AI-Enabled Transformation
Redesigning how a graduate business course on Operations Management operates in the age of AI
Overview
Johns Hopkins Carey Business School needed to solve a problem that most institutions were still pretending didn't exist: AI had already entered the graduate classroom, and the existing infrastructure — rubrics, assessments, feedback models — wasn't built for it.
I was brought in to design and deploy the systems, governance, and human infrastructure to make AI integration work at the program level. Not as an experiment. As a production deployment.
This was a first-of-kind engagement at Carey. I was the first person in this function, laying groundwork explicitly designed to scale — with the expectation that additional AI engineers, teaching staff, and technical contributors would build on the foundation in subsequent terms.
The engagement ran across two consecutive terms, serving 120+ MBA students per cohort.
Impact By the Numbers
The Problem
A top-ranked graduate program had an AI problem — and no framework to solve it.
Faculty knew students were using AI. They had no visibility into which models, at what depth, or with what level of critical engagement. Banning it was both unenforceable and strategically counterproductive in a world where AI fluency is increasingly a professional baseline for MBA graduates.
The assessment infrastructure made it worse. Rubrics designed for a pre-AI classroom measured compliance, not thinking. Multiple-choice quizzes rewarded recall. Open-ended case analyses had no consistent evaluation standard — and no scalable way to deliver quality feedback to 120+ students at once.
The core design challenge: how do you rebuild graduate assessment so that AI amplifies student reasoning rather than replacing it — and how do you make that rigorous, auditable, and scalable from day one?
That was the engagement.
The Opportunity
Most institutions were playing defense — banning AI, ignoring it, or issuing policies that lagged the reality on the ground by at least a semester. Carey had a different posture: if AI fluency was becoming a professional baseline for MBA graduates, the question wasn't whether to integrate it, but how to do it rigorously enough to be worth doing at all.
The timing created a genuine opening. HopGPT — Johns Hopkins' secure enterprise AI gateway — had just launched, giving the institution its first production-grade LLM environment for internal use. The platform was new, the API documentation was still maturing, and real-world deployment use cases were largely untested. The course needed an AI infrastructure. The platform needed a serious user. The conditions were right to build something that could outlast the term it was built for.
The Vision
The goal was never to automate grading for its own sake. The design intent was an AI autograder that students would use themselves — receiving rich, qualitative feedback that guided their reasoning, not just scored their output. The long-term architecture was explicitly agentic: lay the groundwork in year one, then deploy autonomous grading on behalf of the teaching team in subsequent terms as the platform matured.
Equally important was the governance layer. From the start, the engagement was designed to be self-sustaining — not dependent on the person who built it. That meant building infrastructure that could hold institutional memory and improve over time, not just deliver results for a single cohort.
The Approach
Three interconnected workstreams. One transformation.
Workstream 1 — AI Evaluation Architecture
The flagship deliverable was an end-to-end AI grading infrastructure for the Operations Management core course — one of the most analytically demanding courses in the MBA program.
I designed and deployed three distinct AI evaluation systems, one per case study, each built around a BLUF / Analysis / Conclusion rubric structure that assessed reasoning quality, argument construction, and the validity of operational recommendations — not just whether a student arrived at the right number.
The 3 Cases
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queuing theory, capacity analysis, bottleneck identification, staffing optimizations
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value stream mapping, Lean Six Sigma waste identification, process improvement strategy
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AnyLogic digital twin simulation modeling of Operating Room (OR) patient flows, utilization vs. patient wait time trade-offs, simulation interpretation
LLMs Deployed in HopGPT Platform
All systems were built and tested on HopGPT — Johns Hopkins' enterprise AI gateway providing institutional access to nine frontier models across three providers: OpenAI (via Azure), Anthropic (via AWS), and Meta (via AWS).
I benchmarked performance across all available models, iterated on prompt architecture against Lean Six Sigma and structured reasoning methodologies, and worked directly with the JHU IT department to surface API gaps and documentation improvements. This was one of the earliest production deployments on the platform — I was simultaneously builder, tester, and informal product collaborator.
The result: feedback turnaround reduced by two days per cohort, with measurable improvement in feedback consistency and analytical depth across student submissions.
