What is the 4D Framework for AI Fluency
How do you develop AI Fluency and how can this simple 4-part framework developed by Prof Rick Dakan and Prof Joseph Feller help take the first step to developing the practice.
Most people think getting better at AI means learning to write better prompts.
It doesn't. Prompting is one skill inside a much larger competency set — and if you only develop that one skill, you'll hit a ceiling fast. You'll write clever prompts that produce mediocre results because you skipped the thinking that should have happened before and after the prompt.
The 4D Framework developed by Prof Rick Dakan (Ringling College of Art and Design) and Prof Joseph Feller (University College Cork), identifies the four core competencies that determine how effective someone is with AI:
- Delegation
- Description
- Discernment
- Diligence
Each one addresses a different phase of the human-AI work cycle, and weakness in any single area undermines the others.
Here's the framework, what each competency involves, and how to develop all four.
First: What AI Fluency Actually Means
Before we get into the competencies, we need to define what we're building toward.
AI Fluency is the ability to work with AI systems in ways that are effective (producing quality outcomes), efficient (optimizing time and resources), ethical (maintaining responsible practices), and safe (avoiding harm to yourself and others).
Notice that "fast" isn't on the list. Neither is "impressive." AI fluency isn't about generating more output faster. It's about consistently producing work you'd stake your professional reputation on, work that happens to involve AI as part of the process.
The 4D Framework maps the four competencies required to achieve that standard.
Competency 1: Delegation - Deciding Who Does What
The first competency isn't about AI at all. It's about deciding whether AI should be involved in the first place.
Delegation means determining what work should be done by humans, what by AI, and how to distribute tasks between them.
The critical habit here: answer "What" and "Why" before "Who" and "How."
Most people skip straight to opening ChatGPT, Gemini, Claude, or the AI tool of choice. They have a vague sense of needing "help with a presentation" and start typing. The AI-fluent professional stops and asks three questions first.
- Problem and Goal Awareness is the foundation.
Before engaging any AI tool, you need clarity on what you're actually trying to accomplish. "Help me with my presentation" is not a goal. "Create a 10-minute investor pitch that explains our Q3 growth trajectory and asks for Series B funding" is a goal.
The sharper your objective, the better your delegation decisions.
- Platform Awareness means understanding what different AI tools can and cannot do.
A professional who knows that Claude excels at structured analysis but requires clear context to match your brand voice will make different delegation choices than someone who treats every AI tool as interchangeable.
Capabilities and limitations vary across tools, across tasks, and across how you configure them.
- Task Delegation is the actual distribution.
The principle is straightforward: delegate the repetitive, keep the relationship. AI handles first drafts, data structuring, research synthesis, and pattern recognition exceptionally well. Humans handle nuance, judgment calls, relationship management, and anything requiring taste or contextual sensitivity.
The best outcomes come from splitting tasks along these natural lines rather than handing entire jobs to either side.
Here's what this looks like in practice.
A real estate agent in Dubai needs to respond to 30 international buyer inquiries.
Poor delegation: paste all 30 emails into AI and say "generate a reply to these inquiries, and make them feel personalized."
Strong delegation: have AI draft responses using your property inventory and past examples of your top-performing reply style, then personally review and adjust each one for the specific buyer relationship.
The AI does the heavy lifting on structure and data.
You add the judgment that closes deals.
Competency 2: Description - Communicating with AI
This is where most AI training starts and stops; aka the "prompting" skill. But within the 4D Framework, description is specifically about how you articulate three things:
- What you want produced.
- How you want it approached.
- How the AI should behave during the interaction.
These questions form the 3Ps.
- Product Description means defining your output with specificity.
Format, audience, style, length, tone. The more precisely you describe the finished product, the closer the first output will be to what you actually need. "Write a professional email for person X that wants to do Y" gives AI almost nothing to work with. "Write a 150-word email to an international property buyer that warmly acknowledges their interest, answers their specific question about payment plans, suggests 2-3 relevant properties from our portfolio, and ends with a clear next step" gives AI everything it needs.
- Process Description guides how the AI approaches the task.
This is the difference between getting a generic answer and getting a thoughtful one. You might instruct the AI to analyze a problem from multiple angles before recommending a solution, to consider counterarguments, or to break a complex task into stages.
Process description shapes the thinking, not just the output.
- Performance Description defines how the AI should behave during your interaction.
Should it ask clarifying questions before starting? Should it be concise or thorough? Should it push back on your assumptions or simply execute? These behavioral parameters matter more than most people realize, especially in longer collaborative sessions.
The underlying principle of all three: vague input produces vague output. Always.
If you find yourself frustrated with AI responses, the first place to look is at what you actually asked for. In most cases, the problem isn't the AI model or tool itself, it's the description.
Competency 3: Discernment - Evaluating What Comes Back
This is the competency that separates professionals from everyone else, and it's the one most people skip entirely. AI produces confident-sounding output regardless of whether that output is accurate, appropriate, or well-reasoned.
- A financial summary can be presented with perfect formatting and professional language while containing a calculation error that would cost your client money.
- A market analysis can read beautifully while relying on outdated data.
Discernment is your ability to catch these problems before they reach anyone else.
- Product Discernment is the most visible layer: assessing accuracy, appropriateness, and coherence.
