Flexibility
Fit the teaching sequence
I needed activities I could use independently and fit to the pace and objectives of each session.
An Integrated
Decision-Making
Lab Method
for Experiential Learning in
Marketing Strategy Courses
Dr. Jose Mendoza
01 / Background
Teaching and
program context.
Marketing faculty at NYU SPS
Academic Director, MS in Integrated Marketing
Google Higher Ed Faculty AI Fellow
900students
95faculty members
STEMdesignated program
My focus: AI-driven education at scale
How can AI support teaching and learning across a program of this size?
02 / The learning problem and design constraints
Strategic practice that fits
the course and its students.
Students need repeated practice making decisions, seeing consequences, and revising their reasoning under uncertainty.
Why I built custom tools: commercial simulations posed three challenges for my course.
Adaptability
Customize the experience
I wanted greater control over cases, decision rules, feedback, and the role of AI in learning.
Cost
Lower adoption barriers
Commercial licensing costs were a barrier to access and to using tools across courses.
My design response
Build modular tools around course objectives, with adaptable activities, AI learning support, and open resources for faculty.
03 / The teaching innovation
Competitive Studio:
five tools, one teaching method.
Independent browser tools connect practice across the course through a shared learning sequence.
- Framework
- Decision
- Consequence
- Debrief & revision
AI is part of the learning design. Embedded AI learning coaches support feedback and interpretation; an AI counterpart supports negotiation practice.
04 / The method in action: Airlines Simulation
A rival cuts fares.
What do you do?
You lead an airline on the JFK–Boston corridor. Your team must respond before the next round.
6 competing airlines5 roundsOne shared market
Choose a response. Be ready to explain your assumption.
05 / Airlines Simulation: consequences and debrief
Same revenue.
Different economics.
Airline profit
Final profit, USD millions · Spring 2026 classroom run
$17.69MRevenue for both A and E
$2.89MDifference in reported profit
The debrief examines the reasoning.
Airline E reported $8.74M in total costs; Airline A reported $11.63M. The debrief connects pricing, capacity, product, and brand decisions to the cost structure.
Which assumption needs revision? What would you examine before the next decision?
06 / A custom case study
Vela Skincare
An original teaching case, like PITH, built around a strategic investment decision.
$12Mto invest in growth
AExpand the
hero product line
hero product line
BLaunch a
Gen Z sub-brand
Gen Z sub-brand
CEnter
Southeast Asia
Southeast Asia
DBuild a
DTC ecosystem
DTC ecosystem
Choose at least two analytical approaches
AHP Studio
Which option best fits
our priorities?
Evidence for the pitchWeighted ranking
+ sensitivity analysis
+ sensitivity analysis
AI consistency coaching
Scenario Manager
Which option holds up
across different futures?
Evidence for the pitchFour scenarios
+ robustness comparison
+ robustness comparison
Optional AI report explanation
Game theory
How might
competitors respond?
Evidence for the pitchPayoff matrix
+ rival-response logic
+ rival-response logic
Decision trees
Should we invest now,
test first, or stage it?
Evidence for the pitchDecision sequence
+ expected values
+ expected values
One investment recommendationAllocate the $12M to one option—or split across two.
Combine the evidence.
Defend assumptions in the pitch and Q&A.
07 / AI’s role in the teaching design
AI supports the learning.
Students own the judgment.
Faculty + AI
Development partner
GitHub Copilot supported the development of purpose-built course tools.
Faculty define the objectives, inspect the activity, and design the assessment and debrief.
Student + AI
Learning coach
- Pricing: feedback on strategy.
- AHP: consistency coaching and explanations.
- Scenarios: optional report interpretation.
Student + AI
Negotiation counterpart
An AI counterpart responds to proposals in a brand partnership negotiation.
AI feedback helps students examine their approach against the negotiation rubric.
Check the feedback. Test the assumptions. Defend the decision.
Students compare AI explanations with results and justify revisions. AHP and scenario calculations follow defined methods; faculty and students review AI interpretations and evaluations.
08 / Assessment and debrief
Assessment makes
reasoning visible.
Across activities, students explain assumptions, interpret consequences, and justify revisions—including how they use or challenge AI feedback.
Example: airline activity assessment
20%Use of data
20%Competitive adaptation
30%Economic performance
30%Final strategic reflection
A question worth pursuing
One AHP reflection describes revising weights until the ranking matched the team’s initial preference.
Did the team improve its judgments, or tune the model to its preferred answer?
Ask students to justify each revision, compare the sensitivity results, and explain what evidence would change their recommendation.
09 / Student-reported learning
Evidence of how students
describe their reasoning.
“
10 / The faculty takeaway
What faculty can
take into their courses.
- A repeatable teaching methodStart with one decision students struggle to make. Connect the choice, its consequences, and a structured debrief.
- Resources to adaptUse a classroom case, an existing activity, or public code. Match the context and assessment to your learning objective.
- AI as a development and learning partnerCustomize practice with AI. Faculty inspect the activity and feedback; students justify their own decisions.
Start with one objective, one activity, and one debrief.Build connections across sessions as students gain experience.
Tools and teaching resources
11 / Beyond the original course
Continued adoption
and next steps.
Adoption and evolution
Adoption in other courses
Tools now serve other courses, including the Pricing Sandbox.
Independent tools
Separate applications made it easier to adopt individual activities.
Marketing Mix Studio ↗
Evolved from the toolset and already used in two courses.
Extending the design
Custom case studies
The PITH brand exercise applies the design principles. A presentation is forthcoming at NYU’s Faculty Success Initiative.
Faculty adaptation
Design principles accommodate faculty with different technical skill levels.
Pricing Sandbox 2.0
Planned for teaching in Spring 2028.
Thank you
mma.jose-mendoza.com
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