AI Powered Smart Task Scheduler with Motivation Mode
Authors
Dept. of Information Technology,Prof Ram Meghe College of Engineering and Management, Badnera, Amravati, Maharashtra India, 444701sant Gadge Baba Amravati University Amravati Maharashtra (India)
Dept. of Information Technology,Prof Ram Meghe College of Engineering and Management, Badnera, Amravati, Maharashtra India, 444701sant Gadge Baba Amravati University Amravati Maharashtra (India)
Dept. of Information Technology,Prof Ram Meghe College of Engineering and Management, Badnera, Amravati, Maharashtra India, 444701sant Gadge Baba Amravati University Amravati Maharashtra (India)
Dept. of Information Technology,Prof Ram Meghe College of Engineering and Management, Badnera, Amravati, Maharashtra India, 444701sant Gadge Baba Amravati University Amravati Maharashtra (India)
Dept. of Information Technology,Prof Ram Meghe College of Engineering and Management, Badnera, Amravati, Maharashtra India, 444701sant Gadge Baba Amravati University Amravati Maharashtra (India)
Article Information
DOI: 10.51584/IJRIAS.2026.11010099
Subject Category: Artificial Intelligence
Volume/Issue: 11/1 | Page No: 1170-1176
Publication Timeline
Submitted: 2026-01-23
Accepted: 2026-01-28
Published: 2026-02-12
Abstract
Effective task eneration and deadline tracking, typically operate under a static paradigm. These systems fundamentally lack the requisite intelligence to dynamically prioritize tasks based on individual performance metrics or to address the crucial psychological dimension of productivity, specifically user motivation and emotional state, thereby contributing to sub-optimal goal attainment.
This deficiency establishes a critical gap between rudimentary organizational tools and comprehensive productivity platforms. Existing methodologies often neglect the necessity for intelligent task decomposition and adaptive scheduling, failing to provide actionable guidance on how large projects should be segmented and sequenced for maximal efficiency. Furthermore, the absence of integrated psychological support mechanisms means that when users experience fluctuations in mood or motivation, the system offers no compensatory intervention, leading to inconsistent application usage and eventual abandonment. Addressing this dual requirement intelligent functional management and sustained psychological engagement is paramount for developing a truly effective productivity solution.
To mitigate these limitations, this pamanagement and sustained motivation are essential for improving productivity in academic, professional, and personal environments. However, most existing task scheduling applications focus primarily on basic to-do list creation and reminders, while ignoring intelligent prioritization and the psychological factors that influence user performance. This paper presents a Smart Task Scheduler with Motivation Mode, an AI-powered Android application designed to address both organizational and motivational challenges in task management.
The proposed system integrates task scheduling, AI-based priority analysis, and mood-oriented motivational support within a single platform. Developed using Android (Java/XML) and Firebase Realtime Database, the application enables users to create tasks, receive intelligent priority suggestions, and track progress through visual productivity analytics. A Pomodoro-based work timer and personalized motivational messages are incorporated to improve focus and reduce mental fatigue.
Keywords
AI-driven Prioritization, Task Scheduling, Android Application Development, Firebase Realtime Database, Productivity Analytics, Motivation Mode, Pomodoro Technique2.
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References
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