Yesterday, I had the fantastic opportunity to immerse myself in the innovative world of digital learning at LEARNTEC, a premier trade fair that brings together cutting-edge IT-supported learning methodologies and technologies. The experience was nothing short of enlightening. Listening to talks and presentations helped me gain a deeper understanding of the complex interplay between knowledge, learning, and digitalisation.
In this post, I'm sharing the insights gleaned from LEARNTEC and discussing practical strategies that might help you thrive in this exciting era of constant learning and discovery.
In this article:
Our journey into the world of learning has taken an exciting twist in recent decades. The term "Knowledge Doubling Curve," coined by Buckminster Fuller in 1981 (1), captures this acceleration perfectly. It refers to the exponential growth in human knowledge, a trend that has gathered enormous momentum in the digital age. He noticed that until 1900 human knowledge doubled approximately every century, and by the end of World War II, knowledge was doubling every 25 years. Some years later, a report published by IBM anecdotally added to Fuller's theory and predicted that knowledge would double every 12 hours, fuelled by the Internet of Things (2).
But alongside this exponential growth, we're facing an intriguing paradox: the useful lifespan of knowledge is rapidly decreasing.
Today's world is a whirlwind of relentless change. As a result, we're in a constant cycle of replacing out-of-date knowledge with fresh insights. But while the need for continuous learning is clearer than ever, it brings its own set of challenges.
The deluge of information available at our fingertips often leads to "analysis paralysis" and information overload, which can best be understood as:
"That situation which arises when there is so much relevant and potentially useful information available that it becomes a hindrance rather than a help"(3).
With so much to learn and so little time, we risk being overwhelmed by the sheer volume of knowledge. Coupled with our digitally-induced shorter attention spans (4), we're wrestling with how best to learn effectively and efficiently.
So, how can we thrive in this scenario?
Advancements in Learning
Majority of presenters and exhibitors at LEARNTEC aimed to show the transformative power of digital learning and Artificial Intelligence (AI). With its ability to personalise, adapt, and pace learning to our unique needs, AI is poised to revolutionise how we learn and grow in this age of relentless knowledge expansion.
Here are some key takeaways regarding the continuous advancements in learning and benefits of using different AI tools:
- Personalised Learning: creating personalised learning paths for each employee based on their skills, role, and learning style. This includes suggesting suitable courses, adapting content based on performance, and tailoring the pace of learning to the individual's needs.
- Predictive Analytics: predicting future learning trends or needs within the organisation by analysing past data. It can identify gaps in skills and knowledge, enabling companies to address these proactively.
- Intelligent Tutoring: AI can act as a virtual tutor, offering instant feedback, answering questions, and providing explanations. This can be particularly useful for technical or complex subjects, where instant clarification can enhance understanding.
- Microlearning: breaking down information into bite-sized, digestible pieces that fit into busy schedules and cater to shorter attention spans. This approach, known as microlearning, is effective for reinforcing learning and can be conveniently integrated into the workflow. (Read this article on microlearning to find out more about the benefits of this learning approach.)
- Enhancing Engagement: AI can help you gamify the learning experience by introducing points, badges, leaderboards, and other game mechanics. This can motivate learners and make the learning process more enjoyable. (You can read more about gamification in this article).
- Automating Administrative Tasks: AI can automate time-consuming administrative tasks, such as scheduling training sessions, sending reminders, tracking progress, and generating reports. This allows trainers and L&D professionals to focus more on strategic tasks.
- Social Learning: fostering social learning by recommending peer groups, coordinating collaborative projects, and facilitating knowledge sharing. This can build a culture of learning within the organisation.
- Just-In-Time Learning: AI can provide real-time, on-demand learning support, delivering relevant information just when the employee needs it. This can be particularly useful for tasks that are performed infrequently or require quick, on-the-spot learning.
In conclusion, AI has the potential to make corporate learning more personalised, efficient, and engaging, leading to better learning outcomes and ultimately enhancing the organisation's performance.
A Step Further
The next step in individualising learning in companies using AI is to move beyond personalisation of content towards a more comprehensive, holistic learning experience tailored to each individual. This involves understanding the employee's unique skills, career aspirations, learning style, and context and dynamically adapting the learning journey to these factors. Here's how this could look:
- Holistic Skill and Career Development: AI can help in identifying the skills and competencies each individual needs to progress in their career path. It can recommend personalised learning plans, not just based on their current role, but also taking into account their future career aspirations and growth potential.
For example: if an employee in a marketing role has expressed interest in data analytics, the AI tool used in the company could suggest relevant analytics courses, SQL training, or resources on data-driven decision making.
Contextual Learning: AI can analyse an employee's context, such as their current tasks, projects, and challenges, and recommend learning resources that are immediately applicable. This makes learning more relevant and impactful.
For example: a project manager is leading a team in adopting an Agile methodology for the first time. An AI system could recognise this based on project descriptions or status updates, and suggest timely resources about Agile practices, Scrum meetings, or effective backlog prioritisation.
