Role of Machine Learning in Low-Code/No-Code Platforms - A Systematic Literature Review of Opportunities and Challenges

Aug 5, 2026

This thesis examines how machine learning (ML) is applied within low-code/no-code (LC/NC) development platforms, enabling end users to build applications more easily. A systematic literature review synthesizes recent research to identify opportunities, advantages, benefits, and the primary challenges and obstacles of integrating ML into LC/NC workflows. Findings indicate improvements in automation, decision-making, and user experience, while also highlighting risks such as model transparency, data quality, and scalability. It further outlines research gaps and future directions for stronger ML integration.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages49

Reading Time2 h 3 min

AccessFree to download

Introduction

Motivation and Importance of ML Integration in LC/NC Platforms

Problem Statement and Research Gap

Research Objectives and Questions

Evaluation Focus: Benefits, Opportunities, Risks and Challenges

Thesis Structure

Research Method

Research Design

Data Sources and Search Queries

Inclusion and Exclusion Criteria

Literature Collection Process

Data Extraction, Quality and Relevance Assessment

Data Analysis

Threats to Validity

Results

Integration Patterns

Mapping the Review

Role of Machine Learning in Low-Code/No-Code Platforms: A Systematic Literature Review of Opportunities and Challenges Lappeenranta–Lahti University of Technology LUT Master’s Programme in Software Product Management and Business, Master’s thesis 2025 Javad Khaleghi Hozhabrasa Examiner: Associate Professor, Damian Kedziora and Researcher, Nasren Azad ABSTRACT Lappeenranta–Lahti University of Technology LUTLUT School of Engineering Sciences Master’s Program in Software Product Management and Business Javad Khaleghi Hozhabrasa Role of Machine Learning in Low-Code/No-Code Platforms: A Systematic Literature Review of Opportunities and Challenges Master’s thesis 2025 49 pages, 2 figures and 8 tables Examiner: Associate Professor, Damian Kedziora and Researcher, Nasren Azad Keywords: Low-Code (LC), No-Code (NC), Machine Learning (ML), LC/NC Platforms, Systematic Literature Review (SLR) This thesis investigates how machine learning (ML) is used in low-code/no-code (LC/NC) platforms. These platforms allow end users to develop applications more easily. It presentsa systematic literature review of recent studies to explore the main opportunities, advantages, benefits, challenges and obstacles of combining ML with LC/NC development. The results represents that ML can improve automation, decision-making, and user experience in LC/NC tools. However, there exists challenges related to model transparency, data quality, and system scalability. Moreover, this thesis discusses research gaps and possible future directions to improve ML integration into LC/NC platforms. ACKNOWLEDGEMENTS I am truly thankful to my supervisors, Damian Kedziora and Nasren Azad, for their continuous assistance, kind guidance, valuable suggestions, and constructive comments that helped me improve my thesis. SYMBOLS AND ABBREVIATIONS AI EC LC LLM ML NC NLP PRISMA RPA QA RQ SLR Artificial Intelligence Exclusion Criteria Low-Code Large Language Models Machine Learning No-Code Natural Language Processing Preferred Reporting Items in a Systematic Review and Meta-Analysis Robotic Process Automation Quality Assurance Research Question Systematic Literature Review TABLE OF CONTENTS ABSTRACT........................................................................................................................... 2 ACKNOWLEDGEMENTS................................................................................................... 3 SYMBOLS AND ABBREVIATIONS..................................................................................4 TABLE OF CONTENTS....................................................................................................... 5 DECLARATIONS ................................................................................................................. 7 1 Introduction .................................................................................................................... 8 1.1 Motivation and Importance of ML Integration in LC/NC Platforms ....................9 1.2 Problem Statement and Research Gap ................................................................. 10 1.3 Research Objectives and Questions ..................................................................... 12 1.4 Evaluation Focus: Benefits, Opportunities, Risks and Challenges...................... 13 1.5 Thesis Structure ................................................................................................... 14 2 Research Method .......................................................................................................... 15 2.1 Research Design .................................................................................................. 15 2.2 Data Sources and Search Queries ........................................................................ 15 2.2.1 Data Sources ...............................................................

FAQs

What is the main focus of the thesis?

The thesis investigates how machine learning is used in low-code/no-code platforms and evaluates the opportunities, advantages, benefits, and obstacles of this integration.

What benefits does ML integration bring to LC/NC platforms?

The results indicate that ML can improve automation, decision-making, and user experience in LC/NC tools.

What challenges are identified when combining ML with LC/NC development?

Key challenges include model transparency, data quality, and system scalability, which can limit effective adoption.

Aug 5, 2026

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