There’s no doubt that ‘AI’ is super-hyped these days….everyone seems to be using it to sex-up their tech solution or start-up – including in the education space. Endless opinions abound, institutions and startups alike claim AI super-powers….just don’t scratch the surface with most or you might be disappointed.
The HolonIQ Research team are currently preparing a deep dive into AI in Education to provide our clients with informed analysis of what’s happening globally in Artifical Intelligence, but we couldn’t wait to share a few snippets with everyone. In this post we’ve summarised the five tangible applications of AI in education and opened an invitation for experienced education and technology professionals to join HolonIQ’s Executive Panel for our first global survey on Artificial Intelligence in Education.
Beijing, 2018. HolonIQ Co-Founders following two of their obsessions. AI and China. Courtesy TAL
While AI is super-hyped at the moment, it is a real thing and can be explained in plain language. For a fantastic introduction (and then some), Tim Urban’s Wait but Why post ‘The AI Revolution: The Road to Superintelligence‘ from back in 2015 is an easy, entertaining but mindblowing intro to AI and still one of the best. If you are looking to go deeper, grab a copy of Nick Bostrom’s book ‘Superintelligence‘.
Seriously, go beyond the news and blogs on the topic of AI. This is an important topic with far-reaching consequences and deserves your attention.
But just saying “AI” doesn’t tell you much about the underlying applications or technology. So we are using five tangible buckets to analyze AI – here is a really quick description.
Vision is being used in learning and administrative contexts. Emotion recognition can assist in detecting learners’ confusion or engagement while face detection can be used for attendance management, parent/carer access or identity management for testing.
Voice. Campuses and classrooms are starting to use speech to text and voice interface to support campus life and learning activities. Applications for literacy development and language learning are some of the first to use voice recognition in education settings.
Language. Deciphering human language is still one of the most difficult AI problems due to its complexity. However, advances over the past few years have seen applications of NLP into educational contexts such assessing levels of understanding, providing feedback and plagiarism detection.
Algorithms. Deep and machine learning are most prevalent in ‘personalized learning’ systems. Content intelligence and automation, behavioral recommendations provide notifications, intelligent content delivery and personalized learning pathways.
Hardware. At the intersection of AI, Robotics and IoT, hardware-based AI is being deployed on a variety of devices to reduce latency and lowering networking costs. Smart devices on campus, in labs and classrooms connect software systems, data for learning and the physical learning environment in new and smart ways.
We put together a few case studies from big and new players focused on education. Our Deep Dive will include profiles of 100+ AI-native players focused on education.
Identified as one of the world’s most promising AI startups, Chinese face and body recognition company Face ++ is also one of the best funded AI companies, having raised over USD$600m since it was founded in 2011.
Face ++ technology is being used extensively in online learning contexts to assist teachers to identify signs of confusion, engagement and other emotions in their students as well as gesture recognition. The company also focuses on the combination of AI and the Internet of Things and is working on the ‘Smart Campus’ project to bring together the physical and digital in school and university contexts.
USA-based KidSense is built on voice recognition technology that helps children communicate with voice-powered devices. Whereas current technology is trained on adult voice data, KidSense uses speech data from 150,000+ children’s voices and growing.
KidSense technology is used in educational settings to support speech development and by technology products for children which use voice interface. For example, KidSense recently partnered with Qualcomm on voice-based interactions between kids and smartwatches to support a natural and secure way for children to communicate with their devices.
US-based Sense Education aims to solve the ‘scale’ problem of feedback. Sense’s AI algorithms recognize patterns among assessment submissions and group them into different solution archetypes.
This early pattern recognition enables almost immediate identification of areas of strength or weakness across large sets of assessment and enables teachers or graders to provide specific feedback to each group. Each student receives a substantive and timely assessment of their work. Educators gain insights into learning patterns that help them assess and optimize their teaching strategies and improve outcomes.
Founded in 2016, Stockholm-based Sana Labs uses personalization technology to measure students’ answers, response times and an array of contextual information to figure out precisely what they know, how they learn best and how they forget.
Teachers can use data from online courses and assessments to predict issues that might arise before they happen, identify improvement potential, and assign personalized material.
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