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Top Ten Challenges of Computer Vision in the EdTech Industry

3 Mins read

This article features the top ten challenges of computer vision in the EdTech industry

This article provides an overview of the challenges of computer vision in the EdTech industry, it has been improving services in various industries, and in EdTech, computer vision increases safety and security. As AI vision technology is advancing rapidly, more use cases are introduced in the EdTech sector. EdTech applications include personalized learning and vision-based methods to assess the student’s attention and teacher performance. But there is always another side of a coin, though there are so many advantages of computer vision in the EdTech industry it also has a lot of challenges.

What is Computer Vision Technology?

Generally, AI vision technology works in three basic steps:

  1. Acquiring the video frames from a camera
  2. Processing the image with AI algorithms, and
  3. Understanding the image.

As of late, new deep learning advances carried incredible advances to picture acknowledgment. Cutting edge AI techniques, particularly profound learning models, are exceptionally strong and give exact ongoing item identification and picture characterization. Thus, AI vision technology can perform video examinations with the video of normal reconnaissance cameras or webcams. With the development of Edge AI, the blend of edge registering and on-gadget AI, it becomes conceivable to run profound adapting all over. On-gadget AI picture handling with Edge ML makes PC vision frameworks adaptable, private, and vigorous. This article features the top 10 challenges of computer vision in the EdTech industry.

No Ideal Hardware

Face recognition application is crucial for computer vision technology in order to detect and register the attendance of the students recorded in the CCTV cameras. But lack of ideal hardware makes it really difficult to manage and keep a record of attendance.

Poor Internet Connection

Computer vision in the EdTech industry is advantageous during the online examination as it knocks down widespread concerns. But until there is a high-speed internet connection available. It’s said that the spectrum per operator in India is low compared to other countries.

Professional Development

Key among all challenges is the lack of adequate, ongoing professional development for teachers who are required to integrate new technologies into their classrooms yet who are unprepared or unable to understand new technologies. The same thing is happening with computer vision technology in the EdTech sector.

Resistance to Change

According to the researchers, teachers and school leaders often see technological experimentation as outside the scope of their job descriptions. This is why implementing technologies like computer vision in the EdTech industry is still a huge challenge.

Perspective Differences 

Most of the EdTech leaders are working hard to get computer vision technology to market. They want to quickly test their assumptions, get feedback, and pivot if necessary. For them, success is measured in business growth and sales targets. Teachers, on the other hand, like to take things slowly. They are wary of the impact this AI vision technology will have on students and the extra work needed to implement it.

Hackers to Outsmart Technology

Computer vision technology in the EdTech sector uses surveillance cameras to identify the perpetrators. But hackers always find a loophole and use it for their good. In order to make EdTech products safe and user-friendly one should make sure those products are also hacker-free.

Real-Time Object Recognition

It is challenging to deliver truly real-time object recognition due to the limitations of the sensor itself. The same feature is limiting both for semantic segmentation and semantic instance segmentation in the same way it is for object recognition.

Short Project Timelines

When estimating the time-to-market for computer vision applications, some EdTech leaders overly focus on the model development timelines and forget to factor in the extra time needed for:

Camera setup, configuration and calibration

Data collection, cleansing and validation

Model training, testing and deployment.

Cloud Platforms

Deploying computer vision algorithms to the cloud makes sense if you need the ability to rapidly scale the model performance, maximize uptime, and maintain proximity to the data lakes the model relies on. On the other hand, the cloud may not always be suitable for processing sensitive data. Plus, the computing costs can rise sharply without constant optimization.


Running computer vision models on the edge devices, connected or embedded into the main product (e.g., the camera system, manufacturing equipment, or drone) can significantly reduce latency for transferring high-definition visuals (video in particular). However, edge deployments also require more complex system architecture and advanced cybersecurity measures in place to protect data transfers.


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