Learning Designers will have to adapt or die. Here are 10 ways they need to adapt to AI…

Interactive Designers will have to adapt or die. As AI starts to play a major part of the online learning landscape, right across the learning journey, it is now being used for learner engagement, learner support, content creation, assessment and so on. It will eat relentlessly into the traditional skills that have been in play for nearly 35 years. The old, core skillset was writing, media production, interactions and assessment.

In one company, in which I’m a Director, we see a shift towards AI services and products and we’re having to identify individuals with the skills and attitudes to deal with this new demand. This means understanding the new technology (not trivial), learning how to write for chatbots and dealing more with AI-aided design and curation, rather than doing this for themselves. It’s a radical shift.

In another context, using services like WildFire, means not using traditional interactive designers, as the software largely does this job. It identifies the learning points, automatically creates the interactions, finds the curated links and assesses, formatively and summatively. It creates content in minutes not months. This is the way online learning is going. This stuff is here, now.

The gear-shift in skills is interesting and, although still uncertain, here’s some suggestions based on my concrete experience of making and observing this shift in three separate companies.

1. Technical understanding

Designers, or whatever they’re called now or in the future, will need to know far more about what the software does, its functionality, strengths and weaknesses. In some large projects we have found that a knowledge of how the NLP works has been an invaluable skill, along with an ability to troubleshoot by diagnosing what the software can, or cannot do. Those with some technical understanding fare better here.

This is not to say that you need to be able to code or have AI or data scientist skills. It does mean that you will have to know, in detail, how the software works. If it uses semantic techniques, make the effort to understand the approach, along with its weaknesses and strengths. With chatbots, it is all too easy to set too high en expectation on performance. You will need to know where these lines are in terms of what you have to do as a designer. Similarly with data analysis. With traditional online learning, the software largely delivers static pages with no real semantic understanding, adaptability or intelligence. AI created content is very different and has a sort of ‘life of its own’, especially when it uses machine learning. At the very least get to know what the major areas of AI are, how they work and feel comfortable with the vocabulary.

1. Writing

Text remains the core medium in online learning. It remains the core medium in online activity generally. We have seen the pendulum swing towards video, graphics and audio but text will remain a strong medium, as we read faster than we listen, it is editable and searchable. That’s why much social media and messaging is still text at heart. When I ran a large traditional online learning company I regarded writing as the key skill for IDs. We put people through literacy tests before they started, no matter what qualifications they had. It proved to be a good predictor, as writing is not just about turn of phrase and style, it is really about communications, purpose, order, logic and structure. I was never a fan of ‘storytelling’ as an identifiable skill.

However, the sort of writing one has to do in the new world of AI has more to do with being sensitive to what NLP (Natural Language Processing) does and dialogue. To write for chatbots one must really know what the technology can and cannot do, and also write natural dialogue (actually a rare skill). That’s why the US tech giants hire screenwriters for these tasks. You may also find yourself writing for ‘voice’. For example, WildFire automatically produces podcast audio using text to speech and that needs to be written in a certain way. Beyond this, coping with synonyms and the vagaries of natural language processing needs an understanding of all sorts of NLP software techniques.

3. Interaction

Hopefully we will see the reduction in the formulaic Multiple Choice Question production. MCQs are difficult to write and often flawed. Then there’s the often vicariously used ‘drag and drop’ and hideously patronising ‘Let’s see what Philip, Alisha and Sue think of this… ‘ you click on a face and get a speech bubble of text. I find that this is the area where most online learning really sucks.

This, I think, will be an area of huge change as the limited forms of MCQ start to be replaced by open input; of words, numbers and short text answers. NLP allows us to interpret this text. We do all three in WildFire with little interactive design (only editing out which ones we want). There is also voice interaction to consider, which we have been implementing, so that the entire learning experience, all navigation and interaction, is voice-driven. This needs some extra skills in terms of managing expectations and dealing with the vagaries of speech recognition software. Personalisation may also have to be considered. I’m an investor and Director in one of the word’s most sophisticated adaptive learning companies CogBooks, believe me this software is sophisticated and the sequencing has to be handled by software not designers, that’s what makes personalisation on scale possible. With chatbots, where we’ve been designing everything from invisible LSM bots to tutorbots, the whole form of interaction changes and you need to see how they fit into workflow through existing collaborative tools such as Slack or Microsoft teams. there’s a lot of opportunities out there.

4. Media production

As online learning became trapped in ‘media production’ most of the effort and budget went into the production of graphics (often illustrative and not meaningfully instructive), animation (often overworked) and video (not enough in itself). Media rich is not necessarily mind rich and the research from Mayer and others, showing that the excessive use of media can inhibit learning is often ignored. We will see this change as the balance shifts towards effortful and more efficient learning. There will still be the need for good media production but it will lessen as AI can produce text from audio, create text and dialogue. Video is never enough in learning and needs to be supplemented by other forms of active learning. AI can do this, making video an even stronger medium. Curation strategies are also important. We often produce content that is already there but AI helps automatically link to content or provides tools for curating content. Lastly, a word on design thinking. The danger is in seeing every learning experience as a uniquely designed thing, to be subjected to an expensive design thinking process, when design can be embodied in good interface design, use A/B testing and avoid the trap of seeing learning as all about look and feel. Design matters but learning matters more.

