Interactive Designers will have to adapt or die. AI and Generative AI (not the same thing) has started to play a major part of the online learning landscape, right across the learning journey. it is now being used for learner engagement, syllabus planning, core skills identification, 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, all delivered through an authoring language. It remained unchanged for nearly 30 years. In many ways it got worse as the tools began to determine the content, so we got lots of cartoony content, with click on speech bubbles, clumsy, drag and drop, stock photos and MCQs. It was expensive and took months.
Every online learning company on the planet is now having strategy meetings to face uo to the challenge.
This is not easy as many of those involved in traditional online content creation will find it difficult to adapt. Others, however, will embrace the change. Many will have 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 new dialogic tools, and dealing more with AI-aided design and curation, rather than doing this for themselves. It’s a radical shift.
In a Keynote over five years ago, I summarised this shift as follows...
This is the new version…
In another context, using a tool like ChatGPT meant 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. I’m involved in several projects where Generative AI is appearing in real product. One was launched at BETT this week (Glean), others are on their way. 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 a number of different companies.
1. Technical understanding
Designers, IDs, LXDs, Learning Engineers 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 (Natural language Processing) 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 as they understand both the potential and limitations.
This is not to say that you need to be able to code or have pure AI or data science 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 ChatGPT, for example, you really do need to keep up with the speed of releases, ChatGPT came out in late Nov 2022, ChatGPT, an order of magnitude better came out in March 2023. I see far too many people still using and basing their opinions on ChatGPT3. Keep up or become irrelevant.
In a series of category errors, any of the silly clickbait 'look at what I've just done' screen shots don't really understand the underlying technology. most are from ChatGPT3, not 4, like using a version of Wikipedia from 2003. The fine tuning, RLHF and guardrailing is ignored, yet these are the things learning professionals need to know about the technology. This will take time. You always have to go through the clickbait phase to get to the serious comment and use cases. The good news is the amazing things people are doing with the tool, especially in learning.
Similarly with data analysis. With traditional online learning, the software largely delivers static pages with no real semantic understanding, adaptability or intelligence, hence the stickiness of SCORM. 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.
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 is why much social media and messaging is still text at heart. When I ran a large traditional online learning company we 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’ or ‘creativity’ as identifiable skills.
However, the sort of writing one has to do in the new world of AI has more to do with being sensitive to what generative AI does, and dialogue. Prompt writing is important and this is where experienced IDs and graphic artists can excel. Prompting is best done by domain experts. A good graphic artists will know how to ask for the right image in terms of style, fonts, look and feel, with all the right parameters. Similarly with good IDs, who will know how to prompt for great questions, not just fact checking. It is a matter of taking your skills and applying them to using these new tools and technologies.
Hopefully we will see the reduction in the formulaic Multiple-Choice Question production. MCQs are difficult to write and often flawed. Then there is 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. There is also voice interaction to consider, which many will implement, 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. If you don’t know about Whisper, you should. Personalisation may also have to be considered. These tools are basically AI on tap. This software is far too complex to build on your own. Yet it makes smart implementation in scale possible.
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 Nass, Reeves, Mayer and many others, shows that the excessive use of media can inhibit learning. Unfortunately, much of this research is 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 becomes multimodal, creating text, images, audio and video, even 3D worlds.
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. We are now in the world of rapid design by smart software that has done a lot of the A/B testing on a gargantuan scale. These methods look more and more out of date.
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, open input, transfer, good generated rubrics and so on. This will be a feature of more adaptive and dialogue-driven AI driven content. Generative AI produces assessments at speed and with relevance to the competences. That was never the case in traditional online learning.
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. AI is now a feature of data-centric learning solutions. Acquire some basic knowledge of data science, nothing fancy, just get to know the lie of the land.
7. User testing
In one major project , we produced so much content, so quickly, that the client had trouble keeping up on quality control at their end. We were making it faster than it could be tested! 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 quick testing with real learners.
8. Learning theory - pedAIgogy
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 AI you can build pedagogy, or what I call pedAIgogy, into the prompting and therefore learning experiences. We are doing precisely this on one product.
With the implementation of AI, the AI HAS to embody good pedagogic practice. Bill Gates recently published an excellent piece on Generative AI saying that learning will be it b] greatest benefit, but te piece was marred by him pushing ‘learning styles’. Greg Brokman, of Open AI, did te same retweeting a tool based on learning styles. We know better than this and can build good, well-researched, learning practice 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. Agile production
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, scrums and superfast production are useful. I love these. 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. There will inevitable be friction between the old and the new. Treat that as a normal.
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. The good news is that these are now rare. You learn to deal with this when you work in this field. To be honest, A=all of those learning folk telling me that AI shouldn't be used in learning, as it sometimes has an error or two, will happily use content with learning styles, Myers-Briggs, Bloom's pyramid, Maslow and no end of bogus theory and content in courses
This new approach 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. You have to be careful but that has always been te case. I can barely think of a single training project where the SME content was spot on.
The great news is that this all means we can reduce iterations with SMEs, even cut them out altogether, as the software often has more knowledge and can write it to any level or style. The cause of much of the high cost of online learning is expensive SMEs and endless iterations. If the AI is identifying learning points and curated content, using already approved documents, PPTs and videos, the need for SME input is lessened. This saves a ton of time and money.
10. Make the leap
AI is here. 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!
In addition, those in HR and L and D will have to get to grips with AI. It will change the very nature of the workforce, which is our business. This means it will change WHY we learn WHAT we learn and HOW we learn. Almost all online experiences are now mediated by AI - Facebook, Twitter, Instagram, Amazon, Netflix.... except in learning! But that has just dramatically change. Generative AI heralds a new era, a renaissance oin learning, where we can learn anything, at anytime, anywhere using sophisticated AI tutors. What is needed is a change in mindset, as well as tools and skills. It may be difficult to adapt to this new world, where many aspects of design will be automated. I suspect that it will lead to a swing away from souped up graphic design back towards learning. That would will be a good thing.
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