AI Fluency knowledge

The AIGW AI Fluency Framework

The AIGW AI Fluency Framework describes eight capabilities that shape responsible, contextual, and developmental AI-enabled work.

Purpose

Capability is broader than tool use.

The framework is a practical way to discuss how people frame, perform, verify, explain, learn from, and implement AI-supported work. It keeps human judgment and accountability visible when AI performs more generation and execution.

It is an AIGW framework, not a universal standard or a claim that these eight dimensions exhaust AI fluency. The assessment uses the model for developmental reflection; it does not duplicate the framework page or expose its item-level mechanics.

  1. 01 / D1

    Problem Framing and Task Selection

    Define the real decision, audience, constraints, success evidence, and where AI should or should not help.

    AI can produce a polished answer before the real decision is defined. Clear framing keeps effort attached to the right problem and makes proportional AI use possible.

  2. 02 / D2

    AI Collaboration and Orchestration

    Turn a complex assignment into controlled stages with clear AI roles, human roles, review points, and ownership.

    AI can perform several parts of a workflow quickly. Orchestration makes handoffs and human decisions visible so the work can be reviewed, repeated, and owned.

  3. 03 / D3

    Verification and Information Literacy

    Test AI-assisted claims against original sources, dates, methods, assumptions, conflicting evidence, and uncertainty.

    AI can make unsupported claims sound convincing. Verification helps prevent errors from moving into decisions before anyone sees them.

  4. 04 / D4

    Explainable Ownership and Accountability

    Explain, defend, modify, and take responsibility for important AI-assisted work without relying on the AI conversation.

    AI-assisted work still needs a human who can explain assumptions, limits, and trade-offs. Ownership makes important decisions easier to defend and correct.

  5. 05 / D5

    Domain and Contextual Judgment

    Apply professional context, local constraints, stakeholder realities, exceptions, and consequences to AI-assisted recommendations.

    AI can suggest general patterns, but context determines whether a recommendation is workable and responsible. Local constraints and consequences cannot be assumed away.

  6. 06 / D6

    Learning and Capability Transfer

    Build durable understanding that you can recall, reconstruct, transfer, and apply beyond one AI exchange.

    AI can help someone attempt more work, but capability grows when assistance becomes understanding that can be reused. Without transfer, the same support may be needed for every new task.

  7. 07 / D7

    Synthesis and Original Contribution

    Compare, synthesize, interpret, and recommend so the final work adds value beyond generic AI output.

    AI can generate many plausible options, increasing the value of human synthesis and original contribution. A clear human recommendation helps work support a real choice.

  8. 08 / D8

    Human Complementarity and Implementation

    Align people, decisions, responsibilities, and follow-through so AI-assisted evidence can become coordinated action.

    AI creates value only when people can act on the resulting evidence or recommendation. Implementation connects analysis to responsibility, decisions, and follow-up.

How the capabilities interact

Framing affects orchestration. Verification and contextual judgment shape what can be owned. Learning and synthesis determine whether assistance becomes durable contribution. Human complementarity connects the work to implementation.

The framework is intended for development, conversation, and design of better practice. It should be used alongside real work examples and institutional governance, not as a standalone employment or selection instrument.

See how the assessment relates