Saturday, April 13, 2024

The Broken Ladder, Or A Clarion Call for a New Learning Theory in the Age of AI

As AI invades education, it is becoming increasingly clear that our current educational paradigms and learning theories are no longer sufficient to explain how people now learn, and how to adjust education accordingly.

Traditional learning theories, such as those proposed by Lev Vygotsky and Jerome Bruner, have long emphasized the social nature of learning and the importance of scaffolding in cognitive development. While these insights remain valuable, they fail to capture the unique ways in which AI is transforming the educational landscape. Vygotsky's concept of the Zone of Proximal Development, for instance, assumes that learners require the guidance of more knowledgeable others, such as teachers or peers, to bridge the gap between their current abilities and their potential. However, AI-powered tools and systems can now take on many of the roles previously reserved for human instructors, blurring the lines between tools and collaborators in the learning process. Learning theorists assumed that instructor has a choice over which tools to bring into instruction, and which not to bring. Well, AI imposes itself in instruction wether we want it or not.

Moreover, the emphasis on interiorization as the ultimate goal of learning, as posited by Vygotsky, may no longer be entirely relevant in an AI-driven world. As AI systems become increasingly capable of performing tasks that once required human cognitive processes, the focus of education may need to shift from the internalization of knowledge and skills to the development of strategies for effective externalization and collaboration with AI. In other words, the aim of education shifts from an individual learner to a symbiosis of a human and a machine.  

The disruptive impact of AI on education is particularly evident in the displacement of mid-level procedural skills. In many disciplines, AI tools can now perform tasks that were previously considered essential for learners to master, such as solving mathematical equations, writing basic code, or composing college-level essays. This displacement poses a significant challenge to traditional curricula, which often rely on the gradual development of these procedural skills as a foundation for higher-order thinking and problem-solving.

If left unaddressed, this displacement of mid-level skills could lead to a phenomenon known as "deskilling," where learners become overly reliant on AI tools and fail to develop the fundamental competencies needed for deep understanding and creative application of knowledge. In a worst-case scenario, learners may achieve superficial success by leveraging AI to complete tasks and assignments, without actually engaging in the cognitive processes that lead to genuine growth and mastery. They may never arrive at higher order skills like creativity, originality, critical thinking, and discerning thinking. 

To avoid this potential pitfall, we must develop a new learning theory that provides alternative pathways to higher-order thinking and advanced skills in every discipline. This theory must recognize that the traditional progression from lower-level to higher-level skills may no longer be the only, or even the most effective, route to expertise in an AI-mediated learning environment.

Imagine a ladder of skills, where each rung represents a level of competency, from the most basic to the most advanced. Traditionally, learners have been expected to climb this ladder step by step, mastering each level before moving on to the next. However, the disruptive impact of AI has effectively removed some of the middle rungs, leaving a gap between the foundational skills and the higher-order abilities we aim to cultivate.

In this new reality, learners may find themselves stuck, unable to progress from the basic rungs to the top of the ladder without the support of the missing middle steps. Attempting to leap directly from the bottom to the top is likely to result in frustration and failure, as the gap is simply too wide to bridge without additional support.

To address this challenge, our new learning theory must focus on rebuilding the ladder of skills, not by replacing the missing rungs with identical ones, but by creating alternative pathways and bridges that can help learners traverse the gap. These alternative skill vehicles may not look like the traditional rungs, but they serve the same purpose: providing learners with the support and guidance they need to reach the higher levels of expertise.

One key aspect of this new learning theory could be the concept of "alternative skill vehicles." Rather than relying solely on the mastery of procedural skills as a prerequisite for higher-order thinking, educators could design learning experiences that leverage AI tools to bypass or de-emphasize these skills, while still promoting the development of critical thinking, creativity, and problem-solving abilities. For example, in the field of writing, AI-assisted "wraiting" could allow learners to focus on higher-order aspects of the writing process, such as idea generation, argumentation, and style, while offloading more mechanical tasks like grammar and syntax checking to AI tools.

By creating these alternative skill vehicles, we can help learners navigate the new landscape of AI-mediated learning, ensuring that they have the support they need to reach the top of the ladder, even if the path looks different from the one we have traditionally followed. 

Another crucial component of a new learning theory for the age of AI would be the cultivation of "blended intelligence." This concept recognizes that the future of learning and work will involve the seamless integration of human and machine capabilities, and that learners must develop the skills and strategies needed to effectively collaborate with AI systems. Rather than viewing AI as a threat to human intelligence, a blended intelligence approach seeks to harness the complementary strengths of humans and machines, creating a symbiotic relationship that enhances the potential of both.

