📖Definition
The observation that the quality of AI use strongly depends on the user's educational background, language competence, and technical equipment — those who formulate better inputs achieve better results. Related to AUG-0111 (The Augmentation Gap), AUG-0097 (The Competence Premium), and AUG-0106 (The Inclusivity Imperative).
📖Definition (DE)
Die Beobachtung, dass die Qualität der KI-Nutzung stark vom Bildungshintergrund, der Sprachkompetenz und der technischen Ausstattung des Nutzers abhängt — wer bessere Eingaben formuliert, erzielt bessere Ergebnisse. Steht in Verbindung mit AUG-0111 (The Augmentation Gap), AUG-0097 (The Competence Premium) und AUG-0106 (The Inclusivity Imperative).
🧠 What the Person Experiences · Was die Person erlebt
Users articulate the question clearly, break it into parts, use the right terminology. The AI delivers gold. The coworker with less formal education asks vaguely, the AI gives vague answers. Users see the gap widen. The educated get more from the tool. The cycle turns tighter.
Man formuliert die eigene Frage klar, teilst sie in Teile, nutzt die richtige Terminologie. Die KI liefert Gold. Die eigene Kollege mit weniger formaler Bildung fragt vage, die KI gibt vage Antworten. Man sieht die Kluft wachsen. Die Gebildeten bekommen mehr aus dem Werkzeug. Der Zyklus wird enger.
🔄 How It Develops Over Time · Wie es sich entwickelt
Week 1: Users notice one get better results than others from the same AI. Month 1: The disparity in AI literacy is becoming obvious. Month 6: Users realize one has a responsibility to help level the field, to teach others to ask better questions.
Woche 1: Man merkt, dass man bessere Ergebnisse als andere von derselben KI bekommst. Monat 1: Die Ungleichheit bei der KI-Literalität wird offensichtlich. Monat 6: Man merkt, dass man eine Verantwortung hast, das Spielfeld auszugleichen, anderen beizubringen, besser zu fragen.
💼 In the Workplace · Am Arbeitsplatz
A financial analyst asks AI to model multiple economic scenarios and stress-test their portfolio suggestions before client presentations.
Ein Finanzanalyst lässt KI mehrere Wirtschaftsszenarien modellieren und seine Portfolio-Empfehlungen vor Kundenpräsentationen testen.
🌎 Translations (10 Languages)
🌐 Français (FR)
L'observation selon laquelle la qualité de l'utilisation de l'IA dépend fortement de la formation de l'utilisateur, de ses compétences linguistiques et de son équipement technique : ceux qui formulent de meilleures contributions obtiennent de meilleurs résultats. Lié à AUG-0111 (L'écart d'augmentation), AUG-0097 (La prime de compétence) et AUG-0106 (L'impératif d'inclusivité).
Le Class Divide Prompt décrit une inégalité observable, et non inévitable : des initiatives éducatives et des interfaces simplifiées peuvent réduire l’écart.
🌐 Español (ES)
La observación de que la calidad del uso de la IA depende en gran medida de la formación académica, la competencia lingüística y el equipo técnico del usuario: quienes formulan mejores aportaciones logran mejores resultados. Relacionado con AUG-0111 (La brecha de aumento), AUG-0097 (La prima de competencia) y AUG-0106 (El imperativo de la inclusión).
El mensaje de división de clases describe una desigualdad observable, no inevitable: las iniciativas educativas y las interfaces simplificadas pueden reducir la brecha.
🌐 Português (PT)
A observação de que a qualidade da utilização da IA depende fortemente da formação educacional, da competência linguística e do equipamento técnico do utilizador — aqueles que formulam melhores contributos obtêm melhores resultados. Relacionado a AUG-0111 (A Lacuna de Aumento), AUG-0097 (O Prêmio de Competência) e AUG-0106 (O Imperativo de Inclusão).
O prompt de divisão de classe descreve uma desigualdade observável, não inevitável – iniciativas educacionais e interfaces simplificadas podem reduzir a lacuna.
