Beyond the Badge: Why AI Fluency Needs a Scaffold, Not Just a Score

A new wave of AI credentialling platforms has arrived. You can now take a live conversational assessment, receive a numerical score between 1 and 10, and add it to your LinkedIn profile. The ambition is serious and the need is real. AI fluency is becoming one of the most valuable professional skills of the decade.

But there is a problem with the credentialling approach — and it matters most for the people who stand to benefit most from working with AI.

The Problem with Scoring Fluency

Professional benchmarks are designed to answer a single question: how good are you right now? That is a useful question if you are an employer screening candidates. It is much less useful if you are trying to understand how to get better.

More critically, a snapshot score treats AI fluency as a fixed skill, like typing speed or spreadsheet proficiency. But working productively with AI is a dynamic, evolving, deeply personal capability. It is shaped by your domain knowledge, your communication habits, your tolerance for ambiguity — and, as I have been exploring in my own practice, by the cognitive profile you bring to the interaction.

Introducing AIDED-T

AIDED-T is a framework I have been developing to evaluate and support growth in practical AI fluency. Unlike a scoring rubric designed to benchmark performance, AIDED-T is designed as a developmental scaffold — a tool for understanding where you are, how you got there, and what the next step looks like.

Each dimension is assessed not as a pass/fail or a single score, but on a five-stage developmental scale drawn from the Dreyfus model of skill acquisition — from Novice through Advanced Beginner, Competent, Proficient, to Expert. You might be highly competent at Articulation while still at Advanced Beginner level on Technical Literacy. That profile is far more useful for development than a single aggregate score.

Why ADHD Changes Everything

One concept from the credentialling space is particularly important and underexplored: Velocity — a measure of learning momentum and engagement depth. For neurotypical learners, this is an interesting addition to a score. For ADHD learners, it is arguably the most diagnostically significant variable of all.

ADHD is characterised by executive function differences that directly affect learning momentum. Working memory limitations mean that complex, multi-step tasks require more external scaffolding to sustain. And attention regulation — the most visible ADHD characteristic — does not manifest as simple inability to focus. It manifests as variable, context-dependent focus: deep hyperfocus on high-interest tasks, rapid disengagement from low-interest ones.

This means that a Momentum indicator for an ADHD learner should not measure consistency. It should measure engagement depth and adaptation over time. The right question is not "are you making steady progress?" but "when you engage, how deeply do you go, and are you building on what came before?"

A Framework for the Long Game

AIDED-T is not a replacement for professional benchmarking. If you need a credential to demonstrate AI fluency to an employer, go and get one. But if you want to understand how you are actually developing as a human-AI collaborator — and especially if your cognitive profile means that standard learning models do not quite fit you — then a scaffold is more useful than a score.

The goal is not to reach 9.5 out of 10. The goal is to become the kind of thinker who knows how to work with AI as a genuine intellectual partner: directing it, evaluating it, pushing back on it, and using it to amplify capabilities that are already there.