It's no surprise that educators have an uneasy relationship with generative AI. They fear the impact of plagiarism, machine-generated essays, and “hallucinations” (where the system confidently claims something is true rather than simply saying it doesn't know any better) from tools like ChatGPT and Bard. There is palpable concern that generative AI will become a substitute for authentic learning: something that will help a person pass an exam without needing to absorb and internalize the material.
While there is no doubt that AI has been used to circumvent the learning process, ChatGPT has already taken on the role of ad hoc personal tutor for millions, changing learning consumption patterns and improving our relationship with education. The ability to have an AI-powered teaching assistant—one that guides, encourages, and guides students through the material in a one-to-one relationship—is within reach. And the scalability of AI means that someone you can benefit from it.
AI can make learning more addictive and, for many, it already does. The reasons have little to do with cutting-edge advances in artificial intelligence and computer science and more to do with the fundamentals of what makes a student engaged, motivated, and enthusiastic.
What do we mean by addictive?
Saying that AI makes learning addictive refers to a sense of excitement, instilling in the learner a voracious appetite for self-improvement and growth. But more importantly, it continues long after you've accomplished what started your journey. Basically, this comes down to sustained, long-term motivation. Creating self-motivated learners is a challenge most educators face, and a mountain of educational research addresses this topic.
Education has always focused on the human element and it is difficult to imagine a world where machines can replace it.
It is difficult to overstate the importance of motivation. Whether you are learning to speak a new language or taking the first steps towards a career in programming, learning is inherently iterative, where the learner gradually develops confidence and fluency over time. Prolific programming educator Zed Shaw once described this point like "climbing a mountain of ignorance." The first months, when they don't have confidence and they don't understand the subject, they are the most difficult and it is very easy to give up. And that is why an external force is needed to encourage the student to move forward. Confidence, ability, and perhaps even greatness are just around the corner.
One of the examples of this point is Judit Polgár, widely considered the greatest chess player of all time and the youngest chess grandmaster in the world. Judit's father, László, believed that geniuses were made, not born, and that they only required sustained education and training from an early age. László, breaking with the social expectations of communist-era Hungary, chose to homeschool Judit and her two sisters, focusing intensely on chess.
And it worked. Even before she was a teenager, Judit was described as a potential prodigy similar to Garry Kasparov and Bobby Fischer. At age 15, she broke a record previously set by Fischer and two years later she defeated Boris Spassky, another chess heavyweight, in an exhibition match.
While the role of nature and nurture is hotly debated, particularly in analytical games like chess, it is clear that László's approach worked. By combining intense training with the inherent motivation factor that comes with individualized training, Judit became a force within the chess world before reaching adulthood. His sisters, Zsuzsa and Zsófia, also became great teachers.
In a post-retirement interview with Chess.com, Judit attributed the success of her father's teaching method to the confidence it instilled in her: “I think that having private classes in any field makes children improve much faster, and because of this, they gain much more confidence, which increases his speed and his appetite to improve. I think this is one of the most important things for any child, whether they are in school or not. If you can keep their curiosity, they can improve extremely quickly.”
Generative AI can handle the motivational aspect of learning (stimulus, relevance, and specificity) while avoiding the inevitable errors that arise from a uniform, one-size-fits-all education system. But how?
The search for relevance
Academic research on the impact of generative AI as a learning tool is still ongoing. Much of the existing academic literature is inherently speculative or anecdotal and looks at what could happen rather than what they have observed. This is an inevitable consequence of the novelty of generative AI. ChatGPT is very new and research takes time. As more researchers investigate tools like ChatGPT, it will be interesting to see how their findings.
As mentioned, motivation is essential for student success and relevance plays a very important role in achieving this. It is one of the fundamental factors of John Keller's ARCS (Attention, Relevance, Confidence, and Satisfaction) motivation model, a concept established in pedagogical theory.
Within the ARCS model, Keller identified several critical components of relevance, two of which seem especially pertinent to the topic of generative AI: needs matching, where the teacher matches content to the student's needs, and modeling, which shows students how to apply learning in a practical sense.
Generative AI is well positioned to achieve these components. As anyone who has used a GPT-4-based product can attest, you can create a hyper-personalized, highly specific lesson on almost any topic. In a matter of seconds, ChatGPT can tell you how trigonometry can be used in the real world or how a specific part of a computer science class relates to a broader context, even if it may seem abstract and confusing. These examples can be created spontaneously, often due to the student's unique requirements and requests. This process also works for educators.
