AI is good at narrow, prescribed tasks, it is hopeless at general tasks. This, in my view is why big data and learning analytics projects are less appropriate in learning than more precise, proven uses of AI. There’s a paucity of data in learning and it is often messy, difficult to access and subject to overfitting and other problems when trying to make predictions.
On the other hand, using specific techniques at specific points on the learning journey – engagement, support, delivery and assessment, one can leverage AI to best effect. So here’s five ways this was done in 2018, in real projects, in real organisations, some winning major awards.
We’ve had hundreds of early projects this year where chatbots have been used in the context of promising a future use of chatbots. These include learning engagement, learning support, performance support, assessment and well-being. Google demonstrated Google Duplex, that mastered conversational structure in a limited domain, but enough to fool restaurants that it was a human calling. It has been rolled out for further trials on selected Pixel phones. This builds on several different areas of natural language processing – speech to text, text to speech, trained neural networks, conversational structures. We can expect a lot more in this area in 2019.
The world of design has got bogged down in media production, as if to just watch, listen or read were enough to learn. Even media production can be automated, to a degree with AI. We have been producing text to speech podcasts, using automated transcript creation from video and content creation, at last recognizing that learning, as opposed to click-through consumption, needs fast AI-generated production of high effort learning experiences. Award winning, world-beating projects are now created with AI with no interactive designers.
3. Cognitive effort
Online learning has been trapped in largely linear media ‘experiences’ with low effort, multiple choice questions. This year we’ve seen services, that use open input, either as single concepts or free text, which are both created by and interpreted semantically by AI. The ability of AI to interpret text input by learners automates both assessment and feedback. This was realized in real projects in 2018. It will only get better in 2019.
Good learning takes place with timely and relevant action. Almost everything we do online is mediated by AI that delivers timely and relevant options for people – searching on Google, connecting on Social Media, buying on Amazon, entertaining ourselves on Netflix. Adaptive, personalised learning finally showed convincing results on attainment across courses. We can expect a lot more of this in 2019.
The ability to curate content or tap into the vast cognisphere that is the web, is happening as part of course creation as well as separate searched curation. One can wire in external links to content to solve problems of competence, comprehension or curiosity.
Forget blockchain, badges and gamification. The underlying tectonic shift in learning technology will use AI. This is happening in healthcare, with significant ‘better than human’ applications appearing in 2018. This is happening in finance, with chatbots at the front-end and AI playing an increasing role in back-end systems. This is happening in manufacturing, with the automation of factories. This is happening in retail, as the selling, buying and delivery is increasingly through algorithms and AI. It is also happening in learning. This matters. If we are to adapt to the new normal of AI processes on employment, commerce and politics, we must make sure that education keeps up and that we equip ourselves and our children with better skills for this future.
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