero-UI paradigms, and frameworks for ethical, biophilic, and bias-aware design.
The program is task-based and evidence-backed. Every session produces a deliverable. Participants leave with a tested prototype, a documented research and iteration process, an industry-standard portfolio case study, and direct exposure to practitioners through structured Expert Sessions.
I, Arun Murugesan, bring 18+ years of product design practice across SAP, Cisco, Rupeek, and MoneyView, an MS in Computer Software Engineering from BITS Pilani, and current visiting faculty engagement at REVA University.
By the end of this program, participants will be able to:
Plan and conduct user research — structured interviews, contextual observation, card sorting — and synthesize raw data into actionable design insights.
Build information architectures and task flows that reflect how real users think, not how product teams think.
Apply foundational UX principles — Norman's affordances, Nielsen's heuristics, WCAG 2.1 AA — to audit and improve any digital interface.
Design in Figma from wireframe to high-fidelity prototype, using components and design systems.
Run usability tests with real users and document measurable outcomes: task success rate, time-on-task, error rate.
Iterate from evidence — make design decisions that follow from test data, not personal preference.
Document a complete design process as a portfolio case study: problem → research → design → test → outcome.
Apply AI-native UX workflows — generative prototyping, LLM-assisted research, and agentic design systems.
Design for human-machine interaction scenarios including automation handoffs, shared-control interfaces, and multimodal feedback.
Create invisible and Zero-UI experiences using eye-gaze, gesture, voice, and anticipatory design.
Audit interfaces for algorithmic bias, cultural inclusivity, and ethical design.
Apply Privacy by Design and biophilic principles to create experiences that respect human attention cycles, well-being, and user consent.
Engage critically with practitioners through Expert Sessions and apply those insights directly to their own projects.
The program awards 4 credits across 48 total hours — 42 hours of structured teaching and 6 hours of self-directed project submission.
| Component | Hours | Credits |
|---|---|---|
| Core instruction sessions (lectures + workshops) | 24 hrs | 2.00 |
| Expert Sessions (industry practitioners) | 6 hrs | 0.50 |
| Industry Practice Labs (2 × 3 hrs) | 6 hrs | 0.50 |
| Critique and iteration labs | 3 hrs | 0.25 |
| Final jury and presentation | 3 hrs | 0.25 |
| Teaching Total | 42 hrs | 3.50 |
| Self-directed project submission | 6 hrs | 0.50 |
| Program Total | 48 hrs | 4 Credits |
Each 3-hour core session follows this structure:
Each 2-hour Expert Session follows this structure:
AI assist — synthesise interview notes, cluster affinity maps, and spot behavioral patterns faster. Your field observations and judgment stay the foundation.
AI assist — generate layout variations, suggest component structures, and flag heuristic violations. You decide what fits the user's mental model.
AI assist — generate UI variants and component scaffolds in Figma, then analyse usability feedback at scale. Testing and iteration stays human-led.
AI assist — use generative tools to explore HMI, Zero-UI, and ethical design scenarios hands-on, and refine your case study narrative.
Participants choose a project domain at Session 1. The domain must involve a real or speculative human-system interaction challenge.
| Domain | Example Project Ideas |
|---|---|
| Autonomous and AI-Assisted Systems | Self-driving vehicle HMI, AI clinical decision assistant, algorithmic financial advisor UX |
| Smart Environments and Ambient Intelligence | Smart campus space, adaptive retail environment, health-monitoring workplace |
| Wearables and Health Technology | Continuous health monitor interface, mental health wearable, AR fitness coaching |
| Spatial Computing and XR | AR wayfinding system, VR training environment, mixed-reality collaboration tool |
| Conversational and Voice-First Products | Voice-first banking, screenless home assistant, multi-language voice UI for India |
| Ethical AI and Algorithmic Systems | Hiring recommendation audit interface, content moderation transparency layer, recommendation feed controls |
| Any domain with a future-facing human-system challenge | The best projects come from problems you have already observed at first hand |
Expert Sessions are not guest lectures. They are structured industry-exposure components that connect classroom work to professional practice.
Solo practitioner + Q&A + workshop
Solo practitioner + Q&A + workshop
Panel (2–3 practitioners) + Q&A + portfolio review
| Component | Weight | Timing |
|---|---|---|
| Session deliverables | 25% | Sessions 1–11 |
| Expert Session application workshops | 10% | After Expert Sessions 1, 2 and 3 |
| Module 2 mid-program review | 20% | End of Session 6 |
| Final prototype + usability test report | 35% | Session 12 |
| Portfolio case study | 10% | Submitted at Session 12 |
| Total | 100% |
5 user interviews OR 3 contextual observations, with written synthesis.
Card sort or tree test, n ≥ 8.
Clickable Figma prototype covering a complete task flow.
n ≥ 5 participants; task success rate and SUS score documented.
Minimum 3 design changes traceable to specific findings.
Application workshop completed after each Expert Session.
Is the design decision supported by observation or data, not assumption or preference?
Does the decision follow logically from the evidence?
Is the prototype functional and testable by a stranger?
Theory enters at the moment the task demands it. Concepts are introduced because participants need them to proceed.
30% structured input. 70% workshop, testing, and critique. This ratio is protected.
Every design decision requires a one-sentence rationale grounded in research or test data.
Research phases: individual or pairs. Design and prototype: teams of 2–3. Final portfolio and jury: individual.
| Purpose | Tool |
|---|---|
| Generative prototyping | Figma AI, Uizard, Galileo, Relume, Claude Artifacts |
| AI-assisted research and synthesis | ChatGPT / Claude, Dovetail AI, Notably |
| Interface design and design systems | Figma / FigJam |
| Collaborative mapping and IA | Miro / FigJam |
| Usability testing | Maze / Lyssna |
| Bias and accessibility auditing | axe DevTools, Chrome Lighthouse, manual heuristic review |
| Code-based prototypes | HTML, CSS, JavaScript |
MS, Computer Software Engineering — BITS Pilani
BCA — Christ University, Bangalore
| Organisation | Role / Relevant Contribution |
|---|---|
| MoneyView (current) | Head of Design: trust, clarity, and inclusive design at national scale. |
| Rupeek | Built design function from 1 to 22 people; established user research lab. |
| Cisco | SRM enterprise platform: complex multi-role workflows translated into navigable interfaces. |
| DTALE | Design consulting across 30+ brands. |
| SAP (7 years) | Enterprise software design across four product lines; Employee of the Year 2010. |
| Requirement | Notes |
|---|---|
| Classroom / workshop space | Capacity 30; moveable seating and wall/board space. |
| Projection and display screen | For lecture segments, critique sessions and Expert Session presentations. |
| Whiteboard | Minimum 2 large panels. |
| Power outlets | Participants bring their own laptops. |
| Stable Wi-Fi | Required for Figma, Maze and hybrid Expert Sessions. |
