How Data Labeling Accelerates AI Application in the Education?
Recently, AI technology has been considered as one of the important ways to change the existing education field.
The natural language processing（NLP), image recognition, OCR can be used to analyze students’ learning behavior, and customize teaching methods, according to their strengths and weaknesses, reaching one-to-one personalized teaching goals.
Looking from a bigger picture, teaching, management, and evaluation are the three main application directions for AI in education.
To be more specific, image recognition technology can liberate teachers from homework grades.
Speech Recognition
Speech recognition technology has existed for more than 50 years. Only in recent decades speech recognition has made great progress. Now, we have various kinds of software that enable us to decode human speech. The applications cover mobile phones, smart home, virtual assistants, video games, etc.
In general, this technique has been used as an alternative to other input methods, such as typing, clicking, or selecting text. There are many Speech recognition products, such as Cortana, Google Assistant, Siri, etc.
Speech recognition can assist teachers to correct students’ English pronunciation in the oral test. Through voice interaction, AI teachers can give specialized feedback to students.
Most importantly, it is highly likely that the integration of AI technology into the education field enable educators to realize their dream of “customizing teaching methods”, thus truly improving the quality, efficiency, fairness, and other core issues of education.
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One Teacher VS One Student
Of course, it is not an easy thing to teach students one by one in accordance with their personalities. It requires AI support.
AI technology promises to transform education from “one teacher VS multiple students” to “one teacher VS one student”. It is important to collect various types of data at scale. Once collected, the data should be annotated with accuracy before getting the AI algorithm trained.
Data Labeling Tools：
2D boxing，3D boxing， Polygon，OCR, Image recognition，Semantic segmentation, Video annotation
Applications：
Students’ behavior recognition in class: Image recognition,2D boxing，3D boxing
Homework grade: OCR
Personalized learning: Speech recognition
End
ByteBridge, a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service for the machine learning industry.
On the dashboard, we support end-to-end data labeling solutions including visualizing the labeling rules, and all the processes are managed in real-time.
As individuals can decide when to start and end the task, the by yourself service makes it possible to engage and to take control of the labeling loop. Meanwhile, transparent pricing lets you save resources for the more important parts.
For more information, please have a look at bytebridge.io , the clear pricing is available.
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How data labeling assist AI’s application in Education? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
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