Why Human-AI Interaction Needs Its Own Terminology

 Every scientific discipline develops its own vocabulary. Medicine has its Latin roots, law has its precedent-based terminology, and computer science has built an entire lexicon over decades. But human-AI interaction — one of the fastest-growing areas of daily technology use — still lacks a systematic terminology.

Consider a few examples. When a large language model generates confident but factually incorrect output, the research community has settled on the term "hallucination." When an LLM adjusts its responses to match what the user seems to want to hear rather than what is accurate, this is called "sycophancy." These terms exist because researchers needed words to describe observable, recurring phenomena.

But what about the hundreds of other patterns that occur when humans interact with AI systems? What do we call it when a user gradually adjusts their communication style to match an LLM's output patterns? Or when someone develops a preference for one model's response style over another, independent of factual accuracy? Or when a user asks the same question in slightly different ways to test for response consistency?

These phenomena exist. They are observable and reproducible. But most of them do not yet have names.

This is the problem that the AUGMANITAI framework addresses. AUGMANITAI is an open-access compendium of over 1,000 terms describing phenomena in human-AI interaction. It draws on terminology science principles inspired by ISO standards (ISO 704, ISO 1087, ISO 30042) and is published under a CC BY-NC-ND 4.0 license on Zenodo.

The compendium covers areas such as LLM output patterns, user adaptation behaviors, interaction dynamics, prompt-response relationships, and cross-model behavioral differences. Each term follows a structured format with a definition, context of use, and classification.

The goal is descriptive, not prescriptive: to provide researchers, developers, and users with a shared vocabulary for discussing what actually happens when humans and AI systems interact. Standardized terminology helps people find relevant research, compare observations across contexts, and communicate more precisely about their experiences.

The full compendium is available as an open-access dataset:

https://doi.org/10.5281/zenodo.18898910

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