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Graduate Instruction · Workshops · Conferences

Teaching &
Speaking

Teaching and speaking grounded in rigorous analysis, applied practice, human consequences, and the responsibility to make complex systems understandable without making them simplistic.

Pedagogical Statement

Education as applied, accountable practice.

Graduate instruction in AI, data systems, and governance grounded in liberatory teaching, funds of knowledge, and holistic learning, connecting rigor to real-world practice and critical inquiry.

My teaching is grounded in the view that education is an applied system, one that can either reinforce existing structures or actively interrogate and improve them. I approach teaching as an extension of research: structured, practice-oriented, and focused on preparing students to operate in complex, real-world environments.

This approach is informed by bell hooks's framing of education as a practice of freedom. In my courses, the classroom is not a passive delivery mechanism but a working environment where students are expected to engage, question, and construct defensible positions.

I draw on the "funds of knowledge" framework developed by Luis C. Moll and colleagues. Graduate students bring significant domain experience; my role is to surface and integrate that knowledge into formal frameworks, connecting lived expertise to academic models, systems design, and governance practice.

My pedagogy also reflects Laura I. Rendón's sentipensante framework, recognizing that effective learning in AI and data systems requires both analytical rigor and awareness of human impact. Technical decisions do not occur in isolation; they operate within organizational, ethical, and societal contexts that must be explicitly addressed.

Influences: bell hooks, Teaching to Transgress; Moll, Amanti, Neff & Gonzalez, "Funds of Knowledge for Teaching"; Laura I. Rendón, Sentipensante Pedagogy.

Graduate Instruction

Courses shaped around real decisions.

Coursework connects formal technical methods with governance, organizational context, human impact, and the evidence required to defend a decision after implementation.

01 · Artificial Intelligence

AI Governance and Ethics

Responsible adoption, model risk, accountability, policy architecture, documentation, and the limits of purely technical evaluation.

02 · Systems

Systems Analysis and Design

Requirements, stakeholder analysis, process models, systems thinking, architectural decisions, and operational consequences.

03 · Software

Software Engineering

Design discipline, quality, lifecycle decisions, team accountability, risk, and the relationship between technical debt and organizational behavior.

04 · Requirements

Requirements Engineering

Translating ambiguous business needs into testable, traceable, and defensible requirements that survive implementation pressure.

05 · Capstone

Senior Design Project

Applied team delivery, project governance, decision documentation, stakeholder communication, and professional accountability.

06 · Enterprise Practice

Case-Based Learning

Real organizational scenarios that require students to balance technical performance, governance, privacy, security, and implementation constraints.

Speaking & Engagements

Workshops and conversations built for action.

Engagements are designed around the audience's actual decisions, whether that means helping a leadership team establish governance, preparing practitioners to evaluate vendors, or challenging a conference audience to rethink what their metrics really prove.

Client Workshop

Designing an AI Governance Operating Model

A facilitated working session for executive, legal, risk, data, and technology leaders building a governance structure that can function beyond policy documents.

  • Decision rights and escalation paths
  • Roles across business, legal, risk, and technology
  • AI inventory and intake design
  • Controls, evidence, and accountability
  • Implementation roadmap
Conference Workshop

From Compliance Theater to Defensible Evidence

An interactive conference workshop examining why policies, checklists, and vendor claims often fail under scrutiny, and what organizations need instead.

  • Auditability versus audit
  • Conformance versus defensible posture
  • Evidence architecture
  • Traceability and ownership
  • Practical exercises using enterprise scenarios
Leadership Briefing

What Your AI Metrics Do Not Tell You

A briefing for senior leaders on the difference between capability, risk, compliance, and maturity, and why collapsing them into one score creates false confidence.

  • The Four Lenses framework
  • Benchmark versus deployment capability
  • Governance maturity and regression
  • Risk interpretation
  • Questions leaders should ask before scaling
Client Workshop

Procurement-Ready AI: Building the Vendor Evidence Package

A practical workshop for AI vendors and product teams preparing for enterprise procurement and institutional review.

  • Security, privacy, governance, and model documentation
  • Evidence expected by enterprise buyers
  • Common procurement failure points
  • Audit readiness
  • Creating a reusable evidence package
Conference Session

Capable on Paper: Why AI Performance Erodes in Deployment

A research-based presentation on deployment-context capability and the five pathways through which performance demonstrated in evaluation can degrade in operational use.

  • Context shift
  • Data and workflow mismatch
  • Human interaction effects
  • Operational constraints
  • Governance implications
Executive Roundtable

The Adult in the Machine

A facilitated discussion on the organizational responsibilities that remain human even when systems become more autonomous, capable, and embedded in decision-making.

  • Human accountability
  • Operational judgment
  • Governance under uncertainty
  • Role clarity
  • Responsible scaling
Speaking Topics

Themes for conferences, panels, workshops, and media.

Each engagement can be delivered as a keynote, conference session, executive briefing, working session, panel contribution, or multi-part workshop.

AI Governance That Actually Operates

Moving from policy statements to roles, decisions, controls, escalation, evidence, and measurable practice.

The Four Lenses of AI Measurement

Separating capability, risk, compliance, and maturity so organizations stop treating unlike constructs as interchangeable.

Responsible Enterprise AI Adoption

Balancing experimentation, value, governance, privacy, security, data quality, and organizational accountability.

Procurement and Vendor Evidence

What enterprise buyers need to approve AI products and how vendors can prepare credible, reusable evidence.

Data Governance as an Operating Model

Turning stewardship, metadata, classification, lineage, and policy into daily operational practice.

Teaching Responsible Technical Practice

Using enterprise case studies, lived experience, and human consequences to prepare students for complex systems work.

Invite JM to teach, speak, or facilitate.

For conference sessions, workshops, executive briefings, client education, faculty conversations, media appearances, or custom engagements, contact The Blue Narwhal.

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JM Wofford Teaching and Speaking

JM Wofford is a graduate computer science instructor, AI governance practitioner, author, workshop facilitator, and conference speaker. She teaches artificial intelligence, systems analysis and design, requirements engineering, software engineering, senior design, and responsible enterprise technology practice.

Speaking and workshop topics include AI governance, AI measurement, capability, risk, compliance, maturity, procurement readiness, enterprise evidence, data governance, responsible adoption, and human accountability.