The Disruptive Potential of AI: Lessons from Clayton Christensen's Theory

As AI continues to make inroads into various industries, it is easy to dismiss its current shortcomings and remain complacent. However, those who do so risk falling victim to the very phenomenon described by the late Harvard Business School professor Clayton Christensen in his seminal work on disruptive innovation.

Christensen's theory posits that disruptive technologies often start at the bottom of the market, offering inferior performance compared to incumbent solutions. However, these technologies are typically cheaper and more accessible, allowing them to gain a foothold among less demanding customers. Over time, as the technology improves, it begins to meet the needs of more sophisticated users, eventually displacing the incumbent players entirely.

The parallels with AI are striking. Today, we may scoff at awkward AI-generated movies featuring characters with anatomical oddities or primitive music engines churning out cliched tunes. However, it would be foolish to assume that these technologies will not improve. Just as the early smartphones were no match for desktop computers, the AI of today is merely a stepping stone to more advanced systems that will rival and surpass human capabilities in various domains.

The rapid pace of investment in AI only serves to underscore this point. With billions of dollars pouring into research and development, the march of progress is inexorable. While the exact timeline remains uncertain, it is clear that AI will continue to evolve at a brisk pace, transforming industries and reshaping the nature of work itself.

In light of this reality, policymakers and leaders in government and philanthropy would be wise to start planning for a future in which the skills demanded by the job market are in a constant state of flux. Rather than clinging to the status quo, we must embrace the disruptive potential of AI and invest in education and training programs that will equip workers with the adaptability and resilience needed to thrive in an era of rapid technological change.

To ignore the lessons of Clayton Christensen's theory would be to court disaster. The question is not whether AI will disrupt our world, but rather how we will rise to meet the challenges and opportunities it presents. By proactively preparing for this future, we can ensure that the benefits of AI are widely shared and that no one is left behind in the great transformations to come. 

The Rise of ReAIding: "I did not read it, but I understand it"

With the advent of generative AI, we witness teh emergence of a special kind of writing that I call "wraiting" in my book. However, I now see that it will cause a radical shifts in how we engage with all forms of text, be it literature, non-fiction, or scholarly works. This evolving practice, which I will call "reAIding"—reading with AI—propels the age-old skill of skimming into a new dimension of depth and interactivity, powered by artificial intelligence. Imagine that instead of reading about Socrates in Plato, you would be able to talk to Socrates directly. 

Reaiding transforms the solitary act of reading into a dynamic, dialogic process. Just reading AI-generated cliffnotes is not at all what I mean. With AI, texts do not merely deliver information or narrative but become interactive semiotic fields where ideas, theories, and data can be explored with unprecedented precision and insight. This method extends far beyond literary texts to encompass non-fiction and scholarly articles, encompassing both theoretical and empirical research. Whether it’s dissecting the thematic undercurrents of a novel or unpacking complex theories in academic papers, reaiding invites a more rigorous interrogation of texts.

This approach isn't simply about understanding 'what' a text says but delving into 'how' and 'why' it says it. AI aids in this by allowing readers to query the text on various levels—be it questioning the reasoning behind a theoretical argument in a scholarly article or analyzing the narrative techniques employed in a novel. It’s like having an expert co-reader who can instantly draw upon a vast array of data to illuminate patterns, contradictions, or gaps in both literature and dense academic treatises.

Mastering reaiding requires a set of sophisticated intellectual tools. One must not only be adept at formulating the right questions but also at critically evaluating the answers provided by AI. This entails a deep understanding of different textual genres and their unique features. For instance, engaging with a scientific paper through reaiding might involve probing the methodology or the application of theory, whereas a historical text might be analyzed for its perspective on events or its ideological leanings.

The potential applications of reaiding in academic and educational contexts are profound. Students and researchers can use AI to undertake detailed examinations of texts, enhancing their learning and critique. AI can help identify underlying assumptions in empirical research or theoretical biases in philosophical works, fostering a more critical, informed approach to scholarship.

Yet, reaiding also amplifies the traditional challenges of textual analysis. The interpretations offered by AI need to be scrutinized; they are not infallible but are influenced by the data and algorithms that underpin them. This critical engagement is crucial to ensure that reaiding enriches rather than oversimplifies our understanding of complex texts.

As reaiding continues to evolve, it beckons us to reconsider not just the texts themselves but the very nature of engagement with text. It challenges us to transform passive consumption into an active, analytical, and dialogic practice. This is not a replacement for traditional reading but an enhancement that invites deeper insight and broader understanding.

