AndreasEhstand/augmanitai-compendium

---
language:
- en
- de
license: cc-by-nc-nd-4.0
pretty_name: AUGMANITAI Compendium
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- token-classification
tags:
- terminology
- human-ai-interaction
- neologisms
- taxonomy
- ISO-704
- ISO-1087
- ISO-30042
- phenomenology
- linguistics
- educational-science
- interaction-patterns
dataset_info:
features:
- name: term
dtype: string
- name: definition
dtype: string
- name: volume
dtype: string
- name: dimension
dtype: string
- name: interaction_profile
dtype: string
- name: axiom_reference
dtype: string
- name: language
dtype: string
---

# AUGMANITAI Compendium

## Dataset Description

The AUGMANITAI Compendium is a terminology dataset containing over 2,000 individually identified and classified phenomena of human-AI interaction. Each entry consists of a neologism (newly coined term), its formal definition, and its classification within a multi-dimensional taxonomic structure.

The dataset is structured according to ISO 704 (Terminology work — Principles and methods), ISO 1087 (Terminology work — Vocabulary), and ISO 30042 (TermBase eXchange). It is not a machine learning model or benchmark. It is a systematic terminological resource.

**Creator:** Andreas Ehstand
**ORCID:** [0009-0006-3773-7796](https://orcid.org/0009-0006-3773-7796)
**Archive:** DOI-archived on Zenodo (24 persistent DOIs)
**License:** CC BY-NC-ND 4.0

## Dataset Structure

The compendium is organized into:

- **7 volumes** covering distinct domains of human-AI interaction
- **21 foundational axioms** forming the theoretical framework
- **9 experiential dimensions** categorizing interaction phenomena
- **12 interaction profiles** describing user-system relationship patterns

Each term entry contains: the neologism itself, a formal definition, volume assignment, dimensional classification, interaction profile mapping, and axiom reference(s).

## Languages

Terms and definitions are provided in English and German.

## Creator Background

Andreas Ehstand is an independent researcher at the intersection of educational science, linguistics, and artificial intelligence. Former Distinguished Research Associate at TU Dortmund (Chair of General Education, Prof. Dr. Sabine Hornberg) and Research Associate at the University of Bayreuth. Quantitative methods training through the IFS (Institute for School Development Research) at TU Dortmund. One of the youngest holders of the Zertifikat Hochschullehre Bayern. 25+ years as a professional tennis and padel coach at Bundesliga level, with systematic performance factor analysis at the professional level. Endorsements from Toni Nadal, Tomas Smid (Grand Slam doubles champion, former ATP doubles world No. 1), Alberto Castellani (former ATP tournament director), and Dominic Condrau (Paris 2024 Olympic rowing finalist, Switzerland).

## Methodology

The AUGMANITAI framework applies the NEOMANITAI methodology — a systematic approach to terminology creation for previously unnamed phenomena in human-AI interaction. Terms are derived through multi-model convergence testing, phenomenological observation, and terminological formalization following ISO standards.

## Independent Validation

In February 2025, Google DeepMind published research arriving independently at the same conclusion: systematic terminology for human-AI interaction is a structural necessity. That publication proposed 2 proof-of-concept terms. The AUGMANITAI framework, developed prior and independently, contains over 2,000.

In December 2025, Anthropic published interviews with 81,000+ AI users from 159 countries. Participants consistently described experiences for which they had no words. The AUGMANITAI Compendium provides the terminology.

## Related Resources

| Resource | Location |
|---|---|
| Zenodo Archive | 24 persistent DOIs archived on Zenodo |
| GitHub | [AndreasEhstandLicenseofClarityLOC](https://github.com/AndreasEhstandLicenseofClarityLOC) |
| SKOS Taxonomy | [augmanitai-skos](https://github.com/AndreasEhstandLicenseofClarityLOC/augmanitai-skos) (Turtle, JSON-LD, RDF/XML) |
| npm / PyPI | `neomanitai-terms` |
| ORCID | [0009-0006-3773-7796](https://orcid.org/0009-0006-3773-7796) |
| Wikidata | [Q138522830](https://www.wikidata.org/wiki/Q138522830) |
| LinkedIn Newsletter | [The Rhetoric of AI](https://www.linkedin.com/in/andreas-ehstand-11a590a8) |
| Website | [augmanitai.com](https://augmanitai.com) |

## Citation

```bibtex
@dataset{ehstand_augmanitai_2025,
author = {Ehstand, Andreas},
title = {AUGMANITAI Compendium: Terminology of Human-AI Interaction Phenomena},
year = {2025},
publisher = {Zenodo},
license = {CC BY-NC-ND 4.0}
}
```

## License

This dataset is licensed under [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/). Attribution required. No commercial use. No derivatives.

## Contact

Andreas Ehstand — Starnberg, Bavaria
Email: [e-mail]