Skip to content

ChatGPT-Mediated Revised Intercultural Communication Needs (RICN) Questionnaire

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This questionnaire was developed as an AI-mediated extension of the Revised Intercultural Communication Needs (RICN) Questionnaire developed by Ghorbani (2025). The instrument aims to examine learners' perceptions of ChatGPT as an intercultural mediator in English language learning contexts. The ChatGPT-Mediated Revised Intercultural Communication Needs (RICN) Questionnaire was developed to examine learners’ perceptions of ChatGPT as an intercultural mediator within English language learning and intercultural communication contexts. Building on the Revised Intercultural Communication Needs (RICN) framework, this instrument extends linguistic, affective/emotional, and cultural dimensions to generative artificial intelligence-mediated communication environments. The questionnaire consists of four dimensions: ChatGPT-Mediated Linguistic Needs, ChatGPT-Mediated Affective/ Emotional Needs, ChatGPT-Mediated Cultural Needs, and Perceived Role of ChatGPT as an Intercultural Mediator. The instrument includes 29 Likert-scale items designed for research applications in applied linguistics, second language acquisition, intercultural communication, and AI-assisted language learning.

View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9
#artificial intelligence Review Oct 2025

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...

Ranjan Sapkota, Manoj Karkee · 112 citations · ⚡10

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.