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HOW AI IS TRANSFORMING WORKPLACE LEARNING AND DEVELOPMENT

WORKPLACE

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: 

Personalisation

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.

Technology

ARTIFICIAL INTELLIGENCE AND FUTURE OF TECHNOLOGY

Ashish Jain, CEO, Future FX

 

Artificial Intelligence refers to machine intelligence that is programmed to think like humans and mimic their actions. For example while writing this article, I am not actually typing it but dictating it out using the microphone and the text is being typed by Microsoft Word itself.

The ideal characteristic of artificial intelligence is to rationalize and take actions to achieve a specified goal.

As technology advances the previous methods of artificial intelligence are taken for granted as new necessities are conjured. For example the computer was one of the most iconic invention of artificial intelligence but now it is considered as mandatory.

Artificial intelligence is continuously evolving and has to evolve. Machines are made in a way that they understand mathematics, linguistic, psychology and many more other terms that are related to human mind.

Artificial intelligence is used in many sectors for example the medical sector. It is used to test drugs and medicines.

We have applications and games which includes chess where the computer plays against us this is also a feature of artificial intelligence. Similarly self driving cars are also an invention of artificial intelligence. These have to be designed very intelligently.

This can also be used in the financial industry to trace and flag activities in banking and finance such as unusual debit card activity or usage and large deposits.

This also helps to estimate the demand supply and prices of the estimates and that makes trading easier.

Earlier, we had to pay a visit to bank on order to deposit a cheque. Then we updated to ATM/Debit Cards and now you can be identified by your retina. Many different sectors have also adapted this method to make actions it more convenient and safe.

Some more examples of artificial intelligence are iPhone’s Siri, Google’s Smart Assistant, Amazon’s Alexa, Google Maps, Ride- sharing apps like Uber and Ola, diseases mapping, Automated investing, virtual travel booking, social media monitoring, inter team chat tool, NLP tools, etc.

Artificial intelligence is all around us and playing an active role in our daily lives. Every time we open our Facebook newsfeed, do a Google search, get a product recommendation from Amazon or book a trip online, we are using it immensely.

In the coming years, computers might match or even exceed human intelligence and capabilities on tasks such as decision- making, reasoning and learning, analytics and pattern recognition, visual acuity, speech recognition and language translation.

Smart systems in commodities, vehicles, day to day use objects will save time and effort offering us a more customized and comfortable future.

It will help the medical sector hugely in upgrading the medicines and treatments, inventing new ones which haven’t been found yet and making everyone’s lives more safer and healthier. A large number of data can be collected from person to person about their health and nutrition and thus changes can be made in the lifestyle.

Artificial intelligence will bring changes in the educational system making it more revolutionary and advanced.

Overall, every factor has advantages and disadvantages and artificial intelligence has it’s lot too. Considering all the advantages artificial intelligence will also affect the human decision making power, analyzing and rational thinking, lifestyle etc. It will make people lazier and will affect their creativity. It can also lead to unemployment due to increase in usage of machines.

Like everything has a balance, artificial intelligence needs to be balanced too so that we can enjoy it’s benefits without suffering the negatives.

 

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Finance

COULD COVID-19 BE THE CATALYST FOR DIGITAL TRANSFORMATION IN FINANCE?

AI

By Simon Bull, Sales Operations & Business Development Manager at Aqilla

 

We are all now living in a new ‘normal’ where working from home is no longer a luxurious ‘perk’ of the job, but an essential. In the case of many organisations, the transition to flexible, remote working was successful, albeit slightly bumpy. But there is one department that has found it more challenging to transition to the required standards of remote working – the finance department.

The finance department often gets left behind when it comes to digital transformation largely because it is so heavily regulated. And because of this, one of the biggest problems the finance teams face is that it’s sensitive data will likely be stored on a hardware server on office premises. If you look at how organisations update their software as they grow, it’s usually the finance department lagging far behind, or sometimes forgotten about altogether. This is because finance has complex requirements that can lead to the attitude of: if it ain’t broke, why fix it?

Up until now, most finance teams have overcome the challenges this situation presents, but with the repercussions of the pandemic still very much in play, the complications that go hand-in-hand with on-premise technology have been more noticeable than usual. As a result, COVID-19 is becoming a catalyst for a digital transformation in finance, or more specifically moving finance and accounting software away from traditional on-premise solutions to built-for-cloud services. But what are the advantages of this approach, and what should finance teams be looking for in a built-for-cloud solution?

 

  1. Simon Bull

    Cost: The Software-as-a-Service (SaaS) approach that is the basis of many of today’s cloud computing businesses generally offers customers a convenient monthly pay-as-you-go model. Given that all that users need to access the software is a desktop, laptop or smart device and internet connectivity, they can also save money on the server hardware that has previously sat in the corner of the office. Hint: compare pricing from several potential providers to make sure there are no unexpected extras before signing up.

  2. Service: Good cloud-based providers offer extremely strong levels of customer support and service. It should be very easy to get help quickly and conveniently, and they should be in a position to offer advice, identify problems and fix errors without undue delay. Hint: ask for references from existing customers or look for online reviews to assess their service and support capabilities. Also, carefully check their Service Level Agreement (SLA) to clearly understand where their commitments begin and end.
  3. Security: Established cloud providers offer high levels of security, data protection and backup services as part of their ‘as-a-Service’ package. Customers benefit from the protection afforded by security specialists whose job it is to prevent breaches and keep data completely secure. Hint: Check their security policies and consider talking to existing customers about their security track record.
  4. Compliance: Cloud providers specialising in the finance industry should have compliance at the heart of their product set. Hint: Check with potential providers about their levels of compliance and certification, particularly if you have specialised requirements.
  5. Ease of use: today’s built-for-cloud software services are built for purpose, with many offering a high degree of bespoke capabilities so every user can tailor it to their precise needs. This is in contrast to traditional software packages that can be far less flexible, forcing the user to work in a particular way that might not be ideal. Hint: ask potential providers for an online demonstration to check the way the services work meet your needs.
  6. Performance: In the early days of cloud computing, finance software was too basic for many professionals to consider. Today, there are many entry-level services, while others offer a comprehensive range of capabilities to precisely fit the needs of professional finance departments. Hint: evaluate the range of capabilities offered by a cloud provider, which should include areas such as: extensive analysis, proper periodic management and business calendars, multi-currency, multilingual and multi-company operation, full VAT handling International coding, tax and language flexibility, automatic reconciliation / bank integration, built-in key performance measurement, advanced search, selection and drill-down, document and image scanning. Hint: compare the features of different providers in advance – if anything important is missing, look elsewhere.
  7. Regular updates: Software developers find it much easier to update and improve their services when they are delivered online, and can more effectively keep up with finance best practice and changes to rules and regulations. Many also encourage users to suggest improvements or new features which are then provided to customers at no extra cost. Hint: ask providers about how often they update their software and whether you can suggest improvements.

 

For many businesses, these are compelling reasons to adopt cloud-based finance software services, even in normal circumstances. But considered in the context of the current remote working environment, built-for-cloud finance software can help departments to adapt and capitalise on working from home and match the levels of digital transformation seen across many other key business functions.

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