Machine Learning, Artificial Intelligence and Education
Download as PPTX, PDF0 likes106 views
Artificial Intelligence and Education The document discusses several potential implications of AI on education. It may allow for customized, self-paced learning for each student. It could eliminate human errors and limitations of teachers by providing feedback and grading objectively. AI could improve language learning through tools like translation and make quality education accessible to more people.
Machine Learning, Artificial Intelligence and Education
- 1. Artificial Intelligence and Education
- 2. Some potential implications of AI on education Example: Isaac Asimov’s The Fun They Had Makes it possible for every child to learn customised material at their own pace- children do not have to study the same thing at the average pace decided by schools or teachers Further, this means that it is no longer 1 teacher for more than 2 dozen students on average Easier or specially abled children to have same access to education Elimination of the human error and limitations of Human teachers Massive implications on language learning, like translation and image-text recognition Plagiarism and fraud detection Research analysis faster, more accurate, better and with more applications
- 3. ● Student performance, path prediction ● Using Neural Networks ● Agrawal, H., & Mavani, H. (2015). Student Performance Prediction using Machine Learning. International Journal of Engineering Research and,V4(03). doi:10.17577/ijertv4is030127 ● Predicting at-risk students ● Adjustment of difficulty ● Unbiased grading system ● Language learning tools ○ Example: Duolingo voice analysis ○ Google translate text recognition from voice and images
- 4. ● A better structure and course plan ● Feedback on performance ○ Examples: BrightBytes Clarity ○ Lumosity ○ Turnitin ● Using games to improve learning experience, retention and recall ● Has potential to make quality education accessible to everyone, and improve literacy rates if the government plans for it properly ● Customized lessons for each student, optimised according to their needs and strengths
- 5. ● Suggested future path for learning ○ Examples: Genext tutors teaching application ○ DreamBox ○ Mangahigh ● Educational experiments ● Less need for all physical tools to be present- save costs- can do virtual experiments, etc ○ Eg. chemistry lab apps to virtually recreate lab experiments ● Matching students and teachers ○ Example: New Classrooms
- 6. ● Some More examples: ○ Plexuss: to facilitate students in choosing universities ○ ITS: intelligent tutoring system ○ Recognition apps like LeafSnap ○ Woogie: Educational Companion ○ Learning analytics ○ Personal Trainer ○ Viper: plagiarism checking ○ Automated essay grading ○ Better reading levels ● From http://ai.business