Workstream 2 — Governance:
The Co-Creation Framework (Term 2)
The evaluation infrastructure solved the grading problem. The governance question was more complex: how do you ensure students are genuinely developing as thinkers, rather than outsourcing cognition to a model?
In term 2, I co-developed the Co-Creation Framework with the course professor — a governance model that repositioned AI as a structured collaborator rather than an answer engine. The framework required students to submit Chain of Thought Transcripts alongside their deliverables: documentation of their prompting process, iteration decisions, and how AI shaped their reasoning path to the final output.
This made thinking visible and auditable. It preserved critical reasoning as the core of the learning process. And it created a precedent for responsible, transparent human-AI collaboration in graduate education that extended beyond the original course.
Within the first term of deployment, the framework was shared internally across Hopkins departments and externally with faculty at peer institutions — adoption driven by faculty interest, not mandate. The design was intentionally modular, built to be layered on by future contributors across subsequent terms.
Workstream 3 — Assessment Redesign
Alongside the grading infrastructure, I identified a structural weakness in the course's weekly assessment model: multiple-choice quizzes were generating compliance, not engagement. I designed and coded an interactive quiz prototype — incorporating real-time feedback mechanics — before being asked to, and brought it to the teaching team as a recommendation.
That prototype opened a larger strategic conversation about replacing the format entirely. I led the vendor evaluation for an AI-moderated discussion platform built for graduate education — attending product demos, reviewing pedagogical fit against course objectives, contributing structured feedback, and informing the decision to adopt.
The contract was signed. Full deployment was scheduled for the term following my engagement — a direct outcome of the groundwork laid during my time in the role.
External link to Breakout Learning partners—AI moderated discussion platform, in teaching team’s attempt to replace rote quiz formats to critically thinking methods for graduate students.
- 2 Days
Grading turnaround reduction per case study per cohort
3
Number of AI evaluation systems designed & deployed
30%
Increase in course evaluation scores overall
2 Terms
First deployment; iteration & governance frameworks
Outcomes
Reduced grading turnaround by 2 days across 120+ students per cohort over two consecutive terms
Drove a 30% increase in course evaluation scores — with direct student feedback citing enhanced clarity and analytical depth in feedback quality
Co-Creation Framework adopted beyond the original course within the first term of deployment — shared across Hopkins departments and with faculty at peer institutions
Contributed directly to JHU IT's HopGPT platform development as one of its earliest institutional production users — surfacing API gaps, validating model behavior, and informing platform documentation
Delivered a scalable, extensible AI infrastructure designed for multi-term growth — not a one-time implementation
Impact
Adoption: Professor adopted co-creation framework across entire Operations Management curriculum
Transparency: 120+ students per term now openly use AI with required documentation, establishing accountability
Institutional Impact: Created replicable model for responsible AI integration that other courses are now exploring
Learning Outcomes: Students demonstrate authentic critical thinking through documented AI collaboration rather than superficial quiz performance
Cultural Shift: Established precedent that AI should augment human capability, not replace judgment—positioning Johns Hopkins as leader in AI-era education
Who We Are
Every decision we make is shaped by a clear sense of purpose. Our journey has been anything but ordinary. Through every step, we've focused on staying true to our values and making space for thoughtful, lasting work.
We believe in simple ideas, strong relationships, and lasting impact. What began as a passion project has evolved into something more. We’re proud of where we’ve been and even more excited for what’s ahead.
Mention
Closing
The environment was academic. The discipline was production-grade. What this engagement required is what enterprise AI transformation always requires: equal investment in system architecture, governance design, and the human layer surrounding both — built to outlast the person who built it.
Prior to this role, I co-designed a multi-agent AI platform recognized at the 2025 INFORMS International Meeting in Singapore — cited in: Domenge, J., Pandey, R., Simmonds III, M., Warren, G. & Xu, M. (2025, July 20–23). From Code to Confidence: How Students Built a Multi-Agent AI to Navigate Job Interviews. INFORMS International Meeting, Singapore.
Johns Hopkins University Information Technology. (2026). HopGPT: Secure access to large language models. Johns Hopkins University. https://hopgpt.it.jh.edu/
