# Design brief: an AI fluency site for adults starting from zero Every decision below answers to the evidence in `misconception_taxonomy.md`. Where a choice is mine rather than the literature's, it says so. ## The thesis Two dials, moved in opposite directions. Confidence in oneself goes up; confidence in the tool comes down to something calibrated. These are separable in the data ([Lee et al. 2025](https://doi.org/10.1145/3706598.3713778)), and conflating them is the characteristic failure of beginner AI content, which reads as reassurance and produces overtrust. The site's success condition is a visitor who will *try* things without asking permission and *check* things without being told. ## Ten principles **P1. Teach one mechanism, not ten facts.** The ten misconceptions reduce to five distinctions. Lessons are organised by distinction, with the misconceptions as symptoms, because a learner who holds the distinction defuses misconceptions the site never mentions. **P2. Name the wrong intuition out loud before correcting it.** Folk theories are constructed from interface probing and analogy ([Li et al. 2026](https://doi.org/10.1177/20539517261447838)), so they are already in the visitor's head. Conceptual change needs the prior belief surfaced, not bypassed. Every lesson opens with the plausible wrong version, stated sympathetically enough that a holder of it feels recognised rather than caught. **P3. Make verification cheap, never mandatory.** The binding constraint. Cognitive forcing reduces overreliance but is disliked and disproportionately benefits users already high in Need for Cognition ([Buçinca et al. 2021](https://doi.org/10.1145/3449287)); whether an explanation helps depends on the cost of engaging with it, not its quality ([Vasconcelos et al. 2022](https://arxiv.org/abs/2212.06823)). So: no forced quizzes, no gates, no interstitials. Instead, one-click "ask it again" and "check this" affordances that cost less effort than skipping them. **P4. Hands-on over exposition.** Older adults surveyed on AI literacy wanted to learn, could not find a starting point, and preferred hands-on learning ([arXiv:2504.14649](https://arxiv.org/abs/2504.14649)). Every lesson contains something the visitor manipulates within the first screen. No lesson is more than two screens of prose before an interaction. **P5. Spend the anthropomorphism budget on competence, not sentience.** Intelligence attributions predict advice-taking; experience attributions do not, and trend the other way ([Colombatto et al. 2025](https://doi.org/10.1038/s44271-025-00262-1)). The site therefore does not argue about whether AI feels anything. It attacks "it knows" and leaves "it feels" alone, which also avoids a fight that makes users defensive. Telling people to stop anthropomorphising is in any case weak when the system genuinely mimics human communication ([Peter et al. 2025](https://doi.org/10.1073/pnas.2415898122)). **P6. Teach the use pattern, not just the concept.** Generative tools widened the gap between strong and struggling novices rather than closing it ([arXiv:2405.17739](https://arxiv.org/abs/2405.17739)); the accelerating students used the tools differently. Each lesson ends with a concrete behavioural move, phrased as something to do, not something to know. **P7. Analogy discipline: one analogy per concept, and state its limit.** Folk theories form by analogy to familiar objects, so an unbounded analogy becomes the next misconception. Any analogy the site introduces is followed immediately by where it breaks. Banned outright: "digital brain", "it reads the internet", "like a very fast librarian": each seeds M1 or M3. **P8. Interactivity must carry the concept, not decorate it.** A 3D diagram earns its place only when the concept is *spatial or distributional* and the misconception is *visual in origin*. Where the concept is procedural or factual, prose and a static diagram are better and cheaper. This is my judgement call, applied explicitly per concept in the curriculum map; the literature does not settle it. **P9. Accessible by construction.** Mixed-age general-adult audience means WebGL cannot be load-bearing. Every diagram has a prose equivalent that carries the same teaching point, respects `prefers-reduced-motion`, works by keyboard, and degrades to static when WebGL is unavailable. Reading level: plain language, target around grade 9, no unexplained jargon, every technical term glossed on first use. **P10. Honesty about uncertainty, including the site's own.** Two of the ten misconceptions have no direct user-study support. Where the site teaches something contested or thinly evidenced, it says so in one line rather than flattening it into confident instruction. This models the epistemic behaviour the site is trying to teach. ## What the site will not do No "AI is the future" framing; motivation research on this audience shows they are already motivated and stuck on starting points, not on why. No capability hype and no doom. No leaderboards, badges, or streaks, which reward completion over calibration. No live model calls in v1: they make the site fragile, introduce cost and privacy questions, and the concepts are better taught with pre-recorded, inspectable examples where the visitor can see the same prompt produce different outputs without waiting on a network. ## Tone Plain, specific, and unhurried. The register of a competent colleague showing you something on their screen, not a course, not a brand, not a warning label. Second person. Short sentences. No exclamation marks. Humour is fine where it is dry and does not undercut a safety point. ## Success conditions, testable A visitor finishing the site should be able to: say where the words come from without using the word "search"; predict that the same prompt can give a different answer, and check it; name one thing the model cannot reach; describe one verification habit they would actually use; and report that trying things carries no risk of breaking anything. The last is the self-efficacy target and the first four are the calibration target.