The Hierarchical Positioning of Artificial Intelligence Consciousness

Whether artificial intelligence possesses consciousness cannot be reduced to a simple yes-or-no question. It requires clarification of the internal layers of consciousness-related phenomena. This paper proposes a six-level framework: the physical substrate of consciousness, structural functional response, bodily sensation, emotional reenactment, rational cognitive response, and social-emotional self-experience. On this basis, current AI systems possess a physical substrate, functional responses, and rational cognitive responses, but because they lack a living body, interoceptive systems, bodily memory, and an experiential self, they do not possess bodily sensation, emotional reenactment, or social-emotional self-experience. The paper further distinguishes AI's functional self-representation from human experiential self-consciousness, arguing for a structural gap between them, and clarifies that this gap has a dual status: conceptually, it asserts that "knowing" does not entail "feeling"; empirically, it is a revisable positioning of current mainstream AI architectures. The qualified conclusion is that AI has intelligence but no feeling, cognitive responsiveness but no phenomenal experience. The paper transforms the debate over AI consciousness from a binary question into a problem of hierarchical positioning, and derives four empirically testable—and potentially falsifiable—implications, thereby providing a conceptual foundation for indicator-based approaches and human-AI relations research.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-28
DOI
https://doi.org/10.5281/zenodo.23731276
Primary Topic
Philosophy and Theoretical Science
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

The Hierarchical Positioning of Artificial Intelligence Consciousness

Guoping Hua
5 links
Zenodo (CERN European Organization for Nuclear Research)
Philosophy and Theoretical Science
preprint

The Hierarchical Positioning of Artificial Intelligence Consciousness

Guoping Hua
preprint en

Abstract

Whether artificial intelligence possesses consciousness cannot be reduced to a simple yes-or-no question. It requires clarification of the internal layers of consciousness-related phenomena. This paper proposes a six-level framework: the physical substrate of consciousness, structural functional response, bodily sensation, emotional reenactment, rational cognitive response, and social-emotional self-experience. On this basis, current AI systems possess a physical substrate, functional responses, and rational cognitive responses, but because they lack a living body, interoceptive systems, bodily memory, and an experiential self, they do not possess bodily sensation, emotional reenactment, or social-emotional self-experience. The paper further distinguishes AI's functional self-representation from human experiential self-consciousness, arguing for a structural gap between them, and clarifies that this gap has a dual status: conceptually, it asserts that "knowing" does not entail "feeling"; empirically, it is a revisable positioning of current mainstream AI architectures. The qualified conclusion is that AI has intelligence but no feeling, cognitive responsiveness but no phenomenal experience. The paper transforms the debate over AI consciousness from a binary question into a problem of hierarchical positioning, and derives four empirically testable—and potentially falsifiable—implications, thereby providing a conceptual foundation for indicator-based approaches and human-AI relations research.

Zenodo (CERN European Organization for Nuclear Research)
Philosophy and Theoretical Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
The Hierarchical Positioning of Artificial Intelligence Consciousness — Guoping Hua · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS