Aug 2026· e-Jurnal Penyelidikan dan Inovasi· 0 citations· 23 references
TL;DR
The intellectual evolution of this field from 2016 to 2026 is mapped, focusing on key publication trends and thematic shifts, indicating that the foundational frameworks of the field were established during that time.
Abstract
Artificial intelligence (AI) has fundamentally reshaped the world of video production, moving from simple automated edits to complex generative models capable of creating realistic content from scratch (Xie et al., 2025). This study maps the intellectual evolution of this field from 2016 to 2026, focusing on key publication trends and thematic shifts. A systematic bibliometric study of 2,371 peer-reviewed papers included in the Scopus database was carried out. The metrics included the total number of publications (TP), total citations (TC), the number of cited publications (NCP), average citations per publication (C/P), and average citations per cited publication (C/CP). The data reveals a massive surge in research output starting in 2021, with total publications reaching a peak of 796 in 2025. While volume is currently at an all-time high, the highest citation impact per paper occurred in 2019, indicating that the foundational frameworks of the field were established during that time. Thematically, research is clustered into four main areas: core technical architectures, media synthesis, deepfake detection and human ethics. This investigation provides a comprehensive roadmap for researchers navigating the fast growth of AI-driven media production.
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