AI is many things and it is best employed in learning on specific narrow tasks. That’s what we’ve been doing, using AI to create content, semantically analyse free text input, create text to speech podcasts and curate content at WildFire.
One problem we have tackled is the simple fact that the INPUTS into learning content tend to be overlong, overwritten and too detailed. Training departments often get given huge PDFs, long slidedecks packed with text or overlong video. To be fair those in video production are normally professional enough to edit it down to a reasonable length but huge documents and PowerPoints are, I’d say the norm.
AI can be used to automatically shorten this text. This can be done in two ways (or in combination):
Extractive
Abstractive
Extractive
Extractive keeps the text intact and simply removes what it judges to be less useful content. This uses techniques such as inverse term document frequency that gives you a measure of how important words are within a corpus (dataset of text). It looks for sentences with these words and extracts them. There are many more sophisticated extractive algorithms but you get the idea.
The advantage of this approach is that you retain the integrity of the original text, which may be useful if it has been through a regulatory, legal or or subject matter review.
Abstractive
Abstractive tends to use deep learning models and neural networks to understand the content then generate summarised content as a précis. Free from the constraint of having to be loyal to the original structure, these algorithms will write their own abstract getting down to the real essence of the text.
This more powerful technique is more likely to provide a tighter, more suitable output for learning – shorter and a more optimal distillation of the meaning.
Productivity
This is useful in increasing the productivity of any educational or training design team, as you dramatically shorten this necessary editing task. On large PDFs, not uncommon in compliance and SOWs, these techniques really do work well. But it also works well with any text from articles, papers, books, even Powerpoint text and video transcripts. We already automatically grab transcripts from YouTube but this extra step is useful in reducing what is spoken text, down to its real substance. You often get asides and content that works well on screen but not as text. This combination of video plus detailed effortful text, where you pick up the detail and have to make the cognitive effort to show understanding and actual recall of the content is a useful combination. Note that you can scale down in steps until you get to what you feel is an optimal précis. We’ve also found it useful as it surfaces overwriting, repetition, even errors.
Once agreed, the shorter text can be put into WildFire, where other forms of AI create the content, in minutes not months, again dramatically decreasing both time to delivery and costs. The AI cerates the content and analyses free-text input, which is significantly better in terms of improving both retention and recall.
Time matters
This reduction in time is important, as training design has traditionally been a bit of a block in the process. A business sponsor comes to the training department and told it will take weeks and months. Their reaction is often to simply walk away. You can also be seen in the business as delivering timely and, importantly, not over-engineered solutions to business problems.
Less is more
A point, that is often overlooked, is that this is wholly in line with the psychology of learning, which screams ‘less is more’ at us. A good motto that itself summarises what learning designers have to do, is ‘Occam’s Razor’ – use the minimum number of entities to reach the given goal. This is true of interfaces but it also true of content design, media design and the needs of learners.
Our limited working memories along with the need for chunking and retrieval, makes it essential to be precise and as short as possible with learning content. Many courses are over-long with content that is not essential and will be soon forgotten. What learners and businesses want is the crisp essence, what the need to know, not the padding.
Conclusion
This AI technique can be used, alongside other techniques to massively increase speed of delivery, cost and just as importantly efficacy. Your learners will be grateful, as will your business sponsors.
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