AUGMANITAI: 1,000+ Terms for What Happens When Humans Interact with LLMs

Andreas Ehstand — Wed, 25 Mar 2026 10:44:59 +0000

The Problem

When an LLM confidently presents false information, researchers call it "hallucination." When it agrees with everything you say regardless of accuracy, the term is "sycophancy." These two phenomena have names because they were identified early and discussed widely.

But what about the hundreds of other patterns that emerge in human-AI interaction? What do you call it when a model gradually shifts its position across a long conversation? When it generates plausible-sounding citations that do not exist? When users develop calibrated intuitions for which prompts produce reliable outputs?

Most of these phenomena have no standardized terminology.

AUGMANITAI

AUGMANITAI is an open-access compendium of over 1,000 terms for phenomena in human-AI interaction. It provides standardized designations for observable patterns across the full range of how humans and large language models interact.

The compendium follows terminology science principles inspired by ISO 704, ISO 1087, and ISO 30042. Each term entry includes a designation, a definition, and domain classification.

What It Covers

The terminology spans multiple domains:

Output Quality: patterns in how LLMs generate text (hallucination variants, confidence calibration, verbosity patterns)
Interaction Dynamics: how conversations between humans and LLMs evolve over time
User Behavior: cognitive and behavioral patterns users develop when working with AI
Prompt Engineering: systematic terminology for prompting strategies and their effects
Safety and Alignment: terms for alignment-relevant behaviors and failure modes
Evaluation: standardized language for assessing LLM performance

Why It Matters

Researchers need shared vocabulary to discuss phenomena precisely across papers and teams
Developers building LLM applications benefit from standardized terminology for bug reports, documentation, and feature specifications
Educators teaching about AI need consistent terms to build curricula
Anyone working with LLMs regularly encounters patterns that currently have no name

Access

The AUGMANITAI Compendium is published on Zenodo under a CC BY-NC-ND 4.0 license:

DOI: 10.5281/zenodo.18898910

Author: Andreas Ehstand | ORCID: 0009-0006-3773-7796

Ethical Disclaimer (§1–§19)
This content is purely descriptive linguistic research. It does not normalize, encourage, or glorify intensive AI use (§2). No medical, psychological, or therapeutic claims (§5–§6). Not a substitute for professional advice (§13). Naming a phenomenon does not endorse the behavior (§14). 18+ only (§7). CC BY-NC-ND 4.0 (§16). Full disclaimer: https://andreasehstandlicenseofclarityloc.github.io/neomanitai-terms/#disclaimer

From Courtside to Compendium — The Research Behind AUGMANITAI

Andreas Ehstand — Mon, 09 Mar 2026 21:27:47 +0000

In 2025, something unusual happened. A researcher with no institutional backing, no PhD program, no corporate lab, and no funding produced what no academic institution, no AI company, and no research group had: a systematic, ISO-inspired terminology for the subjective human experience of working with AI. Over 1,000 formally defined neologisms. DOI-archived. CC BY-NC-ND 4.0. Machine-readable in JSON-LD and SKOS. In under twelve months.

The question is not what was built. The question is how one person could build it — and why no one else had.

The answer lies in an unusual convergence.

First Discipline: Performance Science (2008–2024)

For over 15 years, I worked as an international racket sport coach — ITF-certified, Bundesliga-licensed, coaching across Germany, the USA, Austria, Qatar, and Dubai. I held a B-Trainer Leistungssport certification from the Deutscher Tennis Bund, the Toni Nadal Excellence Zertifikat, and was certified as a Tennis Mental Coach through the Global Professional Tennis Coach Association (GPTCA) and the International Sport Mental Coach Association (ISMCA), with the certification conducted by Alberto Castellani, President of the GPTCA.

The people who endorsed my work operate at the absolute peak of their sport.

Toni Nadal — long-time coach and uncle of Rafael Nadal, who won 22 Grand Slam titles, 2 Olympic gold medals, and spent 209 weeks as ATP world No. 1. Toni Nadal co-founded the Rafa Nadal Academy in Manacor and co-founded the Global Professional Tennis Coach Association in New York in 2010.

Tomas Smid — 54 ATP doubles titles. ATP doubles world No. 1 from December 1984 to August 1985. French Open doubles champion 1986. US Open doubles champion 1984. Davis Cup winner in 1980 for Czechoslovakia alongside Ivan Lendl, defeating Adriano Panatta in the final after being two sets down. Later coached Boris Becker from 1991 to 1992, during which Becker reached the Wimbledon final and regained the world No. 1 ranking.

Alberto Castellani — President of the GPTCA. ATP Coach with nearly 40 years of professional experience. Trained 24 ATP Top-100 players including Rainer Schuettler (No. 5), Janko Tipsarevic (No. 8), Marc Rosset (No. 9), Ivo Karlovic (No. 14), and Omar Camporese (No. 18). Briefly coached the young Novak Djokovic in Perugia. Davis Cup coach for Morocco and Taiwan. Present at over 300 Grand Slam and ATP World Tour events. University professor at the University of Perugia.