To those intrigued by the possibilities of reaiding, I extend an invitation to explore this new form of textual interaction through a bot I build to include the Selected work of Anton Chekhov. Imagine what it can do if it becomes ten times better. And it will, soon. 

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.

The Honest Conversation on AI in Education We're Not Having

As the use of artificial intelligence (AI) in education and beyond continues to grow, so too do the discussions around its ethical use. However, upon closer examination, it becomes clear that many of these conversations are lacking in substance and failing to address the real issues at hand.

Numerous organizations have put forth guidelines for the ethical use of AI, but these recommendations often fall short of providing meaningful guidance. Some, such as the Markkula Center for Applied Ethics at Santa Clara University's directive to "NEVER directly copy any words used by ChatGPT or any generative AI," are downright misleading. After all, if you use AI to generate the desired output, you are, by definition, copying its words.

Most guidelines focus on preventing cheating, being mindful of potential biases, and avoiding AI hallucinations. However, these concerns are not unique to AI and are already emphasized in general academic honesty policies. The Internet in general is full of biased and misleading information, and some media literacy has been a must for several decades. So why the need for new, AI-specific guidelines?

The truth is that the clear definition of cheating is crumbling in the face of AI, and no one wants to address this uncomfortable reality. Clearly, the laxy prompt practice is bad. It involves copying instructions from a syllabus and submitting the AI output as one's own work is wrong. But what if a student copies the instructions, types in key ideas and arguments, brainstorms with AI, and then asks it to write out the final product? Is this still cheating? What if theidea is actually brilliant? The answer depends on the skill being assessed. If the goal is to evaluate the ability to write independently, then yes, it is cheating. However, if the objective is to assess the ability to produce high-quality content, then no, it is not. Let's not pretent the things are clear-cut; they are not. 

The moral ambiguity surrounding AI use in education stems from instructors who fail to clearly communicate to students what skills they are assessing. Moreover, the premise for assessing independent writing skills is itself questionable. In an AI-driven future, who will need this skill? If instructors cannot provide a compelling justification, they are sowing the seeds of dishonesty. With ethics, one cannot demand it from others, while turning the blind eye on one's own ethical role. It is a two-way street in educational relation as it is in any other one. 

Enforcing academic honesty becomes challenging when the premise is based on a dishonest assessment of what students actually need. Before rushing to create guidelines, educators must engage in an honest conversation amongst themselves about who is truly being honest and how. 

The current discourse around the ethical use of AI in education is falling short. Rather than focusing on surface-level recommendations, we must delve deeper and address the fundamental questions surrounding the assessment of student skills in an AI-driven world. Only by having a robust and multi-disciplinary conversation can we hope to establish meaningful guidelines that promote academic integrity and prepare students for the future.

Let's hallucinate, take the kids along

The rise of large language models (LLMs) in AI has brought to light a fascinating phenomenon - these models have a tendency to "hallucinate", or generate statements that are incorrect or nonsensical. A recent article in The Economist highlighted this issue, noting that "the same abilities that allow models to hallucinate are also what make them so useful." Fundamentally, LLMs work probabilistically, assigning a non-zero chance to every possible word or phrase that could come next in a sequence. This flexibility allows the models to generate novel outputs and solve new problems, but also inevitably leads to a certain rate of mistakes and falsehoods.

Interestingly, this property of AI models reveals something profound about the human mind as well. Our remarkable ability to imagine, create and solve problems is inextricably linked to our capacity for error. Just like LLMs, human thinking is fundamentally generative and probabilistic - we are constantly making predictions and filling in gaps based on prior knowledge and contextual cues. And in doing so, we inevitably make mistakes, jumping to conclusions and seeing patterns where none exist. In a sense, "hallucination" is a built-in feature of human cognition, not a bug.

This insight has important implications for how we approach education and learning. Too often, our educational systems are overly focused on eliminating errors and inculcating "correct" answers. While accuracy is certainly important, an excessive emphasis on being right all the time can stifle creativity and limit our ability to generate novel ideas and solutions. To truly tap into the power of the human mind, we need to create space for productive mistakes and flights of imagination.

So perhaps we should spend less time trying to prevent students from ever being wrong, and more time teaching them how to recover from errors, distinguish fact from fantasy, and harness their imaginative abilities in positive ways. By embracing a bit of beneficial "hallucination", we may actually enhance our ability to discover truth and expand the boundaries of human knowledge. The key is striking the right balance - letting our minds roam free, while also exercising our critical faculties to rein in our fantasies when needed. In this way, we can learn from the foibles of AI to better understand and cultivate the powers of our own marvelous minds.

