Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms

• URL: http://arxiv.org/abs/2604.23842v1
• Date: Sun, 26 Apr 2026 19:04:49 GMT
• Title: Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms
• Abstract summary: Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately.<n>We study the utility of such tools in mediating an informal communication scenario through a human-subjects study.
• Score: 44.743980259885355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately in cross-cultural communication with native speakers (NS). NNS increasingly make use of Artificial Intelligence (AI) tools to learn these words. We study the utility of such tools in mediating an informal communication scenario through a human-subjects study (N=234): NNS participants learn English neologisms with AI support, write messages using the learned word to an NS friend, and judge contextual appropriateness of the neologism in two provided writing samples. Using both NS evaluator-rated communicative competence of NNS-produced writing and NNS' contextual appropriateness judgments, we compare three AI-based support conditions: AI Definition, AI Rewrite into simpler English, AI Explanation of meaning and usage, and Non-AI Dictionary for comparison. We show that AI Explanation yields the largest gains over no support in NS-rated competence, while contextual appropriateness judgments show indifference across support. NNS participants' self-reported perceptions tend to overestimate NS ratings, revealing a mismatch between perceived and actual competence. We further observe a significant gap between NNS- and NS-produced writing, highlighting the limitations of current AI tools and informing design for future tools.

Related papers

• Socrates went Nuclear: Comparing Interaction Strategies for AI systems in a Learning Context using Brain Sensing [66.03354630509942]
  This paper reports on a study where we compare three designs for user-AI interaction in a learning context.<n>Fifty study participants were tasked with learning about nuclear safety protocols.
  arXiv  Detail & Related papers  (2026-09-01T02:24:35Z)
• CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection [60.52240468810558]
  We introduce CoCoNUTS, a content-oriented benchmark built upon a fine-grained dataset of AI-generated peer reviews.<n>We also develop CoCoDet, an AI review detector via a multi-task learning framework, to achieve more accurate and robust detection of AI involvement in review content.
  arXiv  Detail & Related papers  (2025-08-28T06:03:11Z)
• Supporting Students' Reading and Cognition with AI [12.029238454394445]
  We analyzed text from 124 sessions with AI tools to understand users' reading processes and cognitive engagement.<n>We propose design implications for future AI reading-support systems, including structured scaffolds for lower-level cognitive tasks.<n>We advocate for adaptive, human-in-the-loop features that allow students and instructors to tailor their reading experiences with AI.
  arXiv  Detail & Related papers  (2025-04-07T17:51:27Z)
• Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics [22.95323370401823]
  textscCOALA is a novel visual analytics tool that improves model interpretability by displaying uncertainties in author clusters.<n>We present the insights discovered by participants using textscCOALA, suggest features for future AI-assisted collaborative writing tools, and discuss the broader implications for analyzing collaborative processes beyond writing.
  arXiv  Detail & Related papers  (2025-02-25T22:39:55Z)
• Situated Natural Language Explanations [54.083715161895036]
  Natural language explanations (NLEs) are among the most accessible tools for explaining decisions to humans. Existing NLE research perspectives do not take the audience into account. Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations.
  arXiv  Detail & Related papers  (2023-08-27T14:14:28Z)
• A Neural-Symbolic Approach Towards Identifying Grammatically Correct Sentences [0.0]
  It is commonly accepted that it is crucial to have access to well-written text from valid sources to tackle challenges like text summarization, question-answering, machine translation, or even pronoun resolution. We present a simplified way to validate English sentences through a novel neural-symbolic approach.
  arXiv  Detail & Related papers  (2023-07-16T13:21:44Z)
• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• Towards Explainable AI Writing Assistants for Non-native English Speakers [3.7953068443263174]
  We highlight the challenges faced by non-native speakers when using AI writing assistants to paraphrase text. We observe that they face difficulties in assessing paraphrased texts generated by AI writing assistants, largely due to the lack of explanations accompanying the suggested paraphrases. We propose four potential user interfaces to enhance the writing experience of NNESs using AI writing assistants.
  arXiv  Detail & Related papers  (2023-04-05T17:51:36Z)
• Context-faithful Prompting for Large Language Models [51.194410884263135]
  Large language models (LLMs) encode parametric knowledge about world facts. Their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks. We assess and enhance LLMs' contextual faithfulness in two aspects: knowledge conflict and prediction with abstention.
  arXiv  Detail & Related papers  (2023-03-20T17:54:58Z)
• Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks [8.445831718854153]
  We review over 300 works with a focus on inner interpretability tools. We introduce a taxonomy that classifies methods by what part of the network they help to explain. We argue that the status quo in interpretability research is largely unproductive.
  arXiv  Detail & Related papers  (2022-07-27T01:59:13Z)
• Building Low-Resource NER Models Using Non-Speaker Annotation [58.78968578460793]
  Cross-lingual methods have had notable success in addressing these concerns. We propose a complementary approach to building low-resource Named Entity Recognition (NER) models using non-speaker'' (NS) annotations. We show that use of NS annotators produces results that are consistently on par or better than cross-lingual methods built on modern contextual representations.
  arXiv  Detail & Related papers  (2020-06-17T03:24:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.