Darren Hockley , MD at DeltaNet International
We’ve come a long way since the early days of workplace e-learning. From the text-only prototypes of the 1980s, to the advent of multimedia capability in the ‘90s, and then mainstream internet, flash video, and HTML5 in the 2000s.
The demand for more innovative, relevant, and engaging workplace training has meant e-learning has continued to be disrupted and reimagined by technological advancements – a fact that’s almost certainly responsible for the medium’s longevity.
e-learning’s rise as the preferred training choice of many organisations today seems to fly in the face of its original sceptics – many of whom spoke out against its detraction from ‘human’ experience, or else called it ‘homogeneous’, suggesting it lacked the personal touch a real-life trainer could provide.
Whilst today there are many well-documented benefits of e-learning, the industry’s innovators have continued to take these issues seriously and address them in earnest. Design-techniques like immersive and scenario-led learning, e.g., were created to encourage critical thinking and problem-solving skills amongst digital learners.
The issue of personalisation also began to be addressed as Learning Management Systems (LMSs) evolved. Using pre-assessment tools, the systems could highlight gaps in knowledge and create personal development plans for each learner. Indeed, it was (and I believe, still is) this shift away from a ‘one-size-fits-all’ approach to workplace training that informed the intelligent e-learning of 2020 and beyond.
A New Learning Experience
Today’s Learning Platforms are very different from the ones used even a few years ago. One major difference being the incorporation of artificial intelligence and machine learning into their application – an innovation that was largely enabled by the Experience API, or xAPI.
xAPI revolutionised the way data is collected and analysed, allowing learning systems to build a much more comprehensive picture of organisational learning. One reason for this is that xAPI – unlike its predecessor, SCORM – is not limited to e-learning courses or an LMS. It can collect and aggregate data across multiple sources and track learning experiences wherever they take place, both on and off the learning platform.
Any learning activity users undertake (e.g. articles accessed on knowledge bases, queries submitted to helpdesks, documents shared on collaboration platforms, information input into performance management portals, quizzes taken and re-taken, online searches performed, and so on) is recorded as a statement and saved inside of a Learning Record Store (LRS) unique to each user. This data helps create comprehensive learning pathways for each employee – and it also helps to inform the learning design of the future.
Here are some of the ways AI is transforming corporate learning and development:
There’s a reason many e-learning companies no longer refer to their learning platforms as a learning management system or ‘LMS’; the name doesn’t quite cut it anymore. These days you’re much more likely to hear about ‘learning experience platforms’ or LXPs.
LXPs mark a move away from formal, externally managed learning systems and concentrate instead on delivering a learning experience built around the user and their predisposition for learning.
Like LMSs before them, LXPs are built to deliver learning content, but they are also built to be able to learn themselves. This type of artificial intelligence is known as machine learning.
Driven by xAPI’s ability to aggregate data, today’s LXPs can track and respond-to user behaviour (i.e. what learning style they prefer, what they tend to search for, how long they spend learning, how many attempts it takes to pass, and etc.) and deliver content it infers meets our goals, interests, and preferences.
As you can imagine, this process creates highly personalised learning environments that are vastly different from the e-learning of yesteryear.
Learning in the Flow of Work
More sophisticated still, LXPs are being built to interrogate what users are working on and look for learning opportunities to keep the workflow on track. This is a development known as ‘learning in the flow of work’ and it is designed to support work, rather than distract from it.
Rather than stopping work to attend a seminar or sift through an hour-long course looking for vital information, LXPs will simply be able to suggest snippets of information/short videos as we work to help clear up areas of confusion – allowing employees to get on with the task at hand.
The same logic applies to the LXP’s ability to curate lengthier content if necessary. For example, if the system identifies a reoccurring knowledge gap. By pre-empting a learning requirement, the platform will be able to filter and curate content from multiple sources (e-learning course libraries, newspaper articles, journals – basically anything online) and create digital ‘textbooks’ made up of the type of learning content it knows we respond well to. These might be digestible study guides, videos, summaries, quizzes, podcasts, practice tests, gamified challenges, and so on.
Measure the Impact of Learning
Of course, no e-learning is perfect, and quality control is another area AI can help us out with. For instance, AI can explore the efficacy of learning content by analysing learner performance data.