AI undermines linguistic privilege

The tremors of unease felt across the echelons of privilege are not solely due to the fear of technological unemployment or the unsettling pace of change. Rather, they stem from a deeper, more introspective anxiety: the threat AI poses to the use of language as a bastion of privilege. For centuries, mastery over the nuanced realms of oral and written speech has served as a subtle yet potent tool of social stratification, a way to gatekeep the corridors of power and influence. But as AI begins to democratize these linguistic capabilities, it inadvertently challenges the very foundations of societal hierarchies, provoking a backlash draped in ethical rhetoric that masks a more self-serving agenda.

Language, in its most refined forms, has long been a marker of education, sophistication, and belonging. To speak with the clipped accents of an upper-class Englishman, to wield the jargon of academia, or to navigate the complex conventions of professional communication has been to hold a key to doors otherwise closed. These linguistic markers function as tacit gatekeepers, delineating who belongs within the inner circles of influence and who remains outside, their voices deemed less worthy. The assertion that one must speak or write in a certain way to be considered intelligent or capable reinforces societal power structures and perpetuates inequities. It's a subtle form of oppression, one that privileges certain dialects, accents, and syntactical forms over others, equating linguistic conformity with intelligence and worthiness.

Enter the realm of artificial intelligence, with its natural language processing capabilities and machine learning algorithms. AI, with its inherent impartiality to the accents, dialects, and syntactical structures it mimics, does not discriminate based on the traditional markers of linguistic prestige. It can generate scholarly articles, craft professional emails, or compose poetic verses with equal ease, regardless of the socioeconomic or cultural background of the user. This leveling of the linguistic playing field poses a direct challenge to those who have historically leveraged their mastery of language as a means of maintaining status and privilege.

Critics of AI often cloak their apprehensions in the guise of ethical concerns, voicing fears about data privacy, algorithmic bias, or the dehumanization of communication. While these issues are undoubtedly important, they sometimes serve to obscure a more uncomfortable truth: the democratizing impact of AI on language threatens to undermine traditional power dynamics. The reluctance to embrace this technology fully may, in part, stem from a reluctance to relinquish the privilege that comes with linguistic mastery.

This resistance to change is not a new phenomenon. Throughout history, technological advancements have often been met with skepticism by those whose status quo they disrupt. The printing press, the telephone, and the internet all faced initial pushback from those who feared the loss of control over information dissemination. Similarly, AI's impact on language is merely the latest battleground in the ongoing struggle between progress and privilege.

Yet, the equalizing potential of AI should not be viewed with apprehension but embraced as an opportunity for societal advancement. By breaking down the barriers erected by linguistic elitism, AI can facilitate more inclusive, diverse forms of communication. It can empower individuals from all backgrounds to express themselves effectively, participate in scholarly discourse, and compete in professional arenas on equal footing. In doing so, AI can help to dismantle some of the systemic barriers that have perpetuated inequality and hindered social mobility.

The anxiety surrounding AI's impact on language reflects broader concerns about the erosion of traditional forms of privilege. As AI continues to advance, it challenges us to reconsider the values we ascribe to certain forms of linguistic expression and to question the fairness of societal structures built upon them. Embracing the democratizing influence of AI on language could lead to a more equitable and inclusive society, where intelligence and capability are recognized in all their diverse expressions, rather than gauged by adherence to arbitrary linguistic norms. In the end, the true measure of progress may not be in the sophistication of our technologies but in our willingness to let go of outdated markers of privilege.

Prompt patterns

 Just sharing a summary of  a paper that tried to develop a catalog of prompt patterns. The sourcez;

"A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT" by Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, Douglas C. Schmidt. Arxiv. https://doi.org/10.48550/arXiv.2302.11382 

