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7 ways AI & Data are transforming learning

Tesla passed $1 trillion market cap today so it is now worth more than Pfizer, Aztrazeneca, GSK, ExxonMobil, BP, and IBM combined. The only companies now worth more than Tesla are Apple, Microsoft, Google and Amazon. Their common denominator is that their underlying tech is now AI. Europe is falling behind, as we'd rather regulate than innovate.

Those who claim to ‘know’ where AI is going, and how fast, are being constantly challenged. 

So where is it going on learning? Well the main area of focus is NLP (Natural Language processing). AI is moving fast on several fronts here.

Data

Tesla has what seems to be an outrageous valuation. Yet what is being valued is not traditional car production, it is the driving data it harvests and the promise of a world where the very concept of vehicles and transport will be transformed. This will happen in learning. The data we gather will feed into optimising future learning experiences, as processes not events. This is why AI, or rather AI that uses data, will shape the future landscape of learning. Data will lie at the heart of all learning experiences. I explain this in my new book ‘Learning Experience Design’.

AI is the new UI

I’ve written about this in ‘AI for Learning’ and ‘Learning Experience Design’, the reshaping of UX as almost all interfaces are now mediated by AI - all social media, Netflix, Amazon, Google, YouTube - almost everything you do online. This is now happening in learning thorough LXP systems. In addition, voice interfaces are now in smartphones and on devices in cars and homes. It is getting better, faster and is scalable. AI is changing our whole relationship with technology, making it more human.

AI personalises

We know that personalised learning gives really significant advantages to large numbers of learners. We’d all love to have one-on-one teaching but that was never economically possible. It is now. Adaptive and personalised learning, enabled by AI, is now here at all levels in learning. CogBooks, a company I helped build has just been sold to Cambridge University Online and will power its online learning. LXPs, such as Learning Pool’s Stream, something I’ve been involved in, will deliver personalised learning to employees in the workplace and workflow.

AI teaches

Teaching largely addresses deficits in motivation and effort, learning is largely achieved by oneself. Took me a long time to truly understand this. It can create sense-making experiences for learners. The problem with traditional online learning is that it was essentially the presentation of content. It never really did what a good teacher does and that is create the opportunities for learning then allow and support you to make the effort to learn. AI enables both. We do this in WildFire.

AI learns

We used to have teachers and learners. Now we have teachers, learners and technology that also learns. Tesla learns as it aggregates driving data and uses that data to improve performance. The more we use Google the better it gets. The more we use personalised and adaptive learning the better it becomes for future students. We are no longer stuck on a plateau of human performance but on an upward trajectory of performance, making learning better, faster and cheaper.

Transformers

Transformers, such as GTP-3 are already useful in learning. We’ve been using them in WildFire for summarisation, content creation and question generation. This software is so powerful that just learning how to ask it questions or do things for you needs a new skillset - it is called ‘prompting’. These AI models have been trained with unimaginably large data sets. They have so much data in their training set that they, at times, transcend the ability of humans to create prose. They are now also entering the world of audio, images and video. They will literally be transformative.

Edge AI

The processing and application of AI on the ‘edge’, on devices, has really arrived. Look at the new Pixel6 mobile phone to see how AI is being delivered via chips in devices such as phones. It has a Tensor AI chip on-board; so translates, transcribes and does speech recognition blazingly fast. It can also erase unwanted objects on photos. These are seriously difficult tasks that require localised processing.

Conclusion

We can wallow in existing practices and technology and see modest but not substantial change in the efficacy and cost of learning. Or we can accept that the future is one where data, and what we do with that data, determines upward progress. A future that uses AI and data to create learning experiences as processes not events, improve interfaces, personalise, teach, support learning. All of this possible to wherever, whenever and to whoever. Technology, specifically AI and Data are finally delivering what we used to call Lifelong Learning.


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