By Dennis Sale
IN previous columns I initially focused on how the human learning process works, both at the levels of mind and brain, and how these systems work dynamically to build learning capability. Quite simply, without the necessary understanding and competence to manage the natural workings of these systems, it can be a painful learning journey. As Hart (1983), a noted author on the brain and learning, succinctly summarised what this means for teaching:
…designing educational experiences without knowledge about how human brains learn naturally and most efficiently can be compared to designing a glove without any knowledge of the human hand.
The more recent columns identified and illustrated some key strategies, techniques, and tools that can be systematically employed in meeting the overall aim of Mastering the Learning Process. The coverage has inevitably been selective and limited – folks write books on this stuff (yep, I confess) – but hopefully it has been sufficient to build a useful foundation of key knowledge that you can pursue further if motivated.
The focus now is on how Artificial Intelligence (AI) is a likely game-changer in providing learning tools that enable individuals to take full agency and responsibility for their own learning, irrespective of background and context. AI certainly provides vast knowledge access, as well as the techniques and tools to maximise human learning potential. Of significant note, however, there is currently no way to connect artificial neural networks to human brains and download information directly into the brain’s neural architecture. If that day ever comes, we enter the world of “Radical Evolution” as depicted by Garreau (2005) in his groundbreaking book of that title. The technology for such a complex brain-computer interface doesn’t exist yet, and there are significant biological challenges to be overcome.
At present, there is ongoing research on how AI can work in tandem with the human brain by mimicking human cognition. By studying the structure and function of the human brain, researchers are creating new AI models that are more efficient and adaptable which could lead to AI that can learn and solve problems in ways that are more similar – even better – to human intelligence. Hence, while directly merging AI and human brains isn’t on the horizon, there is active research on how AI and the human brain can work together to enhance human capability. Of interest, Elon Musk’s Neuralink is presently working on a brain-computer interface that could be seen as a bridge between the human brain and a form of artificial intelligence. However, there is a significant distinction between Neuralink’s technology and directly merging human and artificial neural networks. For illustration, Neuralink implants a chip with tiny threads into the brain which reads and analysis certain brain activity and translates it into signals a computer can understand. Presently, the focus is on helping people with paralysis control devices such as computers with their thoughts, but future applications may go beyond motor control, potentially treating neurological conditions, and enhancing memory functioning.
Most important is what is directly achievable through the thoughtful use of AI technologies now and in the immediate future, and for me it’s the capability to have one’s personal virtual tutor who can significantly cater for my learning needs 24/7. From my research, in the wider context, I envisage the extensive integration of knowledge relating to how humans learn best – especially from cognitive psychology and neuroscience – with the explosion of AI capability and specialised tools. This will cause systemic changes in many aspects of the educational landscape; essentially a new future of learning in which individuals can take control of their own learning and become less dependent on attendance in formal educational structures. Some examples and how this works is summarised below:
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Personalised Content Learning: AI can tailor learning materials and instruction to each learner’s needs and pace. This is achieved by analysing learners’ data (e.g., learning analytics) to identify specific strengths and weaknesses and then create immediate tailored content knowledge (e.g., text, images, and multi-media), learning activities, and assessment items. In terms of the assessment process, AI’s capability to provide immediate, ongoing, and specific focused feedback to address individual learner needs (e.g., gaps in knowledge and understanding, as well as misconceptions) is an exceptional affordance in supporting learning. Newer tools are also being trained to provide motivational support through linguistic calibration, voice tones, and mannerisms – again customised to individual profiles.
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Improved Accessibility enabling differentiation: AI tools can create adaptive learning environments catering to learners with disabilities or learning differences. For example, AI-powered text-to-speech tools can help learners with reading difficulties, while captioning can benefit learners who are deaf or hard of hearing.
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24/7 Support: AI-powered chatbots and other virtual assistants can provide learners with immediate support and answer their questions as and when required. They do not get tired and are not subject to mood change – well, not yet anyway.
For those of us who follow AI progress closely, it seems that there are new specialised AI tools emerging on a rapid, almost daily basis. Hence, I cannot cover a representative sample within this column, but here’s two examples I find useful for illustration:
Grammarly: This is an AI writing tool that picks up typos, grammatical mistakes, and punctuation errors and enables learners to see corrections and explanations, learn from their mistakes and improve writing skills over time.
Owlift: This is an AI tool that can analyse complex topics and breaks them down into clear, engaging explanations, enabling learners to grasp the key concepts more readily. The ability to understand the key concepts that are fundamental to the deep learning of a subject area is central to effective and efficient learning. It also provides different levels of difficulty so that learners can adjust the complexity based on their prior knowledge or desired depth of understanding. This caters to different learning needs and contexts and enables users to progress at their own pace.
In summary, the strategies, techniques, and tools to significantly aid human learning are now available. However, like a complex jigsaw puzzle, while all the pieces may be in the box, there is much work to be done in assembling them correctly; the same is true for Mastering the Learning Process. This is where the most important over-riding competence of Metacognition Capability, which I have detailed extensively (e.g., Sale, Springer, 2020), is essential. One must be able to do the necessary types of thinking and self-regulatory behaviours to put this jigsaw together. In the next and final column in this series (for now anyway), I offer a summary guide on how to do this. Of course, ultimately the learner must take responsibility to put in the necessary “shift” – plenty of deliberate practice with interleaving and willpower. You know what this means – right!
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Dennis Sale worked in the Singapore education system for 25 years as advisor, researcher, and examiner. He coached over 15,000 teaching professionals and provided 100+ consultancies in the Asian region. Dennis is author of the books Creative Teachers: Self-directed Learners (Springer 2020) and Creative Teaching: An Evidence-Based Approach (Springer, 2015). To contact Dennis, visit dennissale.com.
