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Version: 1.1.4

Understanding Dashboard for School Admins

How to Navigate the Dashboard for School Admins​

This article explains how to effectively use the Dashboard in Kadal to gain insights into your institute’s AI activity. As an Admin, the Dashboard serves as your primary tool for monitoring resource usage, agent performance, and user interactions. It provides detailed visualizations and reports, empowering you to manage and optimize resources efficiently.

Access the Dashboard:​

Once you log in as an Admin, navigate to the Dashboard page. This central hub provides a snapshot of your institution’s overall AI usage, including agent distribution, token consumption, and associated costs.

Analyzing Data Visualizations:​

The Dashboard features intuitive data visualizations that highlight your resource consumption:

  1. Total Agents Chart:
    • A pie chart displays the distribution of AI models used by agents within your institute
    • Each segment represents a specific AI model, such as GPT, Gemini, or Anthropic.
    • This visualization helps you identify which models are most widely used, allowing for better resource allocation.
  2. Total Tokens Used Chart:
    • This chart breaks down token consumption by model, helping you monitor and control resource usage.
    • Segments are color-coded to align with the Total Agents Chart, ensuring consistency across visualizations.
  3. Total Cost of LLM Chart:
    • A pie chart highlights the cost associated with each LLM (Large Language Model) used in agents.
    • This chart helps Admins manage budgets effectively by understanding cost distribution across different models.

A clear legend below the charts matches the colour codes with specific AI models (e.g., yellow for GPT). This makes interpreting the charts straightforward and efficient.

Agent Report:​

The Agent Report provides a comprehensive list of all agents created within your account. Key details include:

  • Agent Name: For easy identification of agents.
  • Status: Indicates whether the agent is active or inactive.
  • Model: Specifies the AI model powering the agent (e.g., GPT, Gemini).
  • Created By: The user who created the agent.
  • Created Date & Time: Helps track newly added or outdated agents.
  • Tokens Used: Resource consumption by each agent.
  • Cost: Displays the expense associated with each agent. Admins can refine reports using filters:
  • Agent Name: To locate a specific agent.
  • Model: To view agents using a particular AI model.
  • Created By: To focus on agents created by a specific user. User Report: The User Report tracks individual users’ activities, providing data such as:
  • Username: Unique identifier for each user.
  • Status: Indicates active or inactive user.
  • Agent Name: The agent associated with the user.
  • Agent Status: Whether the assigned agent is active or inactive.
  • Model: The AI model the user interacts with.
  • Created Date & Time: Timestamp of the user’s creation or agent assignment.
  • Tokens Used: Tracks usage patterns.
  • Cost: Displays the financial impact of LLM interactions. Similar to the Agent Report, Admins can refine User Report data by:
  • Agent Name: To identify users interacting with specific agents.
  • Model: To highlight users working with particular AI models.

These filters help Admins track high-resource users and adjust allocations to maintain efficiency.