It can, for example, identify instances where significant groups of learners have failed to answer a question correctly and interrogate their data to find commonalities amongst them. Perhaps they have all overlooked a specific course update or learning material that the LXP can then prioritise and promote to close the knowledge gap.
Alternatively, it’s possible the course content itself requires further attention from its developers and designers. The issue could be misleading or outdated information, or confusion over some instruction given in the course itself. Artificial intelligence expedites this investigation process and completes necessary data analysis without human bias or emotion impacting the results.
It’s in this way that I see AI complementing rather than replacing human experience when it comes to corporate L&D. In the future, I see AI redefining our understanding of personalised workplace training and envision it enabling e-learning suppliers to be more creative, more progressive, and – dare I say it – more human than ever before.
Darren Hockley is MD at DeltaNet International. The company specialises in creating engaging compliance and health and safety e-learning, and provides an intelligent learning platform, for businesses around the globe.
WHY DIGITAL TRANSFORMATION IN FINANCIAL SERVICES IS ABOUT CULTURE FIRST, TECH SECOND
Stuart Templeton, Head of UK at Slack
In today’s world, there’s no such thing as a ‘non-tech fin’. Every financial services company needs to consider itself a fintech in order to bring about the innovation, speed, and transparency that customers expect, and that’s why most are pumping significant investment into their digital transformation efforts.
Part of the challenge faced by traditional incumbent banks is that they rely on legacy core systems that stifle the speed of change. These core systems were not built in an API first era. The good news of course is that the obligations of PSD2 and open banking have gone some way to facilitate future innovation.
While legacy banking platforms do continue to present a technical challenge, the human one can be even greater. Traditional institutions are often faced with the prospect of rebuilding their culture from scratch in the pursuit of becoming digital-first. Like many industries, the fundamental challenge is one of coordination: the creation and maintenance of alignment over time.
Couple this with the fact that the expectations of today’s workforce are changing, then companies in the industry have a real job on their hands. A growing percentage are digital natives, and millenials – who greatly value trust and transparency – make up the largest proportion of the workforce today. So how have businesses in the industry historically ingrained culture, and how does this need to change?
Old ways of working – Team A, and Team B
Traditionally, the culture within large financial organisations has been separated by two distinct teams: operations, and tech. They are driven by seemingly opposing forces – one by GANTT charts and lofty business goals, the other by agile software delivery and customer obsession. Often, the two don’t even speak the same language, let alone collaborate and share ideas. Of course there are digital projects, but they aren’t the embodiment of the business, and often tech teams find themselves battling to get buy-in from internal stakeholders who are somewhat removed from those that drive innovation.
Part of the problem is even the notion of having digital transformation projects – there is no such thing in today’s environment – as digital is an overarching movement, and financial services institutions must think of themselves as ‘digital factories’ in order to see a marked change. It is no longer enough to deliver tech updates both internally and externally once every few months, with speed diminished by layers of bureaucracy.
What needs to happen, then, is that these two business segments need to find a way to blend that helps the old incumbents forget their binary ideas of teamship from time gone by and instead let them come together to become one unit. Flattening the established hierarchy so that workers from across all lines of the business can communicate, share ideas and identify problems in real-time is, after all, the key to addressing the transformation gap. They need to think on their feet and iterate as they go: it’s agile thinking, but permeating outside of just the software delivery cycle.
Eating the elephant – one bite at a time
The solution, in theory, is relatively simple: companies need to break open the silos of information created by technologies like email and ensure anyone within a business has access to the knowledge and skills they need to make their projects a success. But of course, in practicality, this can present a seemingly insurmountable task.
Using technology to create an agile and transparent working environment that fosters collaboration is key for many financial services organisations that want to see real tangible results from their investments. Digital natives such as TransferWise and Starling Bank are getting this right by prioritising a decentralised business model, one that empowers collaborative working and knowledge sharing that in turn has a positive impact on employee satisfaction and retention.
They do this through collaboration hubs that provide a rich, permanent, searchable record of knowledge for everyone in the organisation.
