Leveraging AI and Automation in Research Project Planning and Execution: A Systematic Literature Review

Amina Saleh Omar, Asnath Nyachiro and Musau Obadiah
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Amina Saleh Omar: Department of Computing and Informatics, JomoKenyatta University
Asnath Nyachiro: Department of Computing and Informatics, JomoKenyatta University
Musau Obadiah: Department of Computing and Informatics, JomoKenyatta University

International Journal of Research and Scientific Innovation, 2025, vol. 12, issue 15, 486-498

Abstract: Artificial intelligence (AI) and automation have transformed research project management by enhancing predictive accuracy, decision-making, and resource allocation. This systematic literature review explores the application of AI-driven models and automation tools in improving research project planning and execution. Machine learning (ML) models, including Random Forest, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and XGBoost, have demonstrated improved cost estimation, scheduling, and risk assessment. Deep learning models such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have enabled dynamic scheduling and real-time decision-making. Hybrid AI models, combining decision trees, genetic algorithms, fuzzy logic, and Bayesian networks, have enhanced flexibility and risk mitigation. Automation tools like Slack AI, Microsoft Power Automate, Tableau AI, and Apache Airflow have streamlined task scheduling, compliance tracking, and progress monitoring, reducing administrative workload and improving project execution efficiency. Despite these advancements, challenges such as algorithmic bias, lack of transparency, and limited accessibility for smaller institutions remain significant barriers to AI adoption. The paper identifies gaps in AI applications in areas such as stakeholder management, communication, and procurement. Future research should focus on enhancing AI model interpretability, improving scalability across industries, and developing structured AI frameworks capable of integrating real-world data for continuous improvement in project management.

Date: 2025
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