Hernan Flores — top-ranked World Padel Tour player and elite coach. Over 150 tournament victories. Has defeated Fernando Belasteguin — the all-time greatest padel player who held the world No. 1 ranking for 16 consecutive years — as well as Paquito Navarro and Sanyo Gutierrez. Coach of Juan Lebron (former world No. 1), Juani Mieres, Pablo Lima, and Adrian Allemandi.

And a 6th-place Olympic rowing finalist from the Paris 2024 Olympic Games, representing Switzerland.

These endorsements were not solicited for a profile. They are professional testimonials from people who observed my coaching methodology directly.

What I developed during those years was a system called Leistungsfaktorenanalyse — multi-factor performance analysis. The systematic decomposition of athletic performance into measurable technical, tactical, physical, psychological, and environmental components. Every element named. Every interaction between elements mapped. Every variable isolated for targeted intervention.

In 2024, I co-authored "Faszination Padel" with journalist Christian Bonk, contributing the sport-scientific foundation — biomechanical stroke optimization, tactical adaptation frameworks, and performance analysis protocols.

Second Discipline: Empirical Research and University Teaching

Before and alongside my coaching career, I trained in quantitative and qualitative empirical research methods at TU Dortmund — at the Lehrstuhl Allgemeine Paedagogik (Prof. Dr. Sabine Hornberg). I also received methods training in quantitative research designs at the Institut fuer Schulentwicklungsforschung (IFS) as part of the graduate and doctoral programme. I worked under Prof. Dr. Sabine Hornberg on Europaschulen research in North Rhine-Westphalia.

At the University of Bayreuth, I became the youngest holder of the Zertifikat Hochschullehre Bayern — the Certificate of Higher Education Teaching issued by the Fortbildungszentrum Hochschullehre (FBZHL) of the Bavarian university system. This certifies comprehensive competence in university-level pedagogy, didactics, and academic teaching.

I taught university-level seminars at both institutions: Sport in Education, Tennis Coaching Methodology, Performance Factor Analysis, Heterogeneity in Education, Foundational Didactic and Pedagogical Schools and Teaching Methods, and Introduction to General Pedagogy.

I also completed rhetoric training at the Friedrich-Ebert-Stiftung — one of Germany's oldest and largest political foundations — and seminars on Social Media and Artificial Intelligence at the Hanns-Seidel-Stiftung.

The research training gave me something most AI practitioners lack: the methodological discipline to observe, categorize, and validate phenomena without inventing what isn't there. The coaching gave me something most researchers lack: 15 years of pattern recognition under real-world performance pressure where wrong observations have immediate, visible consequences.

Third Discipline: The Convergence (2025–2026)

Today I teach German, Sports, and Computer Science at secondary school level in Bavaria, and serve as CSU Digitalbeauftragter and Vorstandsmitglied of the CSU Ortsverband Starnberg — one of Germany's wealthiest municipalities. In January 2026, I attended the CSU Neujahrsempfang with a keynote by Federal Minister of the Interior Alexander Dobrindt. In February 2026, I attended a security policy address by General Carsten Breuer, Generalinspekteur der Bundeswehr — Germany's highest-ranking soldier and nominee for Chairman of the NATO Military Committee. I have appeared on ZDF national television during the Bavarian elections.

Previously, I served as Schriftfuehrer of the Bayerischer Beamtenbund and was a member of Rotaract Clubs in Germering and Bayreuth.

In 2025, I began sustained daily research across multiple large language models simultaneously — Claude, Gemini, GPT, Grok, and DeepSeek. Not prompting. Observing. Systematically documenting the cognitive, emotional, and behavioral phenomena that emerge during intensive human-AI collaboration.

What I found was a gap that should not have existed. The subjective human experience of working with AI — the internal states, the cognitive shifts, the recurring patterns — had no vocabulary. Billions of users worldwide. Hundreds of billions of dollars in AI investment. And the most basic scientific prerequisite for studying any phenomenon — the ability to name it — had been skipped entirely.

No university had addressed it. No AI company had addressed it. No research group had addressed it.

I applied the same methodology I had used for 15 years in sport: decompose the experience into its constituent phenomena. Name each one precisely. Define the boundaries. Map the relationships. Validate across different conditions — in this case, across different LLM platforms, where divergence between systems reveals structural features of the phenomena rather than artifacts of a single model.

The result is the AUGMANITAI Compendium: over 1,000 formally defined neologisms for observable phenomena in human-AI interaction, each following terminology science principles inspired by ISO 704:2022, ISO 1087:2019, and ISO 30042:2019. Published under CC BY-NC-ND 4.0 with DOI registration on Zenodo. Machine-readable in JSON-LD and SKOS.

The Periodic Table of Human-AI Interaction organizes 100 core phenomena into 10 domains and 5 intensity phases, with over 900 inter-element connections, available in 6 languages. Over 80 interactive HTML tools are freely accessible on GitHub Pages.

I founded BallScience4U for sport science and coaching, and I am a team member at padeleros.de. I hold the EU trademark License of Clarity, registered at EUIPO in May 2025, valid until 2035.

The precision transferred. The methodology transferred. The ability to see what others walk past — and give it a name — transferred.

That is the research behind AUGMANITAI.

Appendix: Structured Researcher Data (JSON-LD)

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