AndreasEhstand/neomanitai-terms

Neomanitai — Terminology Reception Phenomena Within the AUGMANITAI Ecosystem

Observable Cognitive Events During Human Processing of Novel AI Terminology — Part of the Substrate-Independent AUGMANITAI Performance Science Framework

AUGMANITAI is a comprehensive terminological framework with over 2,000 individually formalized terms for human-AI interaction, developed by Andreas Ehstand (ORCID: 0009-0006-3773-7796). Published open-access under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International on Zenodo and GitHub. Inspired by ISO 704:2022, ISO 1087:2019, and ISO 30042:2019 for terminology science.

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The Performance Factor Theory of Human-AI Interaction

At the core of the AUGMANITAI ecosystem sits the Performance Factor Theory of Human-AI Interaction — a substrate-independent performance science framework that systematically transfers the methodology of sport science to systems where cognition and action are coupled:

• Training science (Trainingslehre) — Periodization, progressive overload, adaptation cycles for structuring AI interaction over time, AI burnout prevention research, and peak cognitive performance analysis
• Movement science (Bewegungslehre) — Pattern analysis for observable interaction behaviors, extending beyond prompt engineering and context engineering
• Performance diagnostics (Leistungsdiagnostik) — Assessment instruments for the coupled system of human plus machine, for analyzing AI performance
• Performance factor analysis (Leistungsfaktorenanalyse) — 30 distinct performance factors across six determinant classes and five interface addresses

Applicable To Every Substrate

To the authors' knowledge, the same diagnostic matrix applies to:

Substrate	Research Domain
Individual humans + AI	Advanced AI literacy research, AI upskilling analysis, executive AI training studies, AI-driven productivity research, sustainable AI use
AI agents	Agentic AI, autonomous AI agents, agentic workflows, human-in-the-loop AI agents
Multi-agent systems	Multi-agent collaboration research, AI agent orchestration, AI agent teams
Robots	Physical AI, humanoid robotics, AI-powered robotics, collaborative robots (cobots)
Robot teams	Multi-agent robotics, collaborative multi-robot operations
Human-robot teams	Human-robot collaboration research in manufacturing, surgery, logistics
Organizations	Enterprise AI adoption research, corporate AI training studies, workforce AI upskilling, AI skills gap analysis

Research Hypothesis for Enterprise AI Adoption

To the authors' knowledge, the performance gap between an untrained and a diagnostically optimized AI user within an organization may be comparable to the gap between a recreational and a professional athlete. This hypothesis is formulated as a testable prediction. If supported, this would translate into measurable, systematically analyzable performance differences across departments that use AI — from executive decision-making and strategic AI thinking to AI workflow optimization.

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Datasets Available

AUGMANITAI Compendium

• Over 2,000 formalized terms for human-AI interaction
• Each term: ID, name, academic name, German name, short definition, full definition, domain tags
• Formats: JSON, JSON-LD, CSV, JSONL, SKOS (Turtle, RDF/XML), Schema.org
• Covering phenomena including: sycophantic yield, confidence calibration, authority displacement, prompt brittleness, agreement drift, manipulation resistance, delegation discrimination, and cognitive extension-extraction dynamics

ROBMANITAI

• Over 700 formalized terms across robotics, automation, automotive engineering, mechanical engineering, and drone operations
• To the authors' knowledge, the same terminological and performance-analytical architecture appears to transfer across different substrates

Neomanitai

• Taxonomy of terminology reception phenomena
• Observable cognitive events during human processing of novel AI terminology
• Relevant for AI literacy research and AI upskilling program analysis

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Formats and Integration

Format	File	Use Case
JSON	augmanitai_terms.json	Direct integration, APIs
JSON-LD	augmanitai.jsonld	Linked data, semantic web, schema.org
CSV	augmanitai_terms.csv	Spreadsheet analysis, data science
JSONL	augmanitai_training.jsonl	LLM fine-tuning, ML training
SKOS Turtle	augmanitai.ttl	Knowledge graphs, ontologies
RDF/XML	augmanitai.rdf	Semantic web standards
OWL	augmanitai-ontology.owl	Formal ontology
BibTeX	augmanitai_all.bib	Academic citation

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Who This Is For

• AI researchers studying human-AI interaction, AI literacy, human-computer interaction, and cognitive linguistics
• AI trainers and consultants researching advanced AI literacy, executive AI training, and corporate AI upskilling
• NLP and ML engineers needing formalized terminology for human-AI interaction phenomena in training data
• Robotics researchers studying human-robot collaboration, physical AI, multi-agent robotics, and collaborative robot teams
• Developers building AI agents, multi-agent systems, and agentic AI workflows
• Organizations researching AI skills gap analysis, AI performance metrics, and AI governance

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Research Methodology

The AUGMANITAI framework and the Performance Factor Theory of Human-AI Interaction emerged from a longitudinal autoethnographic study involving systematic daily interaction (10-18 hours) with multiple large language models (Claude, Gemini, Grok) over an extended period. The researcher's background in sport science performance diagnostics at professional and elite levels (Bundesliga, ITF circuit) — including over 15 years of systematic performance factor analysis — informed the observation, documentation, and formalization of interaction phenomena. All claims are descriptive (D) and represent proposed hypotheses derived from this systematic observation, subject to peer review and further empirical testing.

