How Data Labeling Accelerates AI Application in Education?
Recently, AI technology has been considered as one of the important ways to change the existing education field.
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 input method. 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 enables educators to realize their dream of “customizing teaching methods”, thus truly improving the quality, efficiency, fairness, and other core issues of education.
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, Voice annotation
Applications:
- Students’ behavior recognition in class
- Personalized learning: Speech recognition
- Homework grade: OCR
End
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Relevant Articles:
1 Main Applications of Natural Language Processing(NLP) — 1
2 Main Applications in Natural Language Processing Field(NLP) — 2
3 Main Applications in Natural Language Processing Field(NLP) — 3
4 Data Labeling Service: How to Ensure Data Quality for Machine Learning and AI Projects?
