Where talent, trust and technology converge.
Building the skills, leadership, and communities needed to scale trustworthy AI and drive meaningful impact.
AI Opportunity for Everyone.
Empowering individuals, leaders, and communities with the knowledge, skills, and connections to thrive in the AI era. We bridge the AI capability gap, expand access to opportunities, and ensure everyone can participate in and benefit from responsible AI innovation.
Evidence that turns responsible AI into practice.
The Institute advances research and evidence-informed practices that help individuals and organizations develop, demonstrate, and apply responsible AI capabilities.
CRAFT is a benchmark initiative connecting education and training providers with employer expectations to strengthen the quality, relevance, and recognition of responsible AI credentials and workforce capabilities. As a national comparative intelligence service, it links provider curriculum design and outcomes with employer hiring and performance data to inform employers, learners, educators, and policymakers — not to function as another credential registry.
Take the CRAFT survey →EdAIArk is an open evidence hub connecting research, practical applications, and documented impact to advance responsible AI in education and the workforce. It is the Institute’s public evidence and open educational resources (OER) layer, documenting responsible and ethical AI leadership, skills, and practices.
Visit EdAIArk →CRAIL offers structured leadership development for mid- and senior-level leaders, aligned to responsible AI governance and practice. Participants build the capability to lead responsible AI adoption across their organizations and earn verified competency credentials. CRAIL 2.0 sector cohorts are extending the program to new industries and under-resourced sectors.
Learn more about CRAIL →Three pathways. One lifelong learning journey.
Open a pathway to see its planned courses in sequence, then open any course for its introduction and full module series.
Professional learning opportunities in responsible AI leadership, governance, and workforce transformation, subject to confirmed offerings and delivery plans.
Notify me when courses open →Pathway 1 · ReadinessAI FluencyUnderstanding AI and using AI tools effectively, through workflows, use cases, prototyping, RAG and MCP. For learners, students, professionals, educators, managers and entrepreneurs.10 courses ＋
AI Fundamentals: Past, Present, and Future7 modules ＋
A grounding in AI's history, current landscape, and future trajectory, helping learners understand where AI came from and where it is headed.
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- The history of AI: from early automation to machine learning
- The rise of deep learning and foundation models
- The current AI landscape
- Generative AI, agentic AI, multimodal AI, and physical AI
- AI opportunities and limitations
- Future trends in AI and collaborative intelligence
- How AI is changing work, learning, business, and society
Generative AI Fundamentals7 modules ＋
An introduction to how generative AI works and how to use it safely, covering foundation models, multimodal generation, and their limitations.
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- What is generative AI?
- Large language models and foundation models
- Text, image, audio, video, and multimodal generation
- AI assistants and AI agents
- Hallucinations, limitations, and reliability
- Human review and verification
- Privacy, confidentiality, and responsible use
AI Tools Course Series21 modules · 5 tracks ＋
Hands-on training across the leading AI platforms — OpenAI, Claude, Grok, Google, and AWS — so learners can choose and use the right tool for the task.
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- Using ChatGPT effectively
- Building prompts and custom workflows
- Using AI for writing, research, analysis, coding, and productivity
- Understanding assistants, agents, and integrations
- Responsible use and data protection
- Using Claude for writing, analysis, research, and document review
- Long-context reasoning and synthesis
- Team productivity workflows
- Safety and responsible use
- Using Grok for research, ideation, and real-time information workflows
- Understanding strengths and limitations
- Use cases for communication, analysis, and productivity
- Gemini and Google Workspace AI use cases
- AI for documents, spreadsheets, slides, meetings, and search
- AI-enabled collaboration and productivity
- Responsible use in enterprise and education settings
- Introduction to AWS AI and machine learning services
- Amazon Bedrock and foundation model use cases
- AI for enterprise applications
- Cloud-based AI deployment considerations
- Security, privacy, and governance in cloud AI
Data Fundamentals for AI8 modules ＋
Covers the role of data in AI systems, from data quality and bias to privacy and governance, so learners can evaluate AI outputs critically.
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- Data basics for AI
- Structured and unstructured data
- Data quality, bias, and representation
- Data privacy and security
- Data labeling and training data
- Data governance
- Using data for decision-making
- Understanding outputs, confidence, and uncertainty
Prompt Engineering and AI Workflow Design7 modules ＋
Teaches effective communication with AI systems and how to design repeatable, human-in-the-loop workflows for daily work.
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- Prompt engineering fundamentals
- Role, context, task, format, and constraints
- Iterative prompting and refinement
- Research, writing, analysis, and summarization workflows
- AI-assisted decision support
- Human-in-the-loop review
- Designing repeatable AI workflows for teams and organizations
Human-Centered Design in Practice7 modules ＋
Focuses on designing AI solutions around real human needs, behaviors, and outcomes, including inclusive design and user testing.
