An Integrated
Decision-Making
Lab Method

for Experiential Learning in
Marketing Strategy Courses

Dr. Jose Mendoza

mma.jose-mendoza.com

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.

Flexibility

Fit the teaching sequence

I needed activities I could use independently and fit to the pace and objectives of each session.

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.

AI is part of the learning design. Embedded AI learning coaches support feedback and interpretation; an AI counterpart supports negotiation practice.

04 / A custom case study

Vela Skincare

An original teaching case, like PITH, built around a strategic investment decision.

05 / 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.

06 / 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

Does the revision improve the judgment, or simply produce a preferred answer?

Ask students to justify each revision, compare the sensitivity results, and explain what evidence would change their recommendation.

07 / Student-reported learning

Evidence of how students
describe their reasoning.

08 / 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

09 / 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