Skip to content

From Emotion to Expression: The AL–DL–CL Framework for Understanding Communication in the Age of AI and Digital Media

Dec 2026 · International Journal of Civilizations Studies & Tolerance Sciences · 0 citations

Abstract

Rapid progress in digital media and artificial intelligence is transforming human communication, facilitating the rapid exchange of messages and minimizing the chances of reflective interpretation. Existing theories in linguistics, psychology, and cultural studies address the structural, cognitive, or social aspects of language, yet fail to explain how emotional processing, internal linguistic structuring, and culturally mediated communication function as a coherent, dynamic system. Through this model, the authors present the AL–DL–CL framework that explores how three layers are engaged in a process that results in communication, through a recursive interaction between the Automatic Layer (AL), the Deliberate Layer (DL), and the Cultural Language Layer (CL). In such a process, the affective or unconscious processes in AL are consistently reframed into structured internal representations via DL, and through CL, and translated back into culturally-expressed language serves as a feedback mechanism, with a tendency to modify cognition and emotional response. Based on a multi-method exploratory approach of sequential cultural transmission, clinical case study analysis, and group-based validation, the results indicate that language is not so much a static container for meaning as a process of reconstruction that is molded by the dynamic interaction between emotion, cognition, and culture. This model also demonstrates how digitally mediated communication can amplify AL-directed reaction and limit DL-level thinking, which increases the potential for misinterpretation, polarization, or misinformation. The AL–DL–CL framework provides guidance in a unified theoretical perspective around language emergence, responsible AI design and digital literacy and intercultural discourse based on the continuous transformation system of communication, and not on the linear encoding.

Read PDF

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.