# What Do You Mean? Exploring How Humans and AI Interact with Symbols and Meanings in Their Interactions

arXiv: https://arxiv.org/abs/2510.05378 · HTML: https://mlsanity.com/paper/2510.05378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [Conversational AI: The Science Behind the Alexa Prize](https://mlsanity.com/paper/1801.03604/) (2018)
- [Design Principles for Generative AI Applications](https://mlsanity.com/paper/2401.14484/) (2024)
- [Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems](https://mlsanity.com/paper/2109.09904/) (2021)
- [From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning](https://mlsanity.com/paper/2505.17117/) (2025)
- [Mutual Theory of Mind for Human-AI Communication](https://mlsanity.com/paper/2210.03842/) (2022)
- [Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations](https://mlsanity.com/paper/2407.08983/) (2024)
- [Human-Robot Interaction in a Shared Augmented Reality Workspace](https://mlsanity.com/paper/2007.12656/) (2020)
- [eXtended Artificial Intelligence: New Prospects of Human-AI Interaction Research](https://mlsanity.com/paper/2103.15004/) (2021)
- [Designing Interfaces for Human-Computer Communication: An On-Going Collection of Considerations](https://mlsanity.com/paper/2309.02257/) (2023)
- [Emergence of Symbols in Neural Networks for Semantic Understanding and Communication](https://mlsanity.com/paper/2304.06377/) (2023)
- [Cognitively Inspired Components for Social Conversational Agents](https://mlsanity.com/paper/2311.05450/) (2023)
- [A Bibliometric Analysis of Trust in Conversational Agents over the Past Fifteen Years](https://mlsanity.com/paper/2408.16837/) (2024)

## Abstract (arXiv)

Meaningful human-AI collaboration requires more than processing language; it demands a deeper understanding of symbols and their socially constructed meanings. While humans naturally interpret symbols through social interaction, AI systems often miss the dynamic interpretations that emerge in conversation. Drawing on Symbolic Interactionism theory, we conducted two studies to investigate how humans and AI co-construct symbols and their meanings. Findings provide empirical insights into how humans and conversational AI agents collaboratively shape meanings during interaction. We show how participants shift their initial definitions of meaning in response to the symbols and interpretations suggested by the conversational AI agents, especially when social context is introduced. We also observe how participants project their personal and social values into these interactions, refining meanings over time. These findings reveal that shared understanding does not emerge from mere agreement but from the bi-directional exchange and reinterpretation of symbols, suggesting new paradigms for human-AI interaction design.