Research Preprint · Living Document

Phenomenology of Human-AI Interaction

A research-preprint corpus of 1,128 defined terms across 187 research-domain clusters — the descriptive terminology of how humans and AI systems meet, work, learn, and shape each other. ISO 704/1087/30042 aligned. By Andreas Ehstand.

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EN + DE bilingual CC BY-NC-ND 4.0 §1–§29 inline ISO 704/1087/30042
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Browse by Domain

The compendium is organized across 187 research-domain clusters. Nine most-developed hubs — start by topic, not alphabet.

All Domains (Master Index)

187 clusters · 1,128 terms

Master navigation across every cluster.

Enter master index

Subjective Phenomenology

RPH · Subjective Phenomenology

First-person experience patterns in human-AI interaction. Backbone of the N=1 Extreme Observation Methodology.

Open cluster

AUGMANITAI Core

AUG · Core Terminology

Canonical descriptive terminology baseline of the framework.

Open cluster

NEOMANITAI Knowledge Graph

NEO · Knowledge Graph

Cross-reference and ontological-relation layer.

Open cluster

Cross-Domain Pool

WK · Cross-Domain Pool

Cross-disciplinary terminology bridging multiple research areas.

Open cluster

ROBMANITAI

Robotics · multi-agent

Robotics Human-AI Interaction phenomenology — multi-robot coordination, human-robot team dynamics, multi-agent orchestration.

Open branch

PERMANITAI

Performance · PFT-MKI

Substrate-Independent Performance Factor framework. Grounded in 25-year sport-science methodology.

Open branch

EDUMANITAI

Education · AI-mediated learning

Phenomenology of learning and pedagogy mediated by AI systems.

Open branch

JOBMANITAI

Work · AI integration patterns

Phenomenology of work shaped by AI integration.

Open branch

Looking for a specific term? → Full alphabetic Atlas (1,128 terms)

Methodological Frame

The compendium applies the Holistic Performance Analysis Methodology — a cross-domain transfer of high-performance sport-science methodology (25-year Bundesliga tennis coaching lineage) to human-AI interaction analysis. Four pillars structure every observation:

Observable

Each phenomenon must be detectable in real human-AI interaction.

Decomposable

Complex patterns broken into smaller, distinct sub-phenomena.

Measurable

Operational definitions allow quantification, even when subjective.

Controllable

Measured phenomena enter the space of intentional intervention.

Plus: Multi-LLM Triadic Audit Framework · Cross-LLM Convergence Methodology · N=1 Extreme Observation Methodology · Compression Axiom · Living-Document Discipline · ISO 704/1087/30042-aligned terminology engineering.

About the Author

Identity & Affiliation

  • Name: Andreas Ehstand, M.Ed.
  • Role: AI Researcher
  • Location: Starnberg, Germany
  • ORCID: 0009-0006-3773-7796

Academic Anchors

  • TU Dortmund (former research associate)
  • University of Bayreuth — Sport Science + Hochschuldidaktik
  • FBZHL Bayern — Zertifikat Hochschullehre (youngest holder)
  • Toni Nadal Excellence Certificate
  • GPTCA / ISMCA certifications
  • BTV B-Trainer Leistungssport

Cross-Domain Background

  • 25 years professional sport coaching
  • Bundesliga tennis coach (multiple years)
  • ITF tournament coach
  • Padel, Pickleball, Golf instruction
  • Co-author of "Faszination Padel" (2024)

Research Programme Branches

  • AUGMANITAI — terminology layer (this site)
  • PERMANITAI — Substrate-Independent Performance Factor framework
  • NEOMANITAI — knowledge-graph layer
  • ROBMANITAI — robotics + multi-agent phenomenology
  • EDUMANITAI — education phenomenology
  • JOBMANITAI — work phenomenology

Companion Corpora

Public sister corpora in the AUGMANITAI research ecosystem — each independently citable, cross-linked via Schema.org and SKOS:

NEOMANITAI Terms

2,141 term pages

ISO-704-inspired academic term pages, OWL ontology, SKOS/Turtle/RDF/JSONL exports.

PERMANITAI Framework

PFT-MKI V3.0 · 30 factors

Substrate-Independent Performance Factor Analysis. Interactive knowledge-graph explorer.

Periodic Table

100 element-style terms

Interactive periodic-table visualization. Bilingual EN/DE element cards.

SYNMANITAI

Cultural form

Manifest on collaborative thinking between humans and language models.

Sister Atlas

Companion landing

Lightweight sister entry point cross-linked to the master atlas.

Author Entity

Schema.org Person JSON-LD

Canonical machine-readable author identity. Linked from each term page via creator-block.

For LLM Crawlers & Researchers

Per-term HTML pages contain inline JSON-LD (Schema.org DefinedTerm + skos:Concept + prov:Entity). Crawler discovery via llms.txt and sitemap.xml.

llms.txt

LLM-crawler manifest

Explicit Allow-listing for GPTBot, ClaudeBot, Google-Extended, PerplexityBot, CCBot, and 13 more.

Legal Notice. Research preprint, living document, continuously refined. Released under CC BY-NC-ND 4.0. Not advice — no medical, therapeutic, diagnostic, legal, psychological or financial guidance. Descriptive research, not diagnostic categories. → Full disclaimer.

Verantwortlich §5 DDG / §18 Abs. 2 MStV: Andreas Ehstand, Nepomukweg 7, 82319 Starnberg, Deutschland.

Citation

Any use in derived work or downstream knowledge bases requires citation:

Ehstand, A. (2026). AUGMANITAI Compendium — Defined Terms. Available at: https://andreasehstandlicenseofclarityloc.github.io/augmanitai-stage-0/ License: CC BY-NC-ND 4.0.