Bridging the AI skills gap
A survey of business journal editors on graduate preparedness, AI adoption, and governance. Sole author.
Submitted to the American Journal of Undergraduate Research
- Period
- February to May 2026
- Role
- Sole author. Faculty mentor Dr Chong Huang, not a co-author
- Approval
- IRB2026-70, exempt, University of the Pacific
- Tools
- JMP Pro 17, Python, matplotlib, Power BI
170
complete responses
1,021
editorial board members in the frame
22
item instrument
The question
Business schools are adding AI to their curricula quickly. The people who referee the field’s published research are well placed to say whether graduates arrive prepared, and they are rarely asked. The study asks them directly, and separately asks what governance their own institutions have put in place.
Method
A 22-item instrument, fielded in Google Forms between February and March 2026 to a sampling frame of 1,021 editorial board members at ABDC A and A*-ranked business and management journals. IRB approval was obtained before any participant contact.
Analysis ran in JMP Pro 17 and Python. Descriptive statistics with distributional diagnostics, importance-performance analysis with paired comparison, internal-consistency estimation, exploratory Pearson and Spearman correlations with assumption checks, multiple linear regression, and descriptive k-means clustering. Figures in matplotlib, dashboards in Power BI.
| Measure | Value |
|---|---|
| Sampling frame | 1,021 editorial board members |
| Complete responses | 170 |
| Response rate | 16.7% |
| Cronbach’s α, seven-item risk index | .937 |
| Spearman-Brown, three two-item composites | .881 / .758 / .724 |
| Minimum detectable correlation at N=170 | |r| ≥ 0.21, α=.05, power=.80 |
PDF · 141 KB
The 22 items as they were shown to respondents, and a closing page from the manuscript’s methods that was not.
What it found
AI literacy was nominated a top-three curriculum gap by 66.5% of respondents. Only 10.6% called their institution’s AI guidelines clear and useful, which is the gap between adopting a technology and governing it.
Importance-performance analysis put all nine competencies in the high-importance, low-preparedness quadrant, with gaps ranging from 1.23 to 1.51. There was no competency the respondents rated as adequately covered.
A three-predictor regression explained 38.5% of the variance in perceived curriculum-employer alignment, an R² of .385.
| Result | Value |
|---|---|
| AI literacy a top-three curriculum gap | 66.5% of respondents |
| Institutional AI guidelines clear and useful | 10.6% |
| Competencies in the high-importance, low-preparedness quadrant | All nine |
| Importance-performance gap range | 1.23 to 1.51 |
| Regression on curriculum-employer alignment | R² = .385, three predictors |
Limitations
A 16.7% response rate on a specialist frame invites self-selection: editors who care about AI in the curriculum are more likely to answer a survey about it. The design is cross-sectional, so nothing here establishes direction.
The analysis stops where the sample stops supporting it. No factor analysis and no structural equation modelling were run, and the manuscript says so rather than leaving a reader to assume they were considered and passed.
The point
Reporting the analysis the sample supports, rather than the analysis that would sound most complete, is the whole of the method here.
Not published
The response data
Not published
Not published, and the reason is specific rather than cautious. 44 of the 84 combinations of discipline, years teaching and level are unique to a single respondent, the sampling frame is a published list of editorial boards, and every respondent wrote free text, some of it naming institutional detail. Suppressing columns does not fix a frame that anyone can obtain and cells that thin.