Sep 2026· Communication and Change· Vol 2· 0 citations· 38 references
TL;DR
This study identifies recurring interpretive patterns, including the privileging of emotional amplification over national identity, the emergence of synthetic performances that appear polished yet affectively hollow, pragmatic acceptance of algorithmic personalization, and persistent trade-offs between global scale and cultural nuance.
Abstract
The global diffusion of Hallyu, once understood primarily through the lens of soft power and national branding, is increasingly shaped by algorithmic infrastructures that mediate cultural visibility, affective resonance, and symbolic legitimacy. Recommendation systems, dubbing technologies, generative idols, and personalization engines now determine how Korean culture circulates and is received across platforms. This study conceptualizes Algorithmic Hallyu as a case of cultural epistemic infrastructure, illustrating how AI systems reconfigure not only the distribution of cultural products but also the meanings and values attached to them. Drawing on analysis of publicly articulated discourse and AI-assisted qualitative simulation, the study identifies recurring interpretive patterns, including the privileging of emotional amplification over national identity, the emergence of synthetic performances that appear polished yet affectively hollow, pragmatic acceptance of algorithmic personalization, and persistent trade-offs between global scale and cultural nuance. The analysis argues that Hallyu can no longer be understood solely as a cultural export of Korea, but rather as a platformed cultural formation increasingly governed by algorithmic systems. Situating Algorithmic Hallyu within broader debates on algorithmic culture, epistemic authority, and platform governance, the study highlights how AI mediates both truth and culture in ways that challenge established notions of authenticity, sovereignty, and cultural legitimacy.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026