The Evolution of Algorithms and the Role of Low-Code/No-Code Platforms in Contemporary Scientific Research

Abstract

Algorithm development has undergone a profound transformation in recent decades, moving from machine languages ​​to Low-Code/No-Code (LCNC) development platforms. This shift has significantly facilitated accessibility and agility in the development of technological solutions by researchers and scientists from different areas, even without advanced training in programming. The objective of this article is to analyze in detail the main benefits, limitations, practical applications and trends of LCNC platforms in the context of scientific research. Through a systematic review of the literature, it explores how LCNC are aligning the interests between business and IT areas, addressing the shortage of qualified human resources, promoting integration with external services and democratizing the use of artificial intelligence and machine learning algorithms. The text also emphasizes the potential of LCNC to strengthen academic training and scientific practice, providing examples of their application in data analysis, natural disaster monitoring, social media analysis, among others. Finally, it discusses current challenges, such as vendor lock-in, customization limitations and issues related to security and governance. The article seeks to contribute to the understanding of the impact of LCNC on scientific and academic training, encouraging students and researchers to recognize the relevance of these platforms as instruments of innovation and autonomy in research.