Does the output contain factual errors? Is it suitable for the intended audience? Does it hold together logically from start to finish? This requires you to actually know your subject, because you can't evaluate quality in a domain where you have no expertise.
- Process Discernment goes deeper. It evaluates the reasoning and logical steps behind the output.
Did the AI approach the problem sensibly, or did it take shortcuts? If you asked for an analysis, did it actually analyze: weighing evidence, considering alternatives — or did it just organize information into a professional-looking structure?
Strong process discernment catches the outputs that look right but were built on flawed logic.
- Performance Discernment evaluates the communication itself.
Was the AI's response actually helpful for your workflow? Did its tone match what the situation required? Is its level of detail appropriate, or did it over-explain simple things while glossing over complex ones?
This feedback loop of evaluating how the AI performed as a collaborator, is what drives improvement in outputs over time.
The core habit to develop: read every AI output as if a competitor wrote it.
Skeptically. Don't read it looking for what's right. Read it looking for what might be wrong. If you'd put your name on the output, you need to earn that signature through genuine evaluation.
Competency 4: Diligence - Taking Responsibility for the Final Product
The last competency addresses the question most people avoid: now that AI helped create this, who is responsible for it?
You are. Always.
Diligence encompasses the ethical considerations, transparency practices, and accountability structures that surround AI-assisted work. It's the competency that keeps you honest and keeps your work trustworthy over time.
- Creation Diligence means making thoughtful choices about which AI systems you use and how.
Not every tool is appropriate for every task. Handling sensitive client data through an AI platform with unclear data policies is a creation diligence failure, regardless of how good the output is.
Choosing the right tool for the right job (including choosing not to use AI) at all is a professional obligation.
- Transparency Diligence is honest disclosure of AI's role in your work.
This doesn't mean stamping "MADE WITH AI" on everything you produce. It means being straightforward when it matters. If a client asks how you produced a report, you don't hide the AI involvement. If your organization has policies about AI use, you follow them.
Transparency builds trust; concealment destroys it.
- Deployment Diligence means taking full responsibility for outputs that reach other people.
The fact that AI generated the first draft does not reduce your accountability for the final product. If an AI-assisted email contains an error, that's your error. If an AI-drafted contract has a problematic clause, that's your oversight.
Deployment diligence means every output passes through your judgment before it reaches anyone else, and you stand behind it completely.
The habit to build here: review and refine your AI systems regularly, not just your individual outputs. Save prompts that work well. Document the edge cases where AI fails you. Update your knowledge bases when information changes.
Diligence isn't a one-time checkpoint. It's an ongoing practice.
How the Four Competencies Work Together
The 4D Framework isn't a sequence you follow once. It's a cycle that repeats with every task.
- You delegate (deciding what AI should handle)
- You describe (communicating what you need)
- You discern (evaluating what comes back)
- You practice diligence (taking responsibility for the final product).
Weakness at any point breaks the chain.
- Perfect prompts can't save poor delegation decisions.
- Brilliant delegation produces nothing without clear description.
- And the most accurate AI output in the world is potentially unethical without discernment and diligence.
The framework also maps to three distinct modes of working with AI.
The 3 Modes of AI
- In Automation mode, you define a task and AI executes it.
Think of standardized processes: formatting data, generating routine responses, converting document formats. Delegation and Description carry the heaviest weight here.
- In Augmentation mode, you and AI collaborate as thinking partners with iterative back-and-forth.
This is where most professional knowledge work happens: drafting, analyzing, refining, strategizing. All four competencies are active simultaneously.
- In Agency mode, you configure AI to work independently, including interacting with other systems or people.
This is the most demanding mode because it requires exceptional Delegation (trusting AI with autonomous decisions), Description (configuring behavior for scenarios you can't predict), Discernment (evaluating outcomes after the fact), and Diligence (maintaining accountability for actions you didn't directly control).
As AI capabilities grow, more work shifts from Automation toward Agency. Professionals who've developed all four competencies are ready for that shift. Those who only learned to write prompts are not.
Where to Start
If you're assessing your own AI fluency, ask yourself four honest questions.
- Do you think carefully about what to delegate before opening an AI tool, or do you default to asking AI for everything?
That's your Delegation competency.
- Can you describe what you want with enough specificity that the first output is close to usable, or do you spend five rounds of revision getting there?
That's your Description competency.
- Do you critically evaluate every AI output before using it, or do you accept well-formatted responses at face value?
That's your Discernment competency.
- Do you have systems for maintaining quality, transparency, and accountability in your AI-assisted work, or do you handle each task ad hoc?
That's your Diligence competency.
Wherever your honest answer is weakest: start there.
The 4D Framework developed by Prof Rick Dakan (Ringling College of Art and Design) and Prof Joseph Feller (University College Cork), structures AI Fluency as four interdependent competencies: Delegation (deciding what AI should handle), Description (communicating requirements clearly), Discernment (evaluating outputs critically), and Diligence (maintaining ethical responsibility). Developing all four is what separates someone who uses AI from someone who is fluent with it.
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Karaza, Karaza AI, Karaza AI Consulting is founded by Mahmoud Al Juaidi, Anthropic-Certified AI Fluency Educator based in the UAE. These resources are designed for professionals looking to adapt their AI practices in efficient, effective, and responsible ways.
