AI Workforce Transformation Initiative

 

Preparing graduates who can design, lead, evaluate, and govern the human-AI systems that are reshaping work, learning, and organizations.

Overview

The AI Workforce Transformation Initiative is the School's investment in redesigning how we prepare students for workplaces where human expertise and AI systems work side by side. It funds faculty to rebuild courses and programs, and it asks each funded project to generate evidence about what actually works. This page gathers what the initiative produces: the projects, the tools, the scholarship, and the resources other educators and trainers can use.

What the Initiative Funds

Conceptual illustration of AI integration throughout a university course, with interconnected icons representing AI, collaboration, technology, analytics, and learning across an academic environment.

Track A: Course Integration

Redesign of an individual course so that workforce-relevant AI competencies run through it rather than sitting in a single week.

Conceptual illustration of systems-level educational transformation, with interconnected icons representing courses, programs, competencies, assessment, collaboration, and student achievement across a university system.

Track B: Educational Transformation

Systems-level work across multiple courses, programs, or degree levels, including shared competency frameworks and aligned assessment.

Proposals are evaluated on fit with these areas as much as on individual course need. They're not exclusive categories — most funded work touches two or three — but they signal where the School wants to build depth.

Organizational Transformation
Leadership in AI-Mediated Environments
Human Performance Improvement
Human-AI Collaboration
Workforce Analytics & Decision Making
AI Literacy & Evaluation
Ethical & Responsible AI Use
Doctoral Preparation for AI-Enabled Research
AI-enabled Learning Systems

 

Funded projects and the foundational work they build on. Each entry follows the same template so the set reads as a program rather than a list of separate efforts.

Funded Projects

FACULTY Oliver S. Crocco, Associate Professor
AWARD Track A, Course Integration, $9,820
PERIOD June 2026 to May 2027
COURSE LHRD 7923, Advanced Mixed Methods Research (doctoral)
COMPETENCIES Evaluating AI-generated research outputs; verifying results across tools; recognizing bias, fabrication, and privacy risks; disclosing AI use to professional standards; designing mixed methods research that incorporates AI responsibly
ASSESSMENT Pre- and post-course competency self-assessment; a rubric comparison on the capstone proposal scored on a new AI-integration dimension; structured analysis of student reflections through a transformative learning lens
HUMAN SUBJECTS LSU IRB exempt approval IRBAM-26-0777, approved 26 August 2026
STATUS Redesign complete; pilot running Fall 2026; assessment and resource compilation spring 2027
OUTPUTS SO FAR Inter-rater reliability calculator (see Tools); reusable disclosure-based syllabus AI policy; verification and disclosure exercises

$150,000

Committed by the School

Funding

Work marked with the AWTI tag was supported, in whole or in part, by the AI Workforce Transformation Initiative of the School of Leadership & Human Resource Development at Louisiana State University.

Suggested acknowledgment for awardees to reuse in papers, software, and slides: "This work was supported by an award from the AI Workforce Transformation Initiative of the School of Leadership & Human Resource Development at Louisiana State University."

Software and instruments built through the School's AI work, released openly so that colleagues, students, and practitioners can use and cite them. Each tool carries a license, a citation, and a plain statement of what it does not do.

Web tool · No installation · Runs in the browser

Computes Krippendorff's alpha (nominal, ordinal, interval) with bootstrap confidence intervals, Fleiss' kappa, and pairwise Cohen's kappa for any number of raters, items, and codes. Built for qualitative coding teams and methods courses.

Audience

Researchers, doctoral students, coding teams
Verification

Checked against R and Python implementations on 240 random coding grids
Access

olivercrocco.com/irr-calculator
License

MIT

Command-line tool · Python

Flags citation-bearing claims in a manuscript whose cited source may not support what the author says, with the retrieved passage shown as evidence. Runs entirely on a local model so confidential manuscripts never leave the machine.

Audience

Authors, peer reviewers, editors, dissertation committees
Honest limit

A triage aid, never a verdict. "Not found" means not found in the passages retrieved.
Access

pip install claim-verifier
Citation

DOI 10.5281/zenodo.21985256

Command-line tool · Python

Checks that the references in a manuscript exist and match their metadata, catching fabricated and malformed citations before submission.

Audience

Authors, reviewers, editors
Citation

Crocco (2026), DOI 10.5281/zenodo.20508955

Command-line and web versions

Suggests matched, conflict-free, career-stage-balanced reviewer panels from the open OpenAlex database. Sends only generic topic terms, never a manuscript's title, abstract, or authors.

Audience

Editors and associate editors
Citation

Crocco (2026), DOI 10.5281/zenodo.20448662

Reproducibility harness

Reproduces a statistical result in more than one environment and reports any discrepancy, the practice taught in LHRD 7923 as the check on AI-assisted analysis.

Audience

Quantitative and mixed methods researchers
Citation

Crocco (2026), DOI 10.5281/zenodo.20448660

Peer-reviewed scholarship, practitioner writing, and conference work on AI in workforce development, learning, and organizations by SLHRD faculty and students.

Crocco, O. S., Mohd Rasdi, R., & Garavan, T. N. (2026). Responsible AI in non-empirical research: Guidance for authors, reviewers, and editors in HRD. Human Resource Development Review.
https://doi.org/10.1177/15344843261445531

Standards for disclosing and evaluating AI use in conceptual scholarship.

Developing doctoral students' competency in verifiable AI-assisted research: A scholarship of teaching and learning study in Advanced Mixed Methods Research (LHRD 7923). IN PROGRESS.

Follows the LHRD 7923 redesign through the Fall 2026 pilot, analyzing how students move from disorientation toward a critical, integrated stance on AI in research. LSU IRB exempt approval IRBAM-26-0777.

What the School offers, and what it gives away. Materials developed through the initiative are compiled for reuse across the College and beyond, at the undergraduate, master's, and doctoral levels.


AWTI Track A
Workshop: AI-assisted scholarly work

Internal · School and College faculty · Spring 2027

A working session on verifiable AI use in research: where AI assists analysis, where it misleads, how to verify a result across tools, and how to disclose AI use to current publisher standards. Drawn from the LHRD 7923 redesign so colleagues can adapt it for their own teaching and research.
AWTI Track A
Reusable syllabus AI policy

Download · Adapt for any course

A single disclosure-based course policy that replaces the conflicting permissive and prohibitive statements common in syllabi, written so that it models the practice it asks students to follow.
AWTI Track A
Course materials from LHRD 7923

Available after the Fall 2026 pilot

Verification and disclosure exercises, the AI-integration competency rubric, and the pre- and post-course competency instrument, compiled for use at any level.

Course

Level

What Students Build

LHRD 7923, Advanced Mixed Methods Research

Doctoral

Judgment over AI-assisted analysis: verification across tools, disclosure to professional standards, and a full mixed methods study design on a workforce question

A prospective student reading this table should be able to see a path through the program, not just a set of courses that mention AI.

FOUNDATIONAL WORK

Petra A. Robinson

Interim Director, SLHRD & Initiative Lead

Established the AI Workforce Transformation Initiative and directs its review, funding, and assessment.

Sunyoung Park

Faculty AI Development & Training

Work supporting faculty AI development and training initiatives, expanding opportunities for instructional innovation and building faculty capacity around emerging technologies.

Corina Caraccioli

AI Systems & Curriculum

Expertise in AI systems that contributed to the development of the School's first AI-focused course.