Education · Curriculum · 2024

Teaching as Research

Built an AI-integrated design curriculum where AI literacy, DesignOps, and product building replace studio traditions that no longer match industry reality.

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01

Problem

Pre-AI Education

Curriculum teaching tools and aesthetics while industry asked different questions.

500+Students impacted
02

System

Curriculum Framework

Five courses integrating AI, systems thinking, and real product constraints.

3Academic years
03

Process

Classroom as Sprint

Teaching philosophy treating each module as a product iteration.

5Core courses
04

Outcome

Industry-Ready Grads

Graduates equipped for AI-era product and design leadership.

2022Program launch
Client
IIAD · New Delhi
Engagement
Associate Professor
Duration
2022 — Present
Primary Outcome
AI Pedagogy
Stack / Tags
DesignOps · Curriculum

Artifacts from this engagement

ProblemChallenge BriefConstraints and context before the engagement ProcessEngagement PhasesHow the work was structured and delivered SystemSolution ArchitectureWhat was built and how it works OutcomesResults & MetricsMeasurable impact and learnings
Academic Practice / AI Pedagogy / Curriculum Design

Teaching
as Research.

When IIAD brought me in as Associate Professor in 2022, design education in India was still largely pre-AI — teaching tools and aesthetics while the industry was already asking different questions. This is the story of building a curriculum that didn't exist: where AI literacy, DesignOps systems thinking, and the realities of product building replace the studio traditions that had governed design education for 40 years.

0
Students Taught
5
Courses Designed
3
Academic Years
2022
Faculty Since
Institution
IIAD · New Delhi
Role
Associate Professor
Since
2022 — Present
Focus
AI Pedagogy · DesignOps
03
Section 01
The
Gap.
What design education in India looked like in 2022 — and why a generation of students were being trained for a profession that no longer existed.
The curriculum was built for 2005.

Design education in India in 2022 was predominantly studio-based — hand skills, visual aesthetics, typography exercises, print traditions. At institutions that had updated their digital curricula, the focus was on tools: Figma, Adobe Suite, interaction design patterns. The industry had moved past both of these. The designers being hired in 2022 were being asked to think in systems, work in AI-augmented pipelines, understand DesignOps governance, and operate as strategic partners to product and engineering teams — not as aesthetic consultants at the end of a brief.

The gap between what students were learning and what they were being hired to do had become a full degree's worth of distance. IIAD's brief was to close it. Not incrementally — structurally.

Foundational Observation · First Semester · 2022
Students could design. They couldn't think in systems. That was the gap — not skill, but mental model.
01
AI was treated as a threat, not a tool. The dominant conversation in design education about AI in 2022 was defensive — will it replace designers? This framing produced graduates who approached AI with anxiety rather than literacy. The curriculum needed to reframe AI as infrastructure that amplifies design judgment, not a replacement for it.
02
DesignOps had no academic framework. The discipline that governs how design teams scale, govern quality, and deliver consistently at speed existed in industry but had no equivalent in academic curricula. Students were graduating with no concept of how design actually functions inside a product organisation.
03
Critique culture was approval-seeking, not rigorous. Studio critique in most institutions had drifted toward validation — tutors noting what worked, students presenting finished work. The failure modes, the structural decisions, the reasoning behind choices — these were neither required nor modelled. The result was students who couldn't defend a design decision with precision.
04
Theory and practice were sequential, not simultaneous. Foundational theory in years one and two, practical application in years three and four. This structure produces graduates who can theorise and graduates who can execute but rarely both — and the industry requires both simultaneously from the first day of employment.
04
Section 02
The
Method.
3 academic years. 5 courses redesigned or built from scratch. The classroom treated as a product sprint — hypothesis, prototype, test, iterate.

Students are the
fastest feedback loop.

Every course was treated as a product with a user. The student is the user. The learning outcome is the product. When a module consistently produced confused students, that was a design failure — and it was treated as one. Modules were redesigned mid-semester when the signal was clear enough. No curriculum is fixed. All of it is version-controlled.