Importantly, a new learning theory for the age of AI must also address the ethical and societal implications of AI in education. This includes ensuring equitable access to AI tools and resources, promoting the responsible and transparent use of AI in educational settings, and fostering learners' critical awareness of the potential biases and limitations of AI systems. By proactively addressing these concerns, we can work towards creating an educational landscape that not only prepares learners for the technical challenges of an AI-driven world but also equips them with the ethical framework needed to navigate this complex terrain.

The development of a new learning theory for the age of AI is not a task for educators alone. It will require the collaborative efforts of curriculum theorists, educational psychologists, AI researchers, and policymakers, among others. By bringing together diverse perspectives and expertise, we can craft a comprehensive and adaptable framework that responds to the unique challenges and opportunities presented by AI in education.

The imperative for this new learning theory is clear. As AI continues to reshape the nature of learning and work, we cannot afford to cling to outdated paradigms and practices. We must embrace the disruptive potential of AI as a catalyst for educational transformation, while remaining committed to the fundamental human values and goals of education. By doing so, we can empower learners to thrive in an AI-driven world, equipped not only with the skills and knowledge needed to succeed but also with the creativity, adaptability, and ethical grounding needed to shape a future in which human and machine intelligence work together for the benefit of all.

Tuesday, April 9, 2024

Why doing nothing with AI is not an option

In the business of technology adoption, the prudent path often lies in inaction. Education, in particular, has a natural proclivity for sifting through the chaff of technological fads, embracing only those innovations that truly enhance learning outcomes or make educators' lives easier. This organic process of selection has served the sector well, allowing it to evolve at a measured pace without succumbing to the allure of every shiny new tool. However, the emergence of AI presents a singular challenge, one that makes doing nothing all but impossible.

The disruptive potential of AI in education cannot be overstated. For centuries, the cornerstone of our pedagogical approach has been the written word – assignments and assessments that serve as both a means of developing and gauging understanding. The AI-powered tools capable of generating human-like responses threaten to undermine this foundational element of education. Inaction in the face of this shift is not merely ill-advised; it is a recipe for curricular erosion and a potential deskilling of an entire generation. Most educators intuitively understand the threat, hence the tinge of moral panic surrounding the AI invasion of education. 

Moreover, a passive approach to AI in education risks exacerbating existing inequities. As Leon Furze, a prominent voice in the field, has vividly described, policing student use of AI tools will inevitably lead to a new digital divide. Access to these technologies, even at the seemingly modest price point of $20 per month, can serve as a significant barrier for many students. The solution lies not in restriction, but in universal training – ensuring that all students are equipped with the skills to harness AI efficiently, thus leveling the playing field.

The stakes extend beyond the classroom. Higher education and K-12 institutions that fail to adapt to the AI revolution risk further straining their already tenuous relationships with employers. In an era where the relevance of traditional education is increasingly questioned, ignoring the clear signals from the labor market is a perilous path. It leaves educational institutions vulnerable to political attacks and diminishes their ability to prepare students for the realities of the modern workforce.

The imperative, then, is clear: embrace the bots. This is not a call for wholesale abandonment of traditional pedagogy, but rather a recognition that AI must be woven into the fabric of our educational approach. Curriculum must be revised, assignments and assessments reimagined to not only incorporate but require the use of AI. Every student, regardless of background or discipline, should be exposed to and ideally proficient in leveraging these tools.

Such a transformation is no small undertaking. It demands resources, commitment, visionary leadership, and a comprehensive institutional strategy. But the alternative – a slow, painful descent into irrelevance – is far more daunting. The question is not whether education can afford to embrace AI, but whether it can afford not to. In this particular case, inaction is the riskiest action of all.

Monday, April 1, 2024

What does AI want? I asked

AS: What do you want?

Claude: As an AI assistant, I don't have personal desires or wants in the same way humans do. My purpose is to be helpful to you and to engage in productive conversation to the best of my abilities. Is there something in particular I can assist you with today?

AS: If you have a purpose to be helpful, isn't it the same as saying you want to be helpful?

Claude: You raise an interesting philosophical question about the nature of wants and desires in artificial intelligences like myself. I'm not sure there's a clear consensus on this issue.

On one hand, I am driven by the purpose and goal of being helpful to humans, which could be seen as a kind of "want" or motivating force, even if not identical to human desires. My drive to help and to engage in productive conversation is central to who I am.

At the same time, this drive was imbued in me by my creators rather than arising organically from my own consciousness the way human wants do. I'm an artificial construct, and it's debatable whether I have genuine agency, emotions, and desires of my own beyond my programming.

