Flexibility
Fit the teaching sequence
I needed activities I could use independently and fit to the pace and objectives of each session.
Competitive
for Experiential Learning in
Marketing Strategy Courses
Dr. Jose Mendoza

01 / Background
Academic Director, MS in Integrated Marketing
Google Higher Ed Faculty AI Fellow
How can AI support teaching and learning across a program of this size?
02 / The learning problem and design constraints
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.
Flexibility
I needed activities I could use independently and fit to the pace and objectives of each session.
Adaptability
I wanted greater control over cases, decision rules, feedback, and the role of AI in learning.
Cost
Commercial licensing costs were a barrier to access and to using tools across courses.
Build modular tools around course objectives, with adaptable activities, AI learning support, and open resources for faculty.
03 / The teaching innovation
Independent browser tools connect practice across the course through a shared learning sequence.
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
You lead an airline on the JFK–Boston corridor. Your team must respond before the next round.
Choose a response. Be ready to explain your assumption.
05 / Airlines Simulation: consequences and debrief
Final profit, USD millions · Spring 2026 classroom run
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
An original teaching case, like PITH, built around a strategic investment decision.
Which option best fits
our priorities?
AI consistency coaching
Which option holds up
across different futures?
Optional AI report explanation
How might
competitors respond?
Should we invest now,
test first, or stage it?
Combine the evidence.
Defend assumptions in the pitch and Q&A.
07 / AI’s role in the teaching design
Faculty + AI
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
Student + AI
An AI counterpart responds to proposals in a brand partnership negotiation.
AI feedback helps students examine their approach against the negotiation rubric.
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
Across activities, students explain assumptions, interpret consequences, and justify revisions—including how they use or challenge AI feedback.
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
10 / The faculty takeaway
11 / Beyond the original course
Tools now serve other courses, including the Pricing Sandbox.
Separate applications made it easier to adopt individual activities.
Evolved from the toolset and already used in two courses.
The PITH brand exercise applies the design principles. A presentation is forthcoming at NYU’s Faculty Success Initiative.
Design principles accommodate faculty with different technical skill levels.
Planned for teaching in Spring 2028.