AI & Adaptive Leadership
AI adoption asks more of us than learning a new tool. It asks us to examine how we think, what we assume, whose knowledge is represented, and who remains accountable for the outcome. Shanequa E. Moore teaches AI through an adaptive leadership lens—combining practical experimentation with reflection on ethics, evidence, equity, and professional responsibility.
Where This Work Focuses
Ethical AI and professional judgment
Helping professionals evaluate AI outputs, recognize limitations, consider bias, protect confidentiality, and retain responsibility for their decisions.
Adaptive leadership and organizational change
Helping leaders examine the habits, assumptions, values, and competing priorities that shape how their organizations adopt technology.
Faculty and educator development
Helping educators design learning experiences that build students’ reasoning, verification, reflection, and accountability when they use AI.
Responsible organizational adoption
Supporting conversations about readiness, appropriate uses, human review, staff learning, and organizational expectations.
Stay in the Driver’s Seat.
AI can support the work, but people remain responsible for its direction. The Driver’s Seat approach helps learners and professionals decide what to delegate, what to review, and what must remain a human responsibility.
The AI Authority Map
The AI Authority Map is a practical way to talk through delegation and accountability. It sorts work into three zones.
AI Can Carry
Tasks where AI can help draft, organize, or summarize, with a person still checking the result.
Human Approval Required
Work AI can help prepare, but a qualified person reviews, corrects, and approves it before it is used or shared.
Human Only
Judgments, relationships, and responsibilities that stay with people, including the decisions they are accountable for.
Where each boundary falls depends on the task, professional standards, organizational policy, and context. The map is a starting point for conversation, not a fixed rule.
How the Learning Happens
“Learning to use AI also means learning to examine our own thinking—our assumptions, evidence, judgment, intuition, and accountability.”
Adaptive learning moves between two vantage points.
The Dance Floor
Hands-on experimentation and practice, where learners try AI tools on real tasks.
The Balcony
Stepping back to observe patterns, question assumptions, and reflect on what the work is revealing.
Along the way, we look closely at training data, algorithmic bias, algorithmic justice, equity, and whose perspectives shape the AI systems we use.
Speaking & Workshops
Recent Presentations
Touro University AI Summit · October 8, 2026
“A Faculty Framework for Teaching Students to Ethically Engineer AI Without Surrendering Human Judgment”
A faculty-focused session on helping students use AI while retaining ethical reasoning, professional responsibility, and human judgment.
NAFSCE National Assembly · San Diego, 2026
“The Ethical AI: From Algorithmic Bias to Family Connection”
NASW–New York State Chapter · June 2026
“AI Tools for Rural and Seasoned Social Workers: Practical Applications and Ethical Integration”
Institute of Living, Hartford HealthCare · Virtual Grand Rounds, February 2026
“Between Harm and Healing: Racialized Mental Health Algorithms and Reparative Pathways for Black Youth”
Columbia University School of Social Work · Guest Lecturer, 2026
New York University · Guest Lecturer, 2026
Shanequa has also trained approximately 1,500 behavioral health professionals in AI ethics.
Topics for Speaking and Workshop Inquiries
The topics below are available for inquiry and can be shaped to fit your audience and goals.
Disrupting the Automation of Thought
Audience: Higher education and professional learning
Focus: Critical thinking, ethical reasoning, and accountability in AI-supported learning
Government under the Hood: Adaptive Leadership in the Age of AI
Audience: Government and public-sector leaders
Focus: Organizational assumptions, public responsibility, equity, and adaptive challenges in AI adoption
Ethical AI for Behavioral Health and Human Services
Audience: Social workers, behavioral-health professionals, and human-service organizations
Focus: Responsible uses, confidentiality, bias, verification, and the boundaries of professional judgment
Try MIA
MIA is a thinking partner that asks before it answers. It helps you do your work with AI while you stay the one doing the thinking.
Moore Success AI Lab
An AI ethics community for professionals.