How Focus Shapes Student Learning - AI & Cognitive Load Theory

Does our Pavlovian restitution and evolutionary bias not only take hold of our lives but also serve as a means for fulfilling the need for the anti-entropic force of the universe. Are we merely pleasure-seeking artefacts built as a means to structure and process information? Learning could be seen as a way or the only reason for existing to serve as a means to connect and structure more information on which we can grow that same base of connected and structured information. Is this process what we call "learning" intrinsic to us, or does this serve a greater purpose for our existence and tribulations here on earth?

In essence, do we learn, or is learning all we do? The work of Ivan Pavlov, a renowned physiologist, sheds some light on this query. Pavlov's classical conditioning theory suggests that behaviour can be altered through conditioned stimuli (Pavlov, 1927). Strict instructional methods, often seen as highly effective, echo Pavlov's findings, implying a structured approach is beneficial for learning. Moreover, Thorndike's Law of Effect posits that responses followed by satisfaction will be strengthened, further supporting the notion of learning through reinforcement (Thorndike, 1911) or the nature that we need to encourage structured information. Skinner (1953) further builds on this premise in his work, demonstrating that behaviours could be shaped by reinforcement or punishment.

Having AI (Artificial Intelligence) in assessment could also be the holy grail as well as the reaper of assessment, delivering the potential for mass holistic assessment like gamified assessment or video-based assessments where students can upload a video of them explaining a subject instead of relying on anxiety-inducing exam halls. Gamification in assessment has been shown to increase student engagement and motivation (Dicheva et al., 2015), while video-based assessments can provide a more authentic and contextualised evaluation of student understanding (Shute et al., 2016).

Interestingly, the quest for understanding human learning processes finds resonance in the realm of artificial intelligence (AI). Much like developing brains, AI operates through networks of neurons. The theory of "Attention is All You Need" highlights that the key to effective learning, in both humans and machines, is a focused architecture (Vaswani et al., 2017). This theory bears a striking resemblance to the Cognitive Load Theory, positing that human cognition has a finite processing capacity and is most efficient when learning is centred around an attention based model.

Transhumanists envision a future where human cognition melds with artificial intelligence, promising an era of augmented consciousness. Prominent figures within this community often argue that humans serve as a biological precursor to our digital successors. Elon Musk, dubbed the Chief Twit or should we say X-chief twit, once alluded to the notion of humans being akin to biological bootloaders for digital superintelligence during a conversation with Joe Rogan on his podcast the Joe Rogan experience (Rogan and Musk, 2018). This leads into the theories suggesting we might be living within a computer simulation (Bostrom, 2003) and in my opinion people tend to shy away from this as it makes them feel uncomfortable not to mention me too… However, much like the film Moneyball I argue this should not be left up to Jonah Hill and Brad Pitt to sort out.

The counter-argument posits that humans, unlike AI, are not solely governed by a quest for knowledge or rewards. One might argue that human experience transcends the boundaries of mere learning into the spiritual and religious. Emotions, an integral aspect of human existence, are often seen as distinct from learned responses. Although, one could counter-argue that our reactions to stimuli and emotional responses are honed through countless 'epochs' of learning and a natural derivation of darwinian evolution theory only prevalent due to the need to be able to guess rewards that don't come easily and maybe counter intuitive over short periods of time. Deep neural networks have parallels to this as a fine tuned state can be produced through "epochs" of fine tuning on data that use reward based feedback to decide better probabilities of the outcomes to continue their existence and use from a user account.

Comparing human consciousness and information processing in AI raises fundamental questions regarding the nature of existence. If one adheres to transhumanist ideologies, learning theory serves as a precursor to broader topics of transhumanism and information theory. On the flip side, sceptics may argue that we live in base reality, where digital tools are mere extensions of human capability, not a pathway to transcendent understanding. A theory which could very well be true because supported by the random nature of quantum mechanics for example.

In conclusion, while our quest for rewards and knowledge mirrors the learning processes of artificial intelligence, the human experience is arguably enriched by a spectrum of emotions, spirituality and consciousness, setting us apart from our digital counterparts for now until we better understand ourselves. Regardless of the stance one adopts, the exploration of human learning and consciousness continues to unveil more about our existence, with or without the digital overlay. Besides, who could overlook the enduring companionship of dogs, a simple yet profound aspect of our reality?

See "Attention" in Action. We’ve built an AI that mimics the human brain's ability to focus on what matters. See how our 'Attention-based' architecture marks exam papers with human-like precision—but without the cognitive overload here.