Education has always been about the human touch and it is difficult to imagine a world where machines can replace it. Humans have an ineffable emotional intelligence that cannot be articulated solely in code. Generative AI expands the capabilities of often overstretched teachers. An example of how this could work is to modify, improve and adapt the learning material.
Typically, a teacher would need more time or energy to create worksheets that are customized for each student’s ability, interests or learning style. They are overworked, and class materials are expensive, often paid for by the teacher himself. But now they can generate personalized learning materials, at scale and on demand, with negligible cost to the school or teacher. Using a tool like ChatGPT, a teacher can paste in a lesson plan and, with simple written instructions, substantially change the format or presentation for an individual student while preserving the core material.
This process takes seconds, making it a convenient option for even the busiest teacher. It's a use case that I imagine many teachers will adopt along with other generative AI capabilities to ideate, correct, and suggest.
It's easy to see how the potential of generative AI for content adaptation could be combined with other proven learning methods, such as gamification.
Video games keep people interested by creating dopamine loops. These loops only work if there is some semblance of progress. For the gaming experience to be worthwhile, the game character must continue to evolve and improve. With each challenge, the character acquires new skills and equipment that will help him face more demanding future challenges. This mechanic is repetitive in itself, so the “loop” moves with the player, taking them to new locations and plots to maintain an element of novelty.
Generative AI allows these mechanics to be implemented in an educational environment. With endless variations of content tailored to the student's situation and ability, students can get the repetition and reinforcement needed for long-term success, without the content feeling tiring or boring. This cycle can continue as the student progresses through the topic, tackling more complex and advanced material as they go.
Person-centered learning
Content alignment is critical to long-term success. This includes both the curriculum, which must be relevant to the students' interests, and the people themselves. People have different initial incentives and abilities that need to be addressed from the beginning.
An article, published in the IEEE Signal Processing magazine, provides an overview of the potential impact of AI on human-centered learning and student engagement. As other pedagogical researchers have observed, students, and people in general, respond to different incentives during learning.
The author writes: “Some students exhibit hyperbolic preferences, overvaluing the present so much that future rewards are largely ignored. Some students show strong reactions even to non-monetary rewards. Some students demonstrate reference-dependent preferences, implying that utility is largely determined by their distance from a reference point, for example, a predefined goal or average performance.”
In short, some people want a sense of immediacy, others want some sort of non-tangible reward (a grade, certificate, or other form of recognition), and others are more focused on how the content will take them to a predetermined destination. These are factors that must be taken into account when developing learning material.
At the same time, it is essential to recognize that capabilities vary. Content must be articulated in different ways to be effective. While some may be comfortable with a dense, academically written explanation of a topic with field-specific jargon incomprehensible to outsiders, others may prefer something more accessible. This is why a one-size-fits-all approach succeeds for some but fails for many others.
And there is the relationship between teacher and student, which also plays a crucial role in motivating students. Robert Gower and Jon Saphier, two respected education writers, highlight three key messages of encouragement that work: “This is important,” “You can do it,” and “I won't give up on you.” It remains to be seen whether these feelings will maintain their impact when conveyed by an AI chatbot. But it is something that, with trivial effort, a system could be programmed to do.
While many of the components mentioned do not yet feature in a mainstream generative AI tool (particularly incentivization), others are firmly within reach. ChatGPT, for example, can provide high- and low-level topic explanations. You can respond to prompts to simplify or provide more detail or complexity. Much of the required functionality already exists, albeit on an ad hoc basis, and it is time for generative AI to play a larger role, not only in the classroom but also in the broader way that people participate in education.
Train constant learners
AI, particularly large language models, has the potential to revolutionize the way students learn. This change will be fundamentally beneficial, especially in terms of how individuals relate to learning and how it alters their consumption patterns.
Much of the focus has been on AI's ability to scale personalized education or democratize education beyond the halls of expensive college campuses. While I do not disagree with these assessments, it is essential to recognize the psychological and sociological impacts of these changes. The notion that AI could make learning not only “fun” but also deeply compelling seems realistic and imminent. Doing so will create a new generation of hyper-capable, hyper-passionate people who will be able to easily adapt to change and constantly update and hone their skills.
That will benefit individuals, the economy and, ultimately, society.