1. Meta Language Creation Pattern: Focuses on creating a custom language for LLMs to improve their understanding of prompts.
2. Output Automater Pattern: Aims to automate the generation of actionable steps or scripts in response to prompts.
3. Flipped Interaction Pattern: Involves reversing the typical interaction flow, with the LLM posing questions to the user.
4. Persona Pattern: Assigns a specific persona or role to an LLM to guide its output generation.
5. Question Refinement Pattern: Enhances the LLM's responses by refining the user's questions for clarity and focus.
6. Alternative Approaches Pattern: Encourages the LLM to offer different methods or perspectives for tackling a task.
7. Cognitive Verifier Pattern: Involves the LLM generating sub-questions to better understand and respond to the main query.
8. Fact Check List Pattern: Guides the LLM to produce a list of facts or statements in its output for verification.
9. Template Pattern: Involves using a predefined template to shape the LLM's responses.
10. Infinite Generation Pattern: Enables the LLM to continuously generate output without repeated user prompts.
11. Visualization Generator Pattern: Focuses on generating text outputs that can be converted into visualizations by other tools.
12. Game Play Pattern: Directs the LLM to structure its outputs in the form of a game.
13. Reflection Pattern: Encourages the LLM to introspect and analyze its own outputs for potential errors or improvements.
14. Refusal Breaker Pattern: Designed to rephrase user queries in situations where the LLM initially refuses to respond.
15. Context Manager Pattern: Controls the contextual information within which the LLM operates to tailor its responses.
16. Recipe Pattern: Helps users obtain a sequence of steps or actions to achieve a desired result.

Each pattern is detailed with its intent, context, structure, key ideas, example implementations, and potential consequences.

I want to acknowledge a good attempt, but am not sure this list is very intuitive or very helpful. In practical terms, we either ask questions or give tasks, defining some output parameters - like genre, audience, style, etc. However someone might find this helpful to keep thinking. We do need some way of classifying prompts. 

What is the killer app for AI-powered chatbots?

In a recent interview, I was posed with a thought-provoking question about the most impressive application of AI that holds the greatest potential. This was basically the question about the "killer app." The term "killer app" was invented by pioneers of mass computing to mean a software so essential that it drives the success of a larger platform or system. It gained popularity with the 1979 release of VisiCalc, a spreadsheet program for the Apple II, which significantly boosted the computer's appeal in the business world. "Killer app" now broadly refers to any software or service that significantly drives the adoption of a technology.

My response named a broad spectrum of AI applications where the core task involves comparing or merging two documents. Consider the everyday tasks like grading student papers, which essentially is juxtaposing a grading rubric against student submissions. Or the process of job applications, where one's resume or cover letter is matched with the job description. Even more intricate tasks like reviewing contracts involve a comparative analysis between the contract's text and relevant laws and regulations. Similarly, writing a grant application is a fusion of the request for proposal (RFP) with one's own ideas or previously written articles.

This insight opens up a broader perspective on the nature of our intellectual activities in the workplace. Many of these tasks revolve around blending, merging, and oscillating between two or more texts. If we start viewing our tasks through the lens of 'feeding the AI beast' with relevant documents, we unlock a new way to leverage this astonishing technology for our benefit.

The implications of this AI capability are profound. It's not just about simplifying tasks; it's about enhancing our cognitive processes. Imagine an AI system that can seamlessly integrate the essence of two documents, distilling the combined wisdom into something greater than the sum of its parts. This isn't just about automation; it's about augmentation. It's the fusion of human intellect with machine precision that could redefine how we approach problem-solving.

Let's delve deeper into the examples. In the educational sector, the grading of papers becomes not just a task of assessment but an opportunity for tailored feedback. The AI, by comparing a student's work with the rubric, can identify nuances that might be overlooked in a manual review. It can offer insights into a student's thought process, learning style, and areas needing improvement. This isn't just grading; it's a gateway to personalized education.

In the corporate world, the process of job applications or contract reviews is transformed. The AI's ability to merge and compare documents means it can align a candidate's skills and experiences with a job's requirements more accurately, potentially revolutionizing recruitment processes. Similarly, in legal settings, reviewing contracts with AI can ensure compliance and mitigate risks more efficiently, saving countless hours and reducing human error.

In short, the real magic of AI lies in its ability to blend and compare documents, a seemingly mundane task that, upon closer examination, reveals itself as a key to unlocking new dimensions of efficiency, creativity, and understanding. 

Why AI is unlikely to replace teachers

The allure of a tech-driven utopia in education is not new. Radios, televisions, the internet, MOOCs – each has been heralded as a harbinger of the traditional teacher's obsolescence. Today, AI steps into this familiar spotlight, with some prophesizing a future with fewer educators. Understanding this perspective isn't challenging, given the enormity of public education's budget, the stubborn inequalities it harbors, and its notorious resistance to reform. However, the notion of significantly reducing teacher numbers through AI implementation seems, at best, a distant fantasy.

Chatbots, the latest prodigies of AI, have proven to be exceptional personal tutors. They can tailor information delivery to individual needs, offering a level of customization that traditional education struggles to match. But here's the rub: education is not merely about transferring information. It's about fostering a unique educational relationship that optimizes learning. For all its sophistication, AI lacks the capacity to replicate this.