Adaptive Learning Paths: AI can continuously monitor an individual's performance and adapt the learning path accordingly. If an individual is struggling with a particular concept, the AI could provide additional resources or change the approach to better suit their learning style.
For example: an employee is enrolled in an AI-driven soft skills development course. If the employee excels in negotiation exercises but struggles with public speaking, the AI could adjust their learning path to include more resources, activities, and practice opportunities related to public speaking.
Emotion AI: Also known as affective computing, this technology can analyse an individual's emotional state and adjust the learning experience accordingly. For example, if the AI detects signs of frustration, it could slow down the pace or switch to a different learning method.
For example: If an AI tutoring system recognises signs of frustration or confusion in a learner – perhaps via facial recognition (if the learner opts in), or through analysing patterns in typing or mouse movements – it could suggest a break, or introduce a different, more engaging type of content (like an explanatory video or interactive game) to alleviate the frustration.
Integration with Workflows: AI can integrate learning into an employee's daily workflow through different communication channels like emails, Slack, Teams, or integrated chatbots.
For example: a project manager working on a new software development methodology they are not entirely familiar with. They could ask the integrated chatbot questions about this methodology directly in the workspace. The chatbot could provide short, concise answers and even supplement with links to more in-depth resources if necessary.
Peer Learning and Collaboration: AI can facilitate peer learning and collaboration by suggesting suitable peer groups, coordinating collaborative projects, and facilitating knowledge sharing.
For example: an AI system detects several employees across different departments who are learning about cybersecurity. The system could recommend forming a learning group, or suggest they work together on a cross-departmental project to implement a new cybersecurity protocol.
Continuous Feedback and Support: AI can provide continuous, real-time feedback and support, helping employees to learn from their mistakes and improve their performance. This could include AI-powered coaching tools or intelligent tutoring systems
For example: an employee is learning a new programming language and an AI-powered coding platform could provide instant feedback – indicating errors, suggesting optimisations, and reinforcing correct practices.
In each case, the AI is facilitating a more personalised, contextual, and effective learning experience for the individual. Please note that while such applications of AI are promising, they require careful attention to privacy, consent, and algorithmic bias to ensure that they are used responsibly.
The Loss of Implicit or "Tacit" Knowledge
Another interesting and surprising piece of information one presenter shared with visitors was that more than 90% of implicit knowledge in companies is not registered and is lost when employees leave or retire.
The loss of implicit or "tacit" knowledge—knowledge that's gained through experience and difficult to transfer to another person by writing it down or verbalising it—is a significant challenge for many companies. This knowledge is often stored in the minds of experienced employees and can be lost when they retire or leave the company.
AI can assist in capturing and preserving this implicit knowledge in several ways:
- Knowledge Management Systems: AI-powered knowledge management systems can analyse and catalogue large volumes of data from diverse sources, such as emails, chat logs, documents, and more. They can identify key patterns, insights, and knowledge points that would otherwise remain hidden.
- Natural Language Processing (NLP): AI algorithms using NLP can convert spoken language into written text. This could be used to transcribe and analyse conversations or interviews with experienced employees, capturing their tacit knowledge.
- Expert Systems: AI can be used to create expert systems which emulate the decision-making ability of a human expert. Employees can interact with these systems to gain access to the tacit knowledge they hold.
- AI Coaching and Mentoring: AI-based coaching tools can learn from interactions with employees over time, accumulating and sharing implicit knowledge in the process. These tools can be made available to all employees, ensuring the knowledge doesn't leave the company when someone retires.
- Machine Learning: AI algorithms can be used to learn from vast amounts of data. They can identify trends, patterns, and correlations that may not be readily apparent, turning implicit knowledge into explicit knowledge that can be shared and utilised throughout the organisation.
- Prediction and Recommendation Systems: Based on the accumulated knowledge, AI can make predictions or suggestions for different situations, thereby utilising and passing on the tacit knowledge.
Remember, while AI can significantly aid in capturing implicit knowledge, it is also essential to encourage a culture of knowledge sharing within the company. Activities like mentorship programs, regular knowledge-sharing sessions, and collaborative projects can help in this regard.
In conclusion, the insights from LEARNTEC reinforce the critical role that AI will play in shaping the future of learning. As we navigate the Knowledge Doubling Curve and embrace lifelong learning, these technologies promise to revolutionise how we learn, transforming challenges into opportunities for growth and innovation.
Suggested reading on the topic:
(1) Fuller, R. B. 1981. Critical Path. New York: St. Martin's Press.
(2) Schilling, D. R. (2013). "Knowledge Doubling Every 12 Months, Soon to be Every 12 Hours." Accessed 26th May 2023. http://www.industrytap.com/knowledge-doubling-every-12-months-soon-to-be-every-12-hours/3950
(3) Bawden, D. & Robinson, L. (2020). Information Overload: An Overview. In: Oxford Encyclopedia of Political Decision Making. Oxford: Oxford University Press. doi: 10.1093/acrefore/9780190228637.013.1360
(4) McSpadden, K. (2015). You Now Have a Shorter Attention Span Than a Goldfish. Time Magazine.