Apr 28, 2022,06:30am EDT
Markus Bernhardt is the Chief Evangelist at Obrizum, pioneering deeptech AI digital learning solutions for corporate learning.
Google Director of Research Peter Norvig famously stated in his keynote speech for the Association for Learning Technology Conference in 2007 that if you only had to read one research paper to learn about learning, it would beBenjamin Bloom’s The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring.
To aid his preparation for the keynote, this brilliant piece of advice had been given to Peter by his friend Hal Abelson, an educator and professor at MIT. In Bloom’s well-known and often-cited paper, the outcome of an experiment is reported comparing the efficacy of three types of teaching: the conventional lecture, the conventional lecture with regular testing and feedback, and one-to-one tuition.
Using the “straight lecture” as the mean, Bloom found an 84% increase in mastery above the mean for a “formative feedback” approach to teaching and an astonishing 98% increase in mastery for one-to-one tuition. While intuitively one might of course have expected one-to-one tuition to stand out in such an experiment, the measured impact compared to the other two methods must surely be considered staggering, and we can immediately see why Hal would have chosen to recommend this paper to Peter.
The highly personalized approach for individuals or to small groups in regard to tuition, as well as many forms of training and practice, had of course been the preferred modality long before this piece of research, albeit it being extraordinarily expensive and thus not widely available. However, Bloom’s research provided the data, and extraordinarily so, that cemented one-to-one tuition and all forms of personalization as the gold standard in learning.
Lecturing is a form of one-size-fits-all learning, and it was invented in 1350, a time when books were extremely expensive and not available to the masses since they were arduously written by hand, traditionally by monks. The idea of a lecture arose since this approach would allow one person to read the material out loud to a large group of learners, allowing everyone in the group to access the content and learn. Like all one-size-fits-all learning, lecturing aims to cater to the “average” learner in a group. This idea of an “average” person turns out foolishly inadequate when the realization sets in that given human complexity, the average will almost never adequately describe a given individual, an idea explored in great detail by Todd Rose in his book The End of Average: How We Succeed in a World That Values Sameness.
In recent years, the term one-size-fits-all has mainly been used to denounce click-through e-learning, where such a one-size-fits-all approach and delivery has been the norm—and the pain for employees across the globe and across sectors. Most notably, this has been the case for annual compliance training, delivered digitally and as a tick-box exercise to allow organizations to present completion certificates to regulators.
This is already changing and fast gathering momentum, as artificial intelligence (AI) is not only changing the way we learn but also the way trainers train, coaches coach and teachers teach. Through AI technology, the individual learner can now access fully personalized programs of learning, in their own time, and thus cover almost any topic via self-guided learning. This personalization at a granular level can be achieved, as now the AI is able to measure and take into account prior existing knowledge, as well as continuously measure learner progress, in both competence and confidence throughout the learning journey. It can do this for each topic or learning objective, respectively. AI is thus able continuously to adapt to the learner, their individual pace of progress and their individual learning needs.
Furthermore, AI can not only deliver the learning fully personalized but also in line with evidence-based theory, such as variation (including multiple-media and multiple model content), utilizing worked examples, retrieval practice, spaced practice, low-stakes quizzes and interleaving of topics.
Being able to deliver fully personalized learning journeys through AI is in itself a huge game changer for digital learning. It will drastically change the way we learn, and it will play a major role in the way employees will reskill and upskill in the future of work.
As we enter an era of more effective and efficient learning, asynchronously delivered, individually personalized and at scale, we are looking at the AI learning revolution.
However, there is even more to this. Often overlooked and not covered in nearly as much depth is what exciting and positive impact this will have on in-person tutoring, training, coaching, mentoring and workshops. We are looking at a whole new realm of learning analytics that the AI piece will deliver as an automatic output—strengths, weaknesses, competence, confidence and learner self-awareness.
For human-led sessions, this available data from the AI piece will allow organizations to better plan and deliver far more personalized in-person sessions than previously possible. Organizations will be able to identify learning needs and gaps and, where necessary, group participants accordingly, greatly increasing the potential learning and performance impact of these in-person sessions.
Where previously these in-person sessions, trainings and workshops would have been delivered with the same approach, and often the same slide deck and supporting materials and content, we will see a higher degree of personalization to the individual or learning group and their respective learning needs.
Utilizing the power of AI for learning is already revolutionizing the level of personalization the learner is receiving, across industries and sectors. Learning impact and learning efficiency are being lifted to excitingly new, previously inaccessible levels — both digitally as well as for in-person learning. Learners, designers, trainers, coaches, and educators — everyone is benefiting as we reach into our toolbox and deploy the new AI learning and automation tools available.