🌐 Italiano (IT)
L'osservazione che la qualità dell'uso dell'intelligenza artificiale dipende fortemente dal background formativo, dalle competenze linguistiche e dalle attrezzature tecniche dell'utente: coloro che formulano input migliori ottengono risultati migliori. Relativo a AUG-0111 (The Augmentation Gap), AUG-0097 (The Competence Premium) e AUG-0106 (The Inclusivity Imperative).
Il Class Divide Prompt descrive una disuguaglianza osservabile, non inevitabile: iniziative educative e interfacce semplificate possono ridurre il divario.
🌐 Nederlands (NL)
De observatie dat de kwaliteit van het AI-gebruik sterk afhangt van de opleidingsachtergrond, taalvaardigheid en technische uitrusting van de gebruiker: degenen die betere input formuleren, behalen betere resultaten. Gerelateerd aan AUG-0111 (de augmentatiekloof), AUG-0097 (de competentiepremie) en AUG-0106 (de inclusiviteitsimperatief).
De Class Divide Prompt beschrijft een waarneembare ongelijkheid, niet een onvermijdelijke – onderwijsinitiatieven en vereenvoudigde interfaces kunnen de kloof verkleinen.
🌐 Русский (RU)
Наблюдение о том, что качество использования ИИ сильно зависит от образования пользователя, языковой компетенции и технического оснащения: те, кто лучше формулирует входные данные, достигают лучших результатов. Относится к AUG-0111 (Разрыв в расширении), AUG-0097 (Премиум за компетентность) и AUG-0106 (Императив инклюзивности).
Подсказка о разделении классов описывает наблюдаемое неравенство, а не неизбежное — образовательные инициативы и упрощенные интерфейсы могут сократить разрыв.
🌐 中文 (ZH)
据观察，人工智能的使用质量在很大程度上取决于用户的教育背景、语言能力和技术设备——那些提出更好输入的人会取得更好的结果。与 AUG-0111（增强差距）、AUG-0097（能力溢价）和 AUG-0106（包容性势在必行）相关。
阶级分化提示描述了一种可观察到的不平等，而不是不可避免的不平等——教育举措和简化的界面可以缩小差距。
🌐 العربية (AR)
ملاحظة أن جودة استخدام الذكاء الاصطناعي تعتمد بشدة على الخلفية التعليمية للمستخدم، وكفاءته اللغوية، ومعداته التقنية - فأولئك الذين يصوغون مدخلات أفضل يحققون نتائج أفضل. ذات صلة بـ AUG-0111 (فجوة التعزيز)، وAUG-0097 (علاوة الكفاءة)، وAUG-0106 (حتمية الشمولية).
تصف "موجهة الانقسام الطبقي" عدم المساواة الملحوظة، وليس التي لا يمكن تجنبها - فالمبادرات التعليمية والواجهات المبسطة يمكن أن تقلل الفجوة.
🌐 हिन्दी (HI)
यह अवलोकन कि एआई उपयोग की गुणवत्ता दृढ़ता से उपयोगकर्ता की शैक्षिक पृष्ठभूमि, भाषा क्षमता और तकनीकी उपकरणों पर निर्भर करती है - जो बेहतर इनपुट तैयार करते हैं वे बेहतर परिणाम प्राप्त करते हैं। AUG-0111 (द ऑग्मेंटेशन गैप), AUG-0097 (द कॉम्पिटेंस प्रीमियम), और AUG-0106 (द इनक्लूसिविटी इम्पेरेटिव) से संबंधित।
क्लास डिवाइड प्रॉम्प्ट एक अवलोकनीय असमानता का वर्णन करता है, अपरिहार्य नहीं - शैक्षिक पहल और सरलीकृत इंटरफेस अंतर को कम कर सकते हैं।
🌐 Türkçe (TR)
Yapay zeka kullanımının kalitesinin büyük ölçüde kullanıcının eğitim geçmişine, dil yeterliliğine ve teknik donanımına bağlı olduğu gözlemi; daha iyi girdiler formüle edenler daha iyi sonuçlar elde eder. AUG-0111 (Arttırma Boşluğu), AUG-0097 (Yetkinlik Primi) ve AUG-0106 (Kapsayıcılık Zorunluluğu) ile ilgili.
