Teaching
Eight distinct courses across the MBA, the MS in Data Science and Analytics, and the undergraduate Management curriculum at Clemson. The current focus is rebuilding the MBA core to treat AI as the through-line of the course rather than a topic added at the end, and to give students experience supervising AI coding agents rather than only reading about them.
● What I am building right now
Four MBA courses are being redesigned together so that AI is the through-line, not the conclusion. The theoretical foundation for the approach is a current paper that argues generative AI restructures the epistemic sequence of learning itself: students learn by executing with an agent, not by reading first and applying later.
The MBA Information Systems course is being rebuilt around the claim that automate moves create the data that powers AI. Every chapter shows the link: smart meters generate the data that anomaly detection learns from; product registries generate the data that predictive models learn from. AI is not a section students can skip; it is the explanation of why the framework still holds.
The long-term MBA 8610 build project asks students to use an LLM coding agent to build a working Node.js system in three phases: automate a real workflow, informate the data it generates, transform the business case those insights enable. Students learn the A-I-T framework by living each level in sequence, not by reading about it. The pedagogy mirrors a parallel argument in a current paper.
MBA 8180 in the Fall sets the conceptual base for business intelligence and analytics; MBA 8990 in the Spring extends the same cohort, with the same teams, into Python and AI-assisted system building. The Fall capstone is designed to scaffold the Spring project. Students leave the sequence able to supervise AI-written code as managers, not just read about it.
DSA 8670 has been redesigned around the Plan, Direct, Verify, Deliver loop for a semester-long team client engagement. Students explicitly practice the supervisory skills generative AI makes load-bearing: turning ambiguous client asks into a project context report, writing prompts that brief an agent rather than instruct it, verifying AI-generated analyses against acceptance criteria, and shipping a client-facing deliverable. AI is required on the team project and forbidden on individual assignments so students experience both modes.
MBA 8080 teaches the craft in Tableau (encoding choices, the “Most Not Wrong” framing of designer judgment, a consultant-style Business Question for ambiguous asks) and then pivots to directing generative AI to produce the actual charts. The bet is that judgment is the durable skill; the tool is whatever ships the work fastest. Students leave able to evaluate a chart someone else (or something else) made, not just to make one themselves.
Signature projects
An MS DSA cohort project that started as a routine analytics deliverable and ended up feeding directly into state-level policy work on abandoned oil wells in Pennsylvania and Ohio. The cohort worked across nine states of well-location and ownership data, built decision support for capping prioritization, and presented to operators and policy staff. The course is being redesigned to make project-delivery experiences of this shape a more central feature of the program.
An end-to-end economic impact analysis of Greenville Theatre across four seasons, built as a reproducible pipeline (geocoded patron data, distance-tiered visitor spending, IMPLAN multipliers from the South Carolina Arts Commission). The report quantified $11.9 million in total local activity, $4.43 of economic activity per $1 of operating expense, and 119.5 full-time-equivalent jobs supported, with a sensitivity analysis on multiplier, audience scale, and local radius. Delivered as a print-ready PDF for the theatre's 100th season.
Course portfolio
MBA
Rebuilding the core IS course from the ground up as a Canvas-native, AI-integrated textbook. Thirteen modules, AI woven into every chapter as the through-line rather than a tacked-on section. Companion long-term build project asks students to use an LLM coding agent to build a Node.js system that automates a real workflow, informate the data it generates, and propose a transform business case.
Tool-agnostic visualization theory paired with hands-on Tableau practice. Students first learn the craft (encoding choices, the “Most Not Wrong” framing for designer judgment, the consultant-style Business Question for ambiguous asks) in Tableau, then pivot to directing generative AI to produce the actual charts. The pedagogical claim is that the judgment is the durable skill; the tool is whatever does the work fastest. Fall 2026 redesign in progress, aiming for Quality Matters certification.
Fall 2026 rebuild. The Fall course establishes the conceptual base; teams formed here persist into Spring’s MBA 8990, where the same students take the analytics they learned in the Fall and ship working systems built with AI coding agents.
Intensive weekend format. The Spring continuation of the MBA 8180 cohort: same teams, deeper build. Students leave able to read, write, and supervise Python written by AI coding agents, with an Ames Housing capstone that requires defensible analytical reasoning behind every model decision.
MS DSA
Client-partnered analytics delivery, redesigned for Fall 2026 around directing AI to do the work. Students move through four phases on a real client engagement: Plan, Direct, Verify, Deliver. AI is required on the team project (Codex CLI against a Palmetto-hosted open model) and forbidden on individual assignments, so students learn both modes deliberately. Worked case study, four phase-specific exemplars, and behavioral rubrics scaffold the experience.
Intensive entry course for the MS DSA program (and now the MS Sports Business and Analytics program). Currently the pilot for a multi-year Quality Matters certification effort across the course portfolio.
Undergraduate
Undergraduate analytics with simulation-based decision projects that put students in dynamic, ambiguous business scenarios rather than clean textbook problems.
Undergraduate IS, refreshed each offering with current industry practice. Recent rooms have featured visiting practitioners discussing LLMs, data lakes, and visualization in their daily work.
Teaching philosophy
Students learn best when engaged and challenged in ways that mirror professional practice. Classroom time mixes short lectures with hands-on coding or visualization work and collaborative problem solving. Assessments are varied so students with different strengths can demonstrate mastery, and rigor is matched to course level, with undergraduates expected to demonstrate basic analytical reasoning and graduate students held to professional standards of technical execution and communication. Flexibility extends to course design as well as to individual students: assignments offer multiple paths to mastery, and life sometimes interrupts plans.
Evidence
MBA Teacher of the Year, Clemson University, 2023 to 2024
Thirty-three sections across eight distinct course preparations since 2019, peaking at six preps and eight sections in a single academic year.
Cross-course average 4.08, most-recent-year average 4.29 (on a 5-point scale).
Pre and post tests across the 2024 to 2025 academic year showed gains of 20 to 30 percentage points (for example, MBA 8990 from 58 to 86; MGT 3180 from 54 to 84).
Visiting Professor at the Indian School of Business, 2014 to 2024, in their Advanced Management Program in Business Analytics.