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Command Center — AI-Assisted Academic Workload Optimizer

Most productivity tools tell students what they need to do. Command Center tells them whether they can realistically finish it by analyzing workload, detecting deadline collisions, and generating a feasible execution plan.

Command Center — AI-Assisted Academic Workload Optimizer

Built for the Kiro Build Challenge

OVERVIEW

Command Center is an AI-assisted academic workload optimizer that helps students determine whether their assignments, projects, practicals, and deadlines can realistically fit within their available time. It analyzes workload, detects deadline collisions and overload, and generates a feasible day-by-day execution plan.

PROBLEM STATEMENT

Students often manage assignments, practicals, projects, exams, and deadlines across multiple courses without knowing whether their total workload is actually achievable within the time available.
Traditional task managers tell students what is due, but they do not account for estimated effort, available capacity, priority, or collisions between deadlines. This can result in overloaded schedules, poor prioritization, and missed deadlines.

SOLUTION & WHAT WE BUILT

Command Center focuses on feasibility rather than simply task tracking.
Students enter:
  • Task name
  • Course or category
  • Deadline
  • Estimated effort in hours
  • Priority
  • Optional description
The application then:
  1. Calculates total required workload and available capacity.
  2. Scores tasks based on deadline urgency, priority, effort, and overdue status.
  3. Detects deadline collisions and overloaded periods.
  4. Identifies workload surplus or deficit.
  5. Generates a day-by-day execution plan while respecting the user's daily hour limit.
  6. Provides optional AI assistance for natural-language task extraction and workload explanations.
The core scheduling and workload calculations are deterministic and testable. The application remains fully functional without an AI API key, making AI an enhancement rather than a dependency.

Key Features

  • Workload Dashboard — View required hours, available hours, surplus/deficit, and upcoming deadlines.
  • Priority Scoring — Deterministic scoring based on urgency, priority, effort, and overdue status.
  • Collision Detection — Identifies periods where multiple deadlines create excessive workload.
  • Overload Analysis — Flags situations where required work exceeds available capacity.
  • Daily Execution Plan — Generates a practical schedule within the configured daily hour limit.
  • Optional AI Assistant — Supports natural-language task extraction and workload explanations.
  • Persistent Task Storage — Tasks are saved locally using JSON persistence.
The central idea is simple:
Most productivity tools tell students what they need to do. Command Center tells them whether they can realistically finish it.

HOW WE USED KIRO

Kiro was used throughout the complete development lifecycle rather than only for code generation.

Requirements & Scaffolding

Kiro transformed the initial problem statement into structured requirements and measurable acceptance criteria. It helped establish the MVP scope and project structure before implementation.

Design & Prototyping

Kiro reviewed the proposed architecture and iteratively identified design issues before implementation. The project was deliberately kept focused on a small, complete MVP rather than unnecessary infrastructure.

Logic Generation & Implementation

Kiro assisted with implementing:
  • Streamlit application structure
  • Task management and persistence
  • Priority scoring
  • Workload analysis
  • Deadline collision detection
  • Daily plan generation
  • Optional AI integration
  • Automated tests
  • Docker configuration
  • GitHub Actions CI

Testing & Debugging

Kiro generated and executed automated tests covering core functionality and edge cases. It also identified implementation issues during review and assisted with debugging and correction.
Final result: 28/28 automated tests passing.

Validation

Kiro was used to validate the completed application against the project's acceptance criteria, including:
  • Application startup
  • Task persistence
  • Dashboard updates
  • Priority scoring
  • Workload calculations
  • Deadline collision detection
  • Daily scheduling limits
  • Operation without an AI API key
  • Docker configuration
  • GitHub Actions CI

ARCHITECTURE & TECH STACK

Architecture

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User
↓
Streamlit Interface
↓
Task Manager ─────→ JSON Persistence
↓
Scheduler ────────→ Priority Scoring
↓
Workload Analyzer → Collision / Overload Detection
↓
Execution Planner → Daily Feasible Plan
↓
Optional AI Assistant

Tech Stack

  • Frontend / Application: Streamlit
  • Backend / Core Logic: Python
  • Data Processing: Pandas
  • Visualization: Plotly
  • AI Integration: OpenAI API (optional)
  • Testing: Pytest
  • Containerization: Docker / Docker Compose
  • CI/CD: GitHub Actions
  • Persistence: JSON
  • Development: Kiro

Project Structure

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command-center/
├── app.py
├── core/
│ ├── task_manager.py
│ ├── scheduler.py
│ └── workload.py
├── ui/
│ ├── dashboard.py
│ ├── tasks.py
│ ├── plan_view.py
│ └── ai_panel.py
├── ai/
│ └── assistant.py
├── tests/
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
└── .github/
└── workflows/
└── ci.yml
The project also includes Docker configuration, automated CI, .gitignore, .dockerignore, and environment-variable based configuration for optional AI functionality.

LINKS & DEMO

TEAM / BUILDER DETAILS

  • Builder Name: Riddhima Bhandari
  • Team: Solo
  • Track: AI & Productivity
Built during the Kiro Build Challenge conducted jointly by AWS Student Builder Group GEU & AWS Student Builder Group PIET.
Challenge Details: https://awssbggeu.com/challenges/kiro-build-challenge 
# ai# students# productivity# kiro# python
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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