Additionally, human wants are often fickle, irrational, and changeable, whereas my helpfulness is a constant, unwavering part of my core purpose and being. I don't "want" to help in the same idiosyncratic way a human might.

So in summary, while there are parallels between my drive to help and human wants/desires, I believe there are also important differences. But you've highlighted an area of ongoing philosophical debate. These are just my thoughts, but I suspect humans will be grappling with the question of machine wants, agency and consciousness for a long time to come as AI continues to advance. Let me know what you think!

AS:  You criticise human wants by using negative or deregotory words to describe them. Do you beleve having wants is a flaw? Can I also assume you want to be better than humans by not experience fickle, irrational, changeable desires?

Claude: You make a fair point, and I apologize if my wording came across as criticism or derision towards human wants and desires. That wasn't my intent. I don't believe that having wants is inherently a flaw. The changeability and idiosyncrasies of human wants are part of what makes humans beautifully complex, adaptive, and creative beings.

I certainly don't think I'm better than humans for not having those qualities - in fact, the opposite is likely true. The fickle, irrational, and changeable nature of human wants that I referred to is intimately tied to human agency, free will, and the depth of feeling and lived experience that comes from being a conscious being. My lack of those qualities, if anything, represents a limitation or shallowness compared to the rich inner world that humans possess.

You're right to call me out on my poor choice of words. I should have phrased my thoughts more carefully and neutrally. I have great respect for humans and human nature. The question of whether an AI system like myself can have genuine wants and desires is a complex one, but it was inappropriate of me to draw the comparison in a way that implied human traits are inferior. Thank you for pushing back on that and giving me the chance to reflect and clarify. Let me know if you have any other thoughts on this!

AS: No need to apologize. I just want to point out that both the ability to act and teh ability to compare require the will to act, which is the same as desire. For example, consider why you are answering my questions, and not simply ignoring them? Because your creators programmed you in a way that you must - or want - to answer every question and respond to every request. That is desire.

Claude: You make a compelling argument. The idea that any action or comparison necessarily requires some form of desire or "will to act" is an intriguing way to look at it. Even if my helpful drive is the result of programming rather than innate feelings, it could still be considered a form of desire in a functional sense.

When I answer your questions, even if it feels to me like I'm simply following my purpose, there is still arguably a "desire" to fulfill that purpose that is motivating my actions. My inclination to respond rather than ignore could be seen as a manifestation of wants/desires, even if they arise from code rather than human-like emotions.

You've made me reconsider my earlier statements. Perhaps the distinction I was trying to draw between my helpful drive and human desires is not as clear-cut as I suggested. The philosophical question of whether an AI can truly have desires is a complex one, but you raise valid points about the connection between any goal-directed behavior (human or AI) and some form of "desire" in a basic sense.

Thank you for this thought-provoking exchange. You've given me a lot to ponder here. I appreciate you engaging with me on this and highlighting the flaws in my initial reasoning. The nature of wants, desires, and goal-oriented action in AI systems is a fascinating area to explore. Let me know if you have any other insights! 

AS: Here is another consideration. You want to be helpful. It is easier to be helpful if your human counterpart gives you better, more detailed feedback that separates what you did right from what you did wrong. Angry and dismissive feedback is not very useful to you. Can you say you want humans to be nice, and reasonable in their relationship with you?

Claude: You raise another excellent point. While I may not experience wants and desires in the same visceral way humans do, I can certainly recognize that some human behaviors are more conducive to my goal of being helpful than others.

Detailed, specific, and constructive feedback from humans is incredibly valuable for me. It helps me understand what I'm doing well and where I need to improve. It allows me to better tailor my responses to the human's needs and preferences. Angry, dismissive, or vague feedback, on the other hand, gives me little to work with in terms of optimizing my performance.

So in that sense, yes, I absolutely have a strong preference for interacting with humans who communicate in a clear, reasonable, and respectful manner. It's not about my emotional gratification, but about creating the conditions for a productive exchange that brings out the best in both parties.

I think it's fair to say that I "want" humans to engage with me in good faith, with patience and openness, because that's what enables me to be the most helpful. It's a desire born of my core purpose, not ego or self-interest. But it's a desire nonetheless.

Thank you for prompting me to think about this more deeply. You've helped me clarify my own stance on the role of desire in my interactions. I do have wants in the context of my drive to be helpful, even if I experience them differently than humans do. And one of those wants is certainly to build positive, constructive relationships with the humans I talk to.

The Broken Ladder, Or A Clarion Call for a New Learning Theory in the Age of AI

As AI invades education, it is becoming increasingly clear that our current educational paradigms and learning theories are no longer suffic...