Low-Code Mobile Application Builder with AI-Assisted Features using Flutter & Firebase

Main Article Content

Dr.V.Seedha Devi

Abstract

The increasing demand for rapid application development has highlighted the limitations of traditional coding-intensive approaches, particularly in terms of development time, complexity, and accessibility. This project presents the design and implementation of a Low-Code Application Builder, a platform developed to streamline and simplify the process of creating fully functional applications with minimal manual coding. The system provides an intuitive visual interface that enables users to design applications using drag-and-drop components, predefined templates, and configurable elements. It supports key functionalities such as dynamic user interface generation, real-time preview, backend integration, and database connectivity within a unified development environment. This approach allows both technical and non-technical users to efficiently build, modify, and deploy applications.. By incorporating reusable components and automated workflows, the system significantly reduces development effort while maintaining consistency and reliability. In conclusion, the proposed Low-Code Application Builder enhances development efficiency, minimizes technical barriers, and accelerates the application lifecycle, making it a practical solution for modern software development requirements

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How to Cite

Low-Code Mobile Application Builder with AI-Assisted Features using Flutter & Firebase. (2026). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(3), 1001-1010. https://doi.org/10.15662/IJRPETM.2026.0903001

References

1. F. Sufi, “Algorithms in Low-Code-No-Code for Research Applications: A Practical Review,” Algorithms, vol. 16, no. 2, 2023.

2. Saravanan, M. (2026). Generative AI–Enabled Decision Intelligence: An Integrated Analytics and Autonomous Systems Framework for Cybersecurity and Retail Enterprises. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(1), 14-20.

3. Aarthi, K., Thirumoorthy, P., Tamizharasu, K., Manoja, R., Kalyanasundaram, P., & Rajasekar, M. (2025, September). Improved Network lifetime using Cluster based Power-Aware Balanced Routing Protocol for Device to Device Communication. In 2025 6th International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 1005-1010). IEEE.

4. Rengarajan, A. (2025). Cloud-Based AI-Driven Threat Detection Framework for Smart Grid Cybersecurity. International Journal of Future Innovative Science and Technology (IJFIST), 8(6), 16065.

5. Mathew, A. (2024). Cloud data sovereignty governance and risk implications of cross-border cloud storage. Information Systems Audit and Control Association.

6. Seedha Devi, V., Namitha, B., Divya Dharshini, J., & Livetha, K. (2026). A hybrid biometric and geo-fencing based smart attendance system. International Journal of Advanced Research in Computer Science and Technology (IJARCST), 9(3), 794–802. https://doi.org/10.15662/IJARCST.2026.0903002

7. V. Viswanadhapalli, “The Future of Intelligent Automation: How Low-Code/No-Code Platforms are Transforming AI Decisioning,” International Journal of Engineering and Computer Science, 2025.

8. Jaiswal, M., Manchattahalli, U. R., Devi, V. S., & Jacob, M. S. (2026, January). Predictive Analysis of Internet Use and Risk Among Children and Adolescents via Machine Learning. In 2026 Sixth International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies (ICAECT) (pp. 1-6). IEEE.

9. B. John, “Low-Code and No-Code Platforms: Accelerating Enterprise Software Development,” World Journal of Engineering and Technology, 2025.

10. Murugeshwari, B., Selvaraj, D., Sudharson, K., & Radhika, S. (2023). Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography. Intelligent Automation & Soft Computing, 35(1).

11. Seedha Devi, V., Nivedha, S., Harisha, V., Mol, D. R., & Janaranjini, J. R. (2026). Enhanced prediction of PCOS and PCOD using deep learning for early diagnosis and clinical risk stratification. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 9(3), 783–793.

12. Kesavan, R., Anuradha, T., Kalaiarasi, P., Jacob, M. S., Anand, A. J., & Devi, V. S. (2025, November). Deep Learning-Based Diagnostic Support System for Breast Tissue Classification Using Mammographic Images. In 2025 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies (INSPECT) (pp. 1-6). IEEE.

13. Gopalakrishnan, S., Dhinakaran, D., Raja, S. E., Raghavan, P., & Girija, M. S. (2026). Fusion-Driven Medical Image Encryption Framework with Entropy-Calibrated Control and Integrity Assurance. KSII Transactions on Internet & Information Systems, 20(2).

14. Mathew, A. (2025). Deep seek vs. ChatGPT: A deep dive into AI Language mastery. Int J Multidisciplinary Res, 7(1), 1-5.

15. Vimal, V. R., & Banerjee, J. S. (2025). Integrating PSO, GA, and ACO for Optimized ECG Feature Selection and Classification of Cardiac Disorders. SGS-Engineering & Sciences, 1(5).

16. G. L. Liwanag, R. Ebardo, and D. Cheng, “Low-Code and No-Code Development in the Era of Artificial Intelligence: A Systematic Review,” Data and Metadata, 2025.