5. Assessment

So many online learning courses have a fairly arbitrary 70-80% pass threshold. The assessments are rarely the result of any serious thought about the actual level of competence needed, and if you don’t assess the other 20-30% it may, in healthcare,for example, kill someone. There are many ways in which assessment will be aided by AI in terms of the push towards 100% competence, adaptive assessment, digital identification and so on. This will be a feature of more adaptive AI driven content.

6. Data skills

SCORM is looking like an increasingly stupid limit on online learning. To be honest it was from its inception – I was there. Completion is useful but rarely enough. It is important to supplement SCORM with far more detailed data on user behaviours. But even when data is plentiful, it needs to be turned into information, visualised to make it useful. That is one set of skills that is useful, knowing how to visualise data. Information then has to be turned into knowledge and insights. This is where skills are often lacking. First you have to know the many different types of data in learning, how data sets are cleaned, then the techniques used to extract useful insights, often machine learning. You need to distinguish between data as the new oil and data as the new snake oil.

We take data, clean it, process it, then look for insights – clusters and other statistically significant techniques to find patterns and correlations. For example, do course completions correlate with an increase in sales in those retail outlets that complete the training? Training can then be seen as part of a business process where AI not only creates the learning but does the analysis and that is all in a virtual and virtuous loop that informs and improves the business. It is not that you require deep data scientist skills, but you need to become aware of the possibilities of data production, the danger of GIGO, garbage-in/garbage out and the techniques used in this area.

7. User testing

In one major project we produced so much content, so quickly, that the clients had trouble keeping up on quality control at their end. You will find that the QA process is very different, with quick access to the actual content, allowing for immediate testing. In fact, AI tends to produce less mistakes in my experience as there is less human input, always a source of spelling, punctuation and other errors. I used to ask graphic artists to always cut and paste text as it was a source of endless QA problems. The advantage of using AI generated content is that all sides can screen share to solve residual problems on the actual content seen by the learner. We completed one large project without a single face-to-face meeting. This quick production also opens up the possibility of A/B testing with real learners. This is an example of A/A testing being used with gamification content – with surprising results.

8. Learning theory

In my experience, few interactive designers can name many researchers or identify key pieces of research on, let’s say the optimal number of options in a MCQ (answer at foot of this article), retrieval practice, length of video, effects of redundancy, spaced-practice theory, even the rudiments of how memory works (episodic v semantic). This is elementary stuff but it is rarely taken seriously.

With the implementation of AI, the AI has to embody good pedagogic practice. This is interesting, as we can build good, well-researched, learning practice into the software. This is what we have been doing in WildFire, where effortful learning, open input, retrieval and spaced practice are baked into the software. Hopefully, this will drive online learning away from long-winded projects that take months to complete, towards production that takes minutes not months and learning experiences that focus on learning not appearance.

9. Communications

Communications with AI developers and data scientists is a challenge. They know a lot about the software but often little about learning and the goals. On the other hand designers know a lot about communications, learning and goals. Agile techniques, with a shared whiteboard are useful. There are formal agile techniques around identifying the user story, extracting features then coming to agreed tasks. Comms are tougher in this world so learn to be forgiving.

Then there’s communications with the client and SMEs. This can be particularly difficult, as some of the output is AI generated, and as AI is not remotely human (not conscious or cognitive) it can produce mistakes. You learn to deal with this when you work in this field, overfitting, false positives and so on. But this is often not easy for clients to understand, as they will be used to design document, scripts and traditional QA techniques. I had AI once automatically produce a link for the word ‘blow’, a technique nurses ask of young patients when they’re using sharps or needles. The AI linked to the Wikipedia page for ‘blow’ – which was cocaine – easily remedied but odd.

We have also worked to reduce iterations with SMEs, the cause of much of the high cost of online learning. If the AI is identifying learning points and curated content, using already approved documents, PPTs and videos, the need for SME input is lessened. As tools like WildFire produce content very quickly, the clients and SME can test and approve the actual content, not from scripts but in the form of the learning experience itself. This saves a ton of time.

10. Make the leap

AI is here. Few argue that it will change the very nature of employment and therefore it will change what you learn, how you learn and even why you learn. We are, at last, emerging from a 30 year paradigm of media production and multiple choice questions, in largely flat and unintelligent learning experiences, towards smart, intelligent online learning, that behaves more like a good teacher, where you are taught as an individual with a personalised experience, challenged and, rather than endlessly choosing from lists, engage in effortful learning, using dialogue, even voice. As a Learning designer, Interactive designer, project Manager, Producer, whatever, this is the most exciting thing to have happened in the last 30 years of learning.
Most of the Interactive Designers I have known, worked with and hired over the last 30  plus years have been skilled people, sensitive to the needs of learners but we must always be willing to ‘learn’, for that is our vocation. To stop learning is to do learning a disservice. So make the leap!

Written by Donald Clark, member of the OEB Global Council

Donal Clark will be speaking in the session Learning and Training with Bots – Why Not? on Dec 6 and will be a panellist in the Discussion on “Higher Education is a Waste of Time and Money and EdTech Won’t Fix That” on Dec 6