5 Courses · IIAD Industrial Design · 2022 — Present
01
AI & Design Thinking
AI as infrastructure · Prompt engineering as design skill · Human-AI collaboration models · Ethics frameworks
AI · New
02
DesignOps & Systems
Design system architecture · Token systems · Governance models · Cross-functional collaboration
Ops · New
03
Product Strategy
Brief writing · Stakeholder mapping · Business model integration · Design as strategic investment
Core · Rebuilt
04
Human-Computer Interaction
Cognitive models · Interaction patterns · Accessibility first · Testing and validation frameworks
Core · Updated
05
Design Research Methods
Ethnographic methods · Synthesis frameworks · Evidence-based decision making · Research documentation
Core · Updated
5 courses — 2 built from scratch (AI & Design Thinking, DesignOps & Systems), 3 structurally updated
01
Curriculum Audit
Semester 1 · 2022

First semester spent as much in observation as in teaching. Documented what students could do, what they couldn't, and the gap between their mental models and the mental models industry was expecting. Ran structured exercises to test systems thinking, AI familiarity, DesignOps literacy, and research rigour. Results confirmed the briefing: the gap was structural, not skill-level. You couldn't close it by teaching harder. You had to teach differently.

02
AI & DesignOps Modules — Built from Zero
Semester 2–3 · 2022–23

No existing academic framework for teaching AI literacy to design students. No DesignOps curriculum template. Both had to be built from first principles — drawing on industry experience (Nagarro, Porsche, Verizon) to define what graduating students actually needed to know. AI & Design Thinking v1 was a 12-week module treating prompt engineering as a design skill, AI tools as collaboration partners, and ethics as a structural constraint — not an appendix. First cohort tested it, it worked, it became a core course.

03
Critique Culture Redesign
Ongoing · All Semesters

Replaced approval-seeking critique with structural critique: every review required students to present the decision logic behind their choices, not the choices themselves. "Why this layout?" not "here's the layout." Introduced failure documentation as a graded deliverable — the most valuable design artefact in any sprint is the thing that didn't work and why. Students initially resisted; by semester three, the critique sessions were the most referenced part of the course by students in exit interviews.

04
Industry Integration — Live Briefs
Year 2 Onwards · 2023–Present

Introduced live industry briefs in year two: real problems from real organisations, with real stakeholders providing feedback. Students designed for Bolo Buddy (actual product requirements, actual user constraints). They built components for design systems that were actually deployed. The brief was not simulated. The consequence of a bad decision was visible. That shift — from academic exercise to real accountability — produced a step-change in the quality and rigour of work.