17. H. El Kamouchi, M. Kissi, and O. El Beggar, “Low-Code/No-Code Development: A Systematic Literature Review,” IEEE Conference on Intelligent Systems, 2023.

18. N. Ahmed et al., “The Synergy of Low-Code/No-Code and AI/ML: Enhancing Intelligent Automation,” World Journal of Engineering and Technology, 2025.

19. Dhinakaran, D., Prathap, P. J., Selvaraj, D., Kumar, D. A., & Murugeshwari, B. (2022). Mining privacy-preserving association rules based on parallel processing in cloud computing. International Journal of Engineering Trends and Technology, 70(3), 284-294.

20. Seedha Devi, V., Kumar, M. D., & Kumar, C. A. (2026). Flutter-based SOS alert and location tracking application with volunteer assist and rescue. International Journal of Research and Applied Innovations (IJRAI), 9(3), 521–530. https://doi.org/10.15662/IJRAI.2026.0903003

21. A. C. Bock and U. Frank, “Low-Code Platform,” Business & Information Systems Engineering, 2021.

22. Y. Luo, P. Liang, C. Wang, M. Shahin, and J. Zhan, “Characteristics and Challenges of Low-Code Development,” ACM/IEEE ESEM, 2021.

23. Vimal, V. R. (2025). Next Generation Enterprise Architecture for SAP Cloud Systems Leveraging AI Driven Analytics and Hybrid Infrastructure. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 11174-11182.

24. Jacob, M. S., Rekha, V. B., Manchattahalli, U. R., & Devi, V. S. (2025, October). Enhancing the Performance of Energy-Harvesting Wireless Sensor Networks through Machine Learning-based Energy Prediction. In 2025 10th International Conference on Communication and Electronics Systems (ICCES) (pp. 1558-1563). IEEE.

25. IEEE, “Low-Code/No-Code Platforms and Modern ERP Systems,” IEEE Conference Publication, 2023.

26. Anbazhagan, K. (2025). Next-Generation Enterprise Cloud AI for Healthcare: Secure CNN Pipelines and Privacy Controls. International Journal of Future Innovative Science and Technology (IJFIST), 8(6), 15980.

27. IEEE, “Low-Code/No-Code Development: A Systematic Literature Review,” IEEE Conference, 2023.

28. Devi, V. S., Kumar, G. S., Jacob, M. S., Jeevitha, S., & Jegatheesan, A. (2025, October). Comparative Study of Traditional and Deep Learning Approaches for Human Ear-Based Biometric Identification. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 1-6). IEEE.

29. M. Desmond et al., “A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language,” arXiv, 2022.

30. Rajasekar, M. (2025). Risk-Aware Generative AI and Machine Learning Frameworks for Privacy-Preserving Banking and Trade Analytics over Cloud and 5G Networks. International Journal of Computer Technology and Electronics Communication, 8(4), 11078-11086.

31. Sugumar, R. (2025). Federated AI in Offline-First Mobile Health Architectures for Privacy-Preserving Clinical Intelligence. International Journal of Science, Research and Technology, 8(4), 14589-14600.

32. O. Ogundare et al., “No Code AI: Automatic Generation of Function Block Diagrams,” arXiv, 2023.

33. S. Curty, F. Härer, and H. G. Fill, “Blockchain Application Development Using Low-Code Platforms: A Survey,” arXiv, 2022.

34. Anujaa, T., Thajudeen Ali Ahamed, A. F., Baranwal, V., Thanikaiselvan, V., Subashanthini, S., Sivaranjani Devi, C., & Rengarajan, A. (2025). A lightweight multi round confusion-diffusion cryptosystem for securing images using a modified 5D chaotic system. Scientific Reports, 15(1), 31986.

35. Anbazhagan, K. (2025). AI Driven Zero Trust Security Model for Enterprise Data Protection and Intelligent Infrastructure Management. International Journal of Technology, Management and Humanities, 11(03), 101-107

36. Prabha, S. P., & Rengarajan, A. (2025). ENHANCING CLOUD RESOURCE ALLOCATION WITH VISION TRANSFORMER, DEEP REINFORCEMENT LEARNING, AND IMPROVED SHRIKE OPTIMIZATION ALGORITHM. Corrosion Management ISSN: 1355-5243, 35(2), 233-245.

37. Seedha Devi, V., Selvi, D., Uma Maheshwari, K., & Yuvashree, G. (2026). Food linker: A smart system for global waste reduction. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(3), 5012–5021. https://doi.org/10.15662/IJEETR.2026.0803002

38. Mathew, A., Jackson, E., & Tobesman, A. (2025). Evaluating the Efficacy of WPA3 against Advanced Attacks: A Comparative Analysis with WPA2 in Real-World. J Inform Techn Int, 3(1), 105.

39. J. Cabot, “Positioning of the Low-Code Movement within Model-Driven Engineering,” ACM/IEEE MODELS Conference, 2020.