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- Human-centered AI principles
- User research and stakeholder mapping
- Problem discovery and needs assessment
- Journey mapping and workflow analysis
- Inclusive design and accessibility
- Human oversight and trust
- Testing AI solutions with real users
AI Use Case Development7 modules ＋
Guides learners through identifying, evaluating, and prioritizing AI opportunities, culminating in a use case canvas.
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- Identifying business, learning, and community problems
- AI use case discovery
- Feasibility, value, and risk assessment
- Use case prioritization
- Stakeholder and data readiness
- Pilot design
- Success metrics and impact measurement
No-Code and Low-Code AI Prototyping6 modules ＋
Hands-on prototyping of AI assistants, chatbots, and automations without advanced coding skills.
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- No-code and low-code AI tools
- Building AI assistants and chatbots
- Workflow automation
- Connecting AI to forms, documents, spreadsheets, and knowledge bases
- Testing and improving prototypes
- Moving from prototype to pilot
Retrieval-Augmented Generation7 modules ＋
Explains how RAG improves AI reliability by grounding model responses in trusted knowledge sources.
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- What is RAG?
- Why RAG matters for enterprise AI
- Knowledge bases and document retrieval
- Embeddings and vector search
- Grounding AI responses in trusted sources
- Reducing hallucinations
- RAG use cases in education, business, legal, healthcare, and public sector settings
Model Context Protocol7 modules ＋
Explores how AI assistants connect to external tools, data, and workflows through MCP, including security and governance considerations.
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- Introduction to MCP
- Connecting AI assistants to external systems
- Tool use, context, and permissions
- AI agents and workflow orchestration
- Security and access control
- Enterprise integration use cases
- Risks and governance considerations
Pathway 2 · EffectivenessResponsible AIRisk management, ethics, regulation, security, governance, workforce, impact and sustainability. For product, legal, compliance, HR, technology, public sector and nonprofit leaders.9 courses ＋
Responsible AI Fundamentals8 modules ＋
Introduces the core principles of responsible AI — fairness, transparency, accountability, privacy, and safety — and how to apply them in practice.
Show all 8 modules ▾
- What is responsible AI?
- Fairness and bias
- Transparency and explainability
- Accountability and governance
- Privacy and data protection
- Safety and security
- Human oversight
- Sustainability and social impact
AI Ethics8 modules ＋
Examines the ethical implications of AI in society, covering human dignity, fairness, consent, misinformation, and power dynamics.
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- Ethical foundations of AI
- Human dignity and autonomy
- Fairness and justice
- Consent, privacy, and surveillance
- Misinformation and manipulation
- Power, inequality, and access
- Ethical decision-making frameworks
- Case studies in AI ethics
AI Risk Management8 modules ＋
Teaches how to identify, evaluate, and manage AI risks, from bias and privacy exposure to third-party and vendor risk.
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- AI risk taxonomy
- Risk identification and classification
- Impact assessment
- Bias and fairness assessment
- Privacy and security risks
- Third-party and vendor risks
- Monitoring and incident response
- AI risk documentation and reporting
AI Regulation and Compliance Coming soon ＋
Covers the global AI regulatory landscape and translates legal requirements into practical compliance obligations.
This course is in development. Modules will be published once content and instructors are confirmed.
AI Security and Cybersecurity Coming soon ＋
Covers the security risks unique to AI systems, from prompt injection to agentic tool misuse, and how to build practical defenses.
This course is in development. Modules will be published once content and instructors are confirmed.
Operationalizing Responsible AI8 modules ＋
Shows how to turn responsible AI principles into organizational practices, controls, and governance processes.
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- Responsible AI operating model
- Governance committees and decision rights
- AI lifecycle management
- Policies, standards, and procedures
- Model documentation and audit trails
- AI procurement and vendor management
- Human review processes
- Monitoring, escalation, and continuous improvement
AI Workforce Development8 modules ＋
Explores how AI is changing workforce needs and how to build inclusive AI talent and reskilling strategies.
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- AI and the future of work
- Skills transformation and reskilling
- AI literacy for all employees
- Human-AI collaboration
- Job redesign and workflow redesign
- Workforce inclusion and access
- AI adoption in education and training
- Measuring workforce readiness
AI for Impact8 modules ＋
Explores how AI can advance social, economic, educational, and community impact while avoiding unintended harms.
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- AI for social good
- AI for education and learning
- AI for public services
- AI for nonprofits and community organizations
- AI for accessibility and inclusion
- Community-centered AI design
- Measuring impact
- Avoiding unintended harms
AI for Sustainability7 modules ＋
Examines AI's relationship to climate, energy, and environmental impact, including the environmental costs of AI infrastructure.
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- AI for climate and sustainability solutions
- AI in energy, transportation, agriculture, and supply chains
- Environmental costs of AI infrastructure
- Data centers, compute, and energy use
- Sustainable AI design
- Measuring environmental impact
- Responsible innovation for sustainability
Pathway 3 · TransformationResponsible AI LeadershipStrategy, adoption, investment, executive roles, change management and board governance. For executives, board members, investors, CAIOs, CTOs, CMOs and senior leaders.8 courses ＋
AI Strategy for Leaders8 modules ＋
Helps leaders align AI with organizational mission, strategy, and long-term value creation.