05
Section 03
The
Framework.
Four teaching principles that replaced the studio model — and the specific curricular innovations that follow from each.
01
01
Theory + Practice
Simultaneously
No concept taught without a real-world analogue. No practice without theoretical grounding. Both, always, together. In DesignOps & Systems, students build a token architecture the same week they learn why tokens exist. In AI & Design Thinking, students prompt-engineer a design solution the same session they study the cognitive model behind LLM behaviour. The gap between theory and application is zero — because they share the same classroom hour.
02
02
Failure as
Primary Data
Failed experiments are not failures. They are the most reliable data source available. Failure documentation is a graded deliverable in every course — not as punishment, but as professional practice. The student who can articulate exactly why their design decision didn't work is already thinking like a senior designer. The one who can only show the thing that worked is showing you the last 10% of their process.
03
03
Industry Velocity,
Academic Rigour
Deliverables are production-quality. Deadlines are real. Industry standards are the baseline. The classroom is not a safe space from professional standards — it's a laboratory where those standards are tested, challenged, and occasionally found wanting. Students presenting to actual product teams, with actual feedback, learn something that no simulated brief can teach: that good work is not a grade, it's a consequence.
04
04
AI Literacy as
Design Infrastructure
The AI & Design Thinking course treats AI tools as professional infrastructure — the same way a surgeon treats a scalpel. You understand how it works, you develop precision with it, you know its failure modes, and you maintain judgment about when not to use it. Prompt engineering is taught as a design skill: precision of language, clarity of intent, iteration on output. Students leave with functional AI literacy, not AI anxiety.
06
Section 04
The
Impact.
500+ students across 3 academic years. Placements that signal the curriculum is producing the right graduate.
500
0
+
Students Taught
Across 5 courses over 3 academic years at IIAD, New Delhi.
5
5
Courses Active
2 built from scratch (AI & Design Thinking, DesignOps & Systems), 3 structurally rebuilt.
2
2
New Disciplines Introduced
AI literacy and DesignOps — neither existed in IIAD curriculum before 2022.
3
3
Academic Years
Ongoing. Curriculum updated every semester based on industry signal and student feedback.
Teaching as
Research
The principle that students are the fastest feedback loop is not a metaphor. Every cohort reveals something about how design concepts actually land — which analogies work, which exercises produce insight versus confusion, which industry constraints students find immediately legible versus baffling. That feedback updates the curriculum continuously. Three years of teaching has produced more usable knowledge about how to teach systems thinking than any amount of reading about pedagogy. The classroom is genuinely a research environment.
Industry ↔
Academia
Bridge
The most productive outcome of the IIAD role is not the curriculum itself — it's the bidirectional transfer between practice and academia. Industry problems inform curriculum design. Curriculum experiments surface frameworks that become usable in client engagements. The DesignOps governance model used in the Porsche engagement was tested on a live student project six months before it was deployed commercially. The academic context provides a low-stakes environment to stress-test frameworks that would otherwise have to be developed on a client's budget and timeline.
What Students
Now Know
Graduating students from courses touched by this curriculum can: articulate why a design decision was made in systems terms, not aesthetic terms; operate in an AI-augmented design pipeline with professional literacy; build and contribute to a design system (not just use one); run a structured critique that surfaces decision logic rather than aesthetic preference; and write a design brief that integrates business constraints from the first line. None of these were expected of IIAD graduates in 2021. They are now the baseline.
07
Section 05
Key
Learnings.
What designing curriculum teaches that designing products doesn't — and what it confirms about both.
01
01
Teaching is
Product Design.

A course is a product. The student is the user. The learning outcome is the value delivered. The critique session is the onboarding flow. The failed assignment is the error state. Every design principle that applies to building a good product applies to building a good course — and the feedback loop is faster, because students tell you immediately when something doesn't work. The best design education I ever got was designing courses.

02
02
Practitioners Make
Better Educators.

The most useful thing I brought to IIAD was not academic knowledge — it was client experience. The Rapipay field research methodology became a course exercise. The Porsche governance framework became a DesignOps module. The Nagarro hiring rubric became a course assessment criteria. Industry experience is not supplementary to design education. For the disciplines that matter most — systems thinking, AI integration, product strategy — it is the curriculum.

03
03
AI Anxiety is a
Curriculum Failure.

Every student who graduates with AI anxiety rather than AI literacy is the product of a curriculum that failed them. The anxiety comes from treating AI as a force acting on design rather than a tool used by designers. The moment students understand the mechanism — what LLMs actually do, how they fail, what they cannot do — anxiety dissolves and utility thinking takes over. Demystification is the first pedagogical act.

04
04
Critique is the
Most Underused Tool.

Well-structured critique is the highest-leverage educational intervention available. It surfaces reasoning, exposes assumptions, forces articulation of decision logic, and models professional discourse — simultaneously, in real time, for everyone in the room. Most institutions use it for validation. Used rigorously, it is a systems thinking exercise, a communication skills session, and a design methods workshop all at once. The investment is zero. The return is compounding.

← Previous Case Study
Nagarro · Zero
To Sixty.
Practice Build · 0→60 Designers · 3 Years
Next Case Study →
Porsche · Service
Center.
DesignOps · Enterprise · 60% Efficiency Gain

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