Authors : P. Naveen Raj; M. Madesh; M. S. Ramesh; S. Venkata Lakshmi

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/6adz4urb

Scribd : https://tinyurl.com/5fbcmunn

DOI : https://doi.org/10.38124/ijisrt/26mar1009

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Modern software development environments require continuous coordination among team members, timely issue resolution, and accurate project planning to achieve successful project outcomes. However, many existing project management tools still rely heavily on manual monitoring and human-driven decision making. Such reliance often leads to delayed responses to issues, inaccurate scheduling of tasks, inefficient distribution of workload, and an overall reduction in productivity. To address these challenges, this work proposes an AI-assisted collaborative framework in which an intelligent agent functions as a virtual team member that supports development activities. The system continuously observes team communication, task progress, issue updates, and code changes in order to develop contextual awareness of ongoing project activities. Based on this contextual understanding, the system generates suggestions only when relevant conditions are detected, such as errors, delays, or inconsistencies. By analysing historical project data together with developer workload patterns, the framework predicts potential delays and recommends adaptive scheduling and planning strategies in advance. A human-in-the-loop mechanism is incorporated to ensure that developers retain complete control over final decisions. This collaborative approach improves productivity, enhances planning accuracy, and supports effective cooperation between human intelligence and artificial intelligence without replacing human expertise.

Keywords : AI-Assisted Project Management, Agent-Based Intelligence, Human–AI Collaboration, Intelligent Software Engineering, Context-Aware Recommendation, Predictive Task Scheduling, Code Analysis Automation, Human-in-the-Loop Systems, Developer Productivity, Knowledge-Driven Collaboration.

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