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- AI as a strategic capability
- Competitive advantage and AI transformation
- AI opportunity mapping
- Build, buy, or partner decisions
- AI investment prioritization
- Business model innovation
- Responsible AI as strategic trust infrastructure
- Measuring ROI, productivity, and impact
Enterprise AI Adoption8 modules ＋
Covers how to move from AI experimentation to scalable enterprise adoption.
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- Enterprise AI maturity models
- AI readiness assessment
- Use case portfolio management
- Data, technology, talent, and governance readiness
- Pilot-to-scale strategy
- Cross-functional collaboration
- Enterprise architecture and AI platforms
- Adoption metrics and success measurement
The Role of the Chief AI Officer8 modules ＋
Defines the emerging CAIO role and how it connects strategy, governance, innovation, and adoption.
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- Responsibilities of the Chief AI Officer
- AI strategy and enterprise roadmap
- AI governance and risk oversight
- Cross-functional leadership
- Use case portfolio management
- Data and technology coordination
- Responsible AI implementation
- Measuring AI value and organizational impact
AI Change Management8 modules ＋
Teaches how to lead people and organizations through AI-enabled transformation.
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- Human side of AI adoption
- Building trust in AI
- Communication and stakeholder engagement
- Workforce fears and resistance
- Upskilling and reskilling
- Change champions and internal communities
- Incentives, culture, and adoption behavior
- Responsible transition planning
AI Governance for Boards8 modules ＋
Prepares board members to oversee AI strategy, risk, compliance, and long-term organizational impact.
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- Board responsibilities in AI governance
- AI risk oversight
- Questions boards should ask management
- AI strategy and competitive positioning
- Regulatory and reputational risk
- Cybersecurity, privacy, and data governance
- Responsible AI metrics and reporting
- Crisis, incident, and accountability oversight
AI Leadership for CTOs Elective8 modules ＋
Helps technology leaders build secure, scalable, and responsible AI systems.
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- AI architecture and infrastructure
- Cloud, data, and model strategy
- Build vs. buy vs. open-source decisions
- AI security and reliability
- RAG, agents, and enterprise integrations
- AI governance by design
- Technical risk management
- Scaling AI platforms responsibly
AI Leadership for CMOs Elective8 modules ＋
Helps marketing leaders use AI for growth, personalization, and brand trust.
Show all 8 modules ▾
- AI in marketing strategy
- Content generation and brand governance
- Customer insights and personalization
- AI-enabled campaigns and automation
- Reputation, misinformation, and brand risk
- Responsible customer data use
- Human creativity and AI collaboration
- Measuring marketing impact
Investing in Responsible AI Elective8 modules ＋
Helps investors evaluate AI opportunities using both value and responsibility criteria.
Show all 8 modules ▾
- Responsible AI as an investment thesis
- Evaluating AI startups and solutions
- AI market trends and value creation
- Governance and risk due diligence
- Data rights, model risk, and regulatory exposure
- AI safety and trust as competitive advantage
- Impact investing and AI for good
- Portfolio support for responsible AI adoption
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Guided by leaders in Responsible AI.
Our Global Advisory Council brings policy, enterprise and academic expertise to everything the Institute builds.
Amari Cowan
AI Policy & Governance Fellow, U.S. Census Bureau
Anastasia Dellaccio
Exec Director of State & Regional Affairs, The Digital Chamber
Catherine Schwallie Farmer
Advocate / Angel Investor / Adjunct Instructor, Autism Angels Group
Frehiwot Gebrekrstos Girmay
AI Policy Researcher, CAIDP / Ethiopian AI Institute
Imen Ameur
Senior Executive Fellow & Co-Chair, Technology and Governance, The Digital Economist
Jordan W. Henry
Founder & Chief AI Ethicist, Veritas AI Consulting; AI2030 Fellow
Dr. Badrul H. Khan
Founder and President, McWeadon Education; e-learning pioneer
Marcy Carbajal
Director of Design / VP of CX, CommonLit
Peter Edmund Johnson
Finance Fellow, Scale AI; Founder/CEO, Ayadee Foundation; Former U.S. Diplomat
Dr. Ronna K. Jackson, DBA
Consulting Manager, Deloitte; Adjunct Professor (UNH, Purdue, CTU)
Rui Pedro Duarte
CEO, LOOP Future; AI Diplomacy faculty, U Geneva; Former Parliamentarian
Shivanku Misra
Managing Vice President, Data, AI & Emerging Technologies, Marriott International
William H. Zemp
Board Member, UN Working Group on Ethical AI; former Interim President, SNHU; U.S. Army Colonel (Ret.)