Looking ahead: Team ‘us’
Predictions are very difficult, but in five years’ time we can expect to see a greatly altered perception of the financial services industry. We can expect that digital communications tools will continue to play an integral role in the evolution of their workforce culture, helping to bring the right people together internally within the business, as well as strengthening relationships externally with partners and customers alike.
Ultimately, in order to keep learning and improving, banks need to ask questions of themselves as competition and customer demand becomes more fierce: “Why are we doing this?” “What’s the benefit here, and who are we considering in the pursuit of this goal?”
To answer these things, a culture of collaboration and openness is key – underpinned, of course, by the tools that empower it.
DISPELLING BIOMETRIC MYTHS AND MISCONCEPTIONS
By Lina Andolf-Orup, Head of Marketing at Fingerprints
Gangsters cutting off enemies’ fingers to access secret locations and spies lifting fingerprints from martini glasses – the imagination of the entertainment world has been running wild ever since biometrics entered the scene.
Couple that with the limitations of some early biometric solutions from fifteen years ago, still anchored in the minds of many consumers, and you have the perfect recipe for an apprehensive and uncertain public.
Thawing lukewarm attitudes with a biometric touch
The biometrics industry has made great strides in the last few years – something particularly true for smartphones. Fingerprint authentication has replaced PINs and passwords as the most popular way to authenticate on mobile, with 70% of shipped smartphones now featuring biometrics.
And it doesn’t end there. Many adjacent markets are now eager to benefit from the secure and convenient authentication solutions that biometrics offer. Take the payments industry, for example, where biometrics payment cards are currently gathering real momentum.
However, some consumers are still uneasy about accepting biometrics. A recent study found that 56% of US and EU consumers are concerned about the switch to biometrics as it’s not enough understood to be trusted.
Although attitudes are shifting for the better, stats like this demonstrate there is still some work to do to disprove common biometric myths and showcase just how smart today’s solutions really are.
Dispel, adopt, repeat
The evolution in consumer biometrics in the last two decades has been phenomenal. And today’s solutions are far more advanced and safe than many may think.
To help bring an end to the myths, let’s expose some of the most common misconceptions around biometrics.
Myth: Biometric data is stored as images in easy-to-hack databases.
A leading myth about biometrics is that when a fingerprint is registered to a device, it is stored as an image of the actual fingerprint. This image can then be stolen and used across applications. In reality, the biometric data is stored as a template in binary code – put simply, encrypted 0s and 1s. Storing a mathematical representation rather than an image makes hacking considerably more challenging. In most consumer applications, this template is also not stored in a cloud-based location, its securely hosted in hardware on the device itself for example in the smartphone, in the payment card. Thus, it stays privately with its owner.
Myth: Fingerprints can be easily replicated to ‘trick’ devices.
The internet is full of articles and videos that claim it is possible to use materials from cello tape to gummy bears to craft fingerprint spoofs and access biometric systems. Although there may have been a time where gummy bear spoofing was the go-to party trick, todays’ consumer biometric authentication solutions have too many technological defences, such as improved image quality and matching algorithms, to simply ‘trick’ devices. Plus, on top this, the criminal needs to have access to the person’s device where this fingerprint is enrolled e.g. smartphone, payment card, before he/she notices and blocks it. This is not scalable nor common, in comparison to gaining access to someone’s PIN code or skimming a contactless card.
Myth: Physical change will prohibit access to my device.
Although our irises don’t change as we age, our fingerprints can and our faces will. Does that mean we have to update our biometric devices every few months to capture these changes? Not quite! Unless there are drastic, sudden changes, the ‘self-learning’ algorithms in modern-day biometric systems are able to keep up with our developing looks.
Who you gonna call? Mythbusters!
These are just some of the common biometric myths and misunderstandings perpetuating in consumer mindsets. Thankfully, though, while we’re working hard to rid the world of the myths, belief in the value of biometrics is only expected to grow. But as solutions expand and diversify, the myth-busting fight will continue.
Fingerprints has been a leader of innovation in biometrics for the last two decades. We’re proud of the expertise and R&D we’ve been able to pour into our biometrics solutions to deliver stronger security and a better user-experience. To learn more about the most common biometric misconceptions and the modern-day technology that allows us to dispel them, download our eBook here.
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