Students are using AI. The research asks whether they can stand behind the work.
Since the 2022-23 generative-AI inflection point, the research program has followed how business students move from awareness to routine use. The studies examine whether adoption develops alongside verification, disclosure, supervision, trust calibration, and explainable professional judgment.
A 43.7-point gap separates belief in human review from demonstrated supervision readiness.
In the Spring 2026 sample, 90.8% said they knew when human review was needed. Only 47.1% met the stricter ready-supervisor screen. The result shifts attention from whether students endorse responsible AI to whether they can apply approval boundaries and oversight in practice.
Self-reported readiness often weakens when students must perform the practice.
The research repeatedly finds transfer gaps between what students believe, what they say they can do, and what appears in scenario-based or open-ended work. Two named patterns help explain the problem.
The Disclosure Translation Gap
Many students know AI use should be acknowledged. Far fewer write a disclosure that identifies the tool, explains its role, describes verification, and makes responsibility clear.
The Confidence Paradox
Confidence provides only a weak signal of demonstrated AI judgment. Course exposure appears to improve use, practice breadth, and disclosure readiness faster than scenario-based performance.
Agentic use is growing faster than oversight routines.
Students are beginning to delegate multi-step work while approval boundaries and audit habits remain uneven. The practical challenge is helping future professionals define where human review must occur and what evidence should remain after the workflow runs.
use multi-step or agentic AI weekly or more
Advanced use is no longer a distant scenario. A substantial share of students already delegate work across multiple steps.
of weekly users do not consistently require human approval before external action
The practical issue is whether students can define and enforce the boundary for human approval.
Students create with AI far more often than they analyze with it.
Early year-three benchmarks show frequent use for drafting, images, and ideation, with much thinner use for data cleaning, coding, and analytical workflows. This Depth Gap matters because high-stakes business work requires verification, context, and defensible reasoning.
- Roughly 70% reported frequent creation-oriented use.
- About 10% reported frequent analytical-workflow use.
- Practice breadth appears to be a better fluency signal than frequency alone.
Creation
Drafting, images, ideation, and low-stakes production.
Analysis
Coding, data work, multi-step analysis, and decision support.
Early benchmark from the year-three deep-dive sample. The finding is directional and is being extended in the continuing research program.
One evolving question, examined through several studies.
The work connects student readiness, trust, privacy, course design, assessment evidence, and human-centered professional practice. Each stream contributes a different view of what responsible AI fluency requires.
Bridging the AI Gap
Early studies documented the gap between awareness, comfort, career expectations, and students’ demand for more integrated AI education.
Belief to Behavior
Follow-up work examined Believers, Adopters, and Skeptics, including how ethical readiness, trust, privacy concerns, and policy ambiguity shape movement from interest to use.
The Reluctant Architect
Students intend to build with AI while withholding personal data. Trust drives use more strongly than usefulness, and confidence is an imperfect proxy for demonstrated proficiency.
From Novelty to Utility
As AI becomes routine infrastructure, the research tracks driver, depth, and risk shifts in how students use and supervise increasingly capable systems.
Beyond AI Disclosure
The governance-as-pedagogy work moves beyond declarations toward process evidence: disclose, document, verify, and defend.
The Seven Anchors
A human-centered marketing capability framework for preserving judgment, trust, relationship, creativity, purpose, and customer meaning as production becomes abundant.
Move the work between scholarship, teaching, and institutional practice.
The program includes conference papers, peer-reviewed manuscripts, applied reports, faculty workshops, and institutional frameworks. Recognition includes Best Paper honors at the Marketing Educators’ Association and presentations across two years at the Academy of Marketing Science.
Use the findings to ask stronger questions about capability and evidence.
The Diagnostic and implementation pathways translate the research into decisions a business school can test, document, and improve.