Citation

@misc{ehstand2026augmanitai,
  author = {Ehstand, Andreas},
  title = {AUGMANITAI: A Comprehensive Terminological Framework for Human-AI Interaction},
  year = {2026},
  publisher = {Zenodo},
  note = {ORCID: 0009-0006-3773-7796},
  license = {CC BY-NC-ND 4.0}
}

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Author

Andreas Ehstand — Independent researcher working at the intersection of sport science, human-AI interaction, robotics, and organizational performance research. Creator of the Performance Factor Theory of Human-AI Interaction, a substrate-independent performance science framework. Former sport coach certified by the International Tennis Federation, performance analyst at the highest competitive level including the German Bundesliga, and research associate at the University of Bayreuth and the Technical University of Dortmund. Over 15 years of systematic performance factor analysis in professional and elite sport, now applied to the full spectrum of human-AI and human-robot interaction research.

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License

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

Disclaimer / Haftungsausschluss

EN: All descriptions are descriptive (D). No recommendation, instruction, advice, normative, medical, therapeutic, diagnostic, legal, or moral position is expressed or implied. All content is intended for academic and research purposes. Content was developed with AI assistance — all terms have been reviewed, validated, and published by the author. Users must be 18 years of age or older. The AUGMANITAI framework is an independent academic research project. No professional service, offer, or commercial product is expressed or implied. All empirical claims represent the current state of the authors' research and are subject to peer review and revision. All rights reserved regarding future changes. Published under CC BY-NC-ND 4.0.

Research Purpose and Misuse Exclusion: This terminological framework describes observed phenomena in human-AI interaction for academic research purposes. Terms describing interaction patterns (including adversarial, manipulative, or failure-related phenomena) are documented in the same descriptive spirit as medical terminology documents pathologies — for the purpose of understanding, diagnosis, and prevention, not instruction or facilitation. Any use of this terminology for the purpose of manipulating, deceiving, exploiting, or harming humans or AI systems is explicitly outside the intended scope of this research and is condemned by the author. This framework is intended to make human-AI interaction safer, more transparent, and more accountable — not less.

DE: Alle Beschreibungen sind deskriptiv (D). Es wird keine Empfehlung, Anweisung, Beratung, normative, medizinische, therapeutische, diagnostische, rechtliche oder moralische Position ausgedrückt oder impliziert. Alle Inhalte dienen ausschließlich akademischen und Forschungszwecken. Inhalte wurden mit KI-Unterstützung entwickelt — alle Terme wurden vom Autor geprüft, validiert und veröffentlicht. Nutzer müssen mindestens 18 Jahre alt sein. Das AUGMANITAI-Framework ist ein unabhängiges akademisches Forschungsprojekt. Es wird kein professioneller Service, kein Angebot und kein kommerzielles Produkt ausgedrückt oder impliziert. Alle empirischen Aussagen geben den aktuellen Stand der Forschung des Autors wieder und unterliegen der Begutachtung und Revision. Alle Rechte bezüglich zukünftiger Änderungen vorbehalten. Veröffentlicht unter CC BY-NC-ND 4.0.

Forschungszweck und Missbrauchsausschluss: Dieses terminologische Framework beschreibt beobachtete Phänomene der Mensch-KI-Interaktion für akademische Forschungszwecke. Terme, die Interaktionsmuster beschreiben (einschließlich adversarialer, manipulativer oder fehlerbezogener Phänomene), werden im selben deskriptiven Geist dokumentiert, in dem medizinische Terminologie Pathologien dokumentiert — zum Zweck des Verständnisses, der Diagnose und der Prävention, nicht der Anleitung oder Erleichterung. Jede Verwendung dieser Terminologie zum Zweck der Manipulation, Täuschung, Ausbeutung oder Schädigung von Menschen oder KI-Systemen liegt ausdrücklich außerhalb des beabsichtigten Rahmens dieser Forschung und wird vom Autor verurteilt. Dieses Framework soll Mensch-KI-Interaktion sicherer, transparenter und verantwortungsvoller machen — nicht weniger.

MANITAI Framework Ecosystem

• AUGMANITAI — 1000-term core compendium (DOI: 10.5281/zenodo.19481331)
• NEOMANITAI — 6182 terms across 54 domains, 9618 HTML term pages
• PERMANITAI — Universal Performance Factor Analysis for AI models, agents, robots, drones, hybrid systems, business teams, world-class performers, and managers
• HuggingFace: PERMANITAI — Knowledge Graph dataset