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Education

Using ANAI, citizen data scientists and other developers with limited machine learning expertise can easily ingest data, generate insights and build/train ML models.

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potential use cases

AI in Education

Personalized Syllabus

Evaluate the performance of a student and track their learnings to give a personalized syllabus using an AI-based recommendation system.

Administrative Works

Take care of administrative works such as student attendance, admission processes, etc. using AI saving a lot of time, cost, and trivial errors that usually happen.

Chatbots

Deploy AI-based chatbots for doubt clearing to help students solve their doubts 24 x 7, without any limitations.

Global Learning and Research

AI systems can indirectly enable students and teachers from all over the globe to connect and share knowledge with each other and research on relevant things without any borders and limitations.

limits

challenges found

01
Deployment
The implementation of such AI-powered systems by small educational entities can be quite difficult as the expertise needed might not be there and the cost to hire such experts is humongous.  
02
Privacy
Implementing AI systems within the education sector involve giving out private information to the private entities building and running such models which generate concern for privacy among the students and their guardians. 
03
Cost

The cost incurred while building, deploying, and maintaining such AI models is tremendous and can lead to an extra burden on the educational entities using such models. 

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04
Trustability

AI-based systems should be trusted by everyone affected by their decisions. Hence, any bias within the system should be removed as soon as possible as it can affect students in the future. 

our platform

With ANAI, Build The Best AI Solutions

  • ANAI provides an all-in-one solution to perform all the data science workflow-related tasks on a single platform - from data engineering to the model deployment - by implementing a no-code approach in its UI helping entities with limited expertise with setting up such systems with ease.
  • ANAI' eXplainable AI (XAI) and Responsible AI (RAI) enable educational entities to get an explanation of their model's results and also understand the biases present within the system helping build a trust in the model and create transparency within the system.
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