# On the Same Page: Dimensions of Perceived Shared Understanding in Human-AI Interaction

arXiv: https://arxiv.org/abs/2505.20068 · HTML: https://mlsanity.com/paper/2505.20068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 55 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 3 of 3)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Debate Over Understanding in AI's Large Language Models](https://mlsanity.com/paper/2210.13966/) (2022)
- [When Will AI Exceed Human Performance? Evidence from AI Experts](https://mlsanity.com/paper/1705.08807/) (2017)

## Built on by (most cited first, 1 of 1)

- [Private Etymology: Designing Relational Reuse of Shared Symbols in Long-Term Human-AI Interaction](https://mlsanity.com/paper/2608.08443/) (2026)

## Abstract (arXiv)

Shared understanding plays a key role in the effective communication in and performance of human-human interactions. With the increasingly common integration of AI into human contexts, the future of personal and workplace interactions will likely see human-AI interaction (HAII) in which the perception of shared understanding is important. Existing literature has addressed the processes and effects of PSU in human-human interactions, but the construal remains underexplored in HAII. To better understand PSU in HAII, we conducted an online survey to collect user reflections on interactions with a large language model when it sunderstanding of a situation was thought to be similar to or different from the participant's. Through inductive thematic analysis, we identified eight dimensions comprising PSU in human-AI interactions: Fluency, aligned operation, fluidity, outcome satisfaction, contextual awareness, lack of humanlike abilities, computational limits, and suspicion.