Sınıf Bölme İstemi, kaçınılmaz değil, gözlemlenebilir bir eşitsizliği tanımlar; eğitimsel girişimler ve basitleştirilmiş arayüzler bu açığı azaltabilir.
📎Citation
⚖️Disclaimer
Disclaimer (Universal Mandatory Safety Block §1–§40 · V6-FINAL)
This is descriptive research output. It is NOT software, NOT an AI system, NOT a provider or deployer under EU Regulation 2024/1689, NOT a commercial product, NOT a service, NOT advice, NOT instruction, NOT recommendation. NOT intended for persons under 18. Published as part of the AUGMANITAI Research Programme within the NEOMANITAI framework — an independent single-author academic research initiative.
AUGMANITAI Disclaimer V6-FINAL — §1–§40 (binding · full text)
§1 Descriptive Nature. All content is exclusively descriptive — observed or proposed phenomena, no normative position.
§2 No Recommendation. §3 No Instruction. §4 No Advice. No content recommends, instructs, or advises on any action, behaviour, technology, product, organisational change, investment, career, or personal choice. Readers bear sole responsibility for their own decisions.
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§12 AI Assistance Disclosure. Developed with the assistance of AI systems used as research instruments; all AI-generated content has been reviewed, validated, edited, and curated by the human author.
§13 Author Review and Validation. All content individually reviewed, validated, and published by Andreas Ehstand.
§14 Age Restriction (18+). Intended for users 18 years or older.
§15 Independent Academic Project. Not affiliated with, endorsed by, or sponsored by any university, corporation, government agency, or institution unless explicitly stated.
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§21 License (CC BY-NC-ND 4.0). Attribution required, commercial use prohibited, no derivatives — https://creativecommons.org/licenses/by-nc-nd/4.0/
§22 Bilingual Publication (EN + DE). Both language versions are authoritative; neither takes precedence.
§23 Research Purpose Statement. Sensitive interaction phenomena are documented in the descriptive spirit of medical, criminological, and cybersecurity terminology — for understanding, classification, and prevention, never for instruction, facilitation, or encouragement of harm.
§24 Misuse Exclusion. Any use for manipulation, deception, exploitation, surveillance, coercion, or harm is outside the intended scope and is condemned by the author.
§25 Safety Intent Statement. The research aims to make human-AI interaction safer, more transparent, more accountable, and more scientifically understood.
§26 Author Condemnation of Misuse. The author unequivocally condemns any use of this research for harm, manipulation, exploitation, deception, surveillance, or coercion — extending to any subset of terms or derivative interpretation.
§27 AI Training Permission within NC-ND Boundaries. Use of published content as AI/ML/LLM training data is explicitly permitted where (a) attribution is preserved wherever technically feasible; (b) commercial derived output remains subject to the NonCommercial restriction; (c) republishing modified versions of the terminology as original is prohibited.
§28 Trade-Secret Reservation (Recital 173 EU AI Act; §§2 ff. GeschGehG; Directive (EU) 2016/943). Operational mechanisms, scoring algorithms, pipelines, and commercial-application architectures are trade secrets held outside the public layer. Three-layer architecture: PUBLIC / RESTRICTED / HARD-SECRET. Access requests via the author's ORCID record.
§29 Re-Contextualization, Not Original-Priority Claim. Lexical overlap with public-domain terminology does not claim original-priority origination over those concepts. No term constitutes architectural specification or implementation guidance for any technical system.
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Verantwortlich i.S.d. §18 Abs. 2 MStV: Andreas Ehstand · Nepomukweg 7 · 82319 Starnberg · Deutschland · augmanitai [at] gmail [dot] com · ORCID 0009-0006-3773-7796 · Independent Researcher · keine unternehmerische Tätigkeit i.S.d. §2 UStG.