A Marketing Research curriculum rebuilt from live analytics to AI-assisted code

The School of Business at the American University in Dubai invited Trustline Digital to help modernize approximately 20% of MKTG 361 — Marketing Research. The engagement joined faculty ownership of the course with an industry view of how analytics and AI-assisted technical work are now carried out.

The brief was not to replace the course. Its research foundations, five course learning outcomes, grading weights and programme-learning-outcome mapping all needed to remain intact.

The course was strong on research fundamentals, but students did not work with a live digital analytics platform. Technical labs were entirely menu-driven in SPSS, while the AI policy addressed text generation without teaching students how to direct, inspect and take responsibility for AI-assisted code.

Simply adding a Python week or an AI demonstration would have created another disconnected tool. The curriculum needed a sequence in which each new capability built on evidence students had already handled.

The most useful unit of design was not the lecture; it was the artifact. If students define a problem, write a specification, direct an implementation, review the result, test it and document what they own, their judgment remains visible even when an AI agent helps produce code.

One real GA4 dataset could connect those artifacts. Students would first learn to read the signal, then compare explanations, then specify a technical response and finally review and defend what had been built.

Two viable redesigns were developed and walked through with the faculty lead: modular additions that could be inserted independently, and an integrated arc that carried one dataset across four consecutive weeks. The department selected the integrated option.

The redesign was deliberately additive. Existing learning outcomes, assessment weights and programme mapping were preserved, while the group project and weeks 8–11 were recalendared around a more coherent applied journey.

Week one introduces GA4 funnel structure, traffic acquisition and key-event rates. Week two moves into attribution modelling and conversion paths, with SPSS retained as a comparison rather than discarded. Week three turns the research problem into a technical specification and a guided AI-agent implementation. Week four covers code review, debugging, testing and client-ready documentation.

The supporting workflow makes six responsibilities explicit: define the problem, write the specification, direct the implementation, review and debug, test, and document and own. Assessment can therefore focus on reasoning and evidence rather than on whether a student typed every line unaided.

The delivery package included two branded proposals, a stakeholder walkthrough, the complete lesson-planned syllabus, Fall 2026 recalendaring, a revised group-project brief and the applied teaching materials required for the modernized sequence.

The final syllabus and project brief were approved for Fall 2026: four weeks modernized, with no changes to the existing CLO/PLO mapping. Dedicated teaching-assistant support is planned from the second offering onward.

The result is a course that keeps its academic foundation while giving students a defensible way to work with live analytics, attribution, Python and AI-assisted implementation as one connected research practice.