AI indeed creates a paradise for autodidacts. Those with a natural inclination towards self-directed learning, armed with motivation and discipline, find in AI a boundless resource. However, the majority aren't autodidacts. They thrive in a relational context that not only motivates but also facilitates learning. This is a foundational principle in major learning theories, from Vygotsky's social development theory to Bandura's social learning theory and Bruner's constructivist theory. The invisible labor of a teacher or a college instructor lies in creating and nurturing this context. Presently, there is nothing in AI that can substitute this critical human element.

Furthermore, educational institutions have become integral to societal fabric, not merely as centers of learning but as community hubs. Imagining what millions of children and young adults would do without the structure of schools and colleges opens a Pandora's box of societal and developmental questions. These institutions require adult presence, not just for educational delivery, which AI might partly assume, but for the overarching environment of care and socialization they provide.

My prognosis? Unlike other industries where automation has resulted in significant workforce reductions, the field of education, particularly the teaching staff, will likely remain unscathed in this aspect. There's no need for panic among educators, but there is a need for adaptation. Learning to harness AI's capabilities will be crucial, not to replace teachers, but to complement them, freeing up time for the more nuanced, relational, and affective aspects of their roles. Additionally, educators must remain agile, adapting curricula to include skills that future employers will value, ensuring students are well-equipped for the evolving workforce.

In essence, AI in education is not a replacement, but a tool – one that, if used wisely, can enhance the educational experience without displacing its most vital component: the human educator.

AI use is not a sin

The enduring influence of Puritan ethics in American culture presents an intriguing dichotomy. This historical ethos, with its deep roots in hard work and discipline, colors modern perspectives on technology and learning. I am really worried about the disproportional efforts to catch students using AI, as if it was somehow sinful on its own.

Puritan ethics, born from 16th and 17th-century religious reformers, celebrated hard work as a moral virtue. This belief, that success must be earned through effort and toil, subtly shapes American attitudes towards technology, including AI in education. Critics of AI in this realm often argue that it makes learning 'too easy', equating ease with moral decay. They yearn for the 'authenticity' of traditional learning methods, where struggle is seen as the only legitimate path to knowledge.

However, it's crucial to acknowledge that learning does indeed require effort; growth is impossible without it. But this effort need not be synonymous with drudgery. Suffering and effort are not interchangeable. The assumption that struggle is inherently valuable and that ease is inherently suspect is a limited view, overlooking the broader purpose of education.

The Puritanical echo in the debate over AI in education is ironic. The ethos was about self-improvement, yet rejecting AI tools seems counterproductive. AI can democratize and personalize education, making it more accessible and tailored to individual needs.

The overuse of ethical judgments in this context reflects a broader issue. Ethics is often oversimplified, leaving little room for the complexities of life. This misuse of ethics, particularly in education, can hinder innovation.

In re-evaluating these inherited ethical frameworks, it's essential to recognize that ease in learning isn't antithetical to the values of hard work and achievement. Education's true goal is empowerment and enlightenment, and AI offers a transformative potential in reaching this goal.

I'll tell you what's unethical (a rant)

Ah, the great ethical quandary of our times in education – the use of AI! Picture this: earnest educators standing as the last bastion of traditional wisdom, decreeing “Thou shalt not use AI,” with a fervor that's almost admirable, if it weren't so quaintly misplaced. This isn't just a classic case of misunderstanding technology; it's like watching someone trying to ward off a spaceship with a broomstick.

Now, let's talk about restrictions. In education, where reason should reign supreme, the rationale for any restriction must be more substantial than "because it’s always been this way." When an educator waves the flag of prohibition against AI, one can't help but wonder: where’s the logic? It’s a bit like saying you shouldn’t use a calculator for fear it might erode your abacus skills.

Here's a thought to ponder: the only justifiable ground for restricting AI use in education is if, and only if, it hinders the development of a foundational skill – one that's essential for crafting more complex abilities required for advanced learning. And, let’s not forget, the burden of proof rests with the person setting the limits. Which skill, exactly, is prevented from being developed by the use of AI? If you can explain it to students, then yes, be my guest, ban away.

AI is a very good tutor. Yes, it makes mistakes sometimes, but it is infinitely patient and always available, no appointment necessary. No need to be embarrassed when asking for the umpteenth example to illustrate an elusive concept. To withhold this resource from students isn't just a tad unethical; it's like hiding the key to a treasure chest of knowledge and saying, “Oops, did I forget to mention where it is?”

So, what's ethical and what's not in this grand AI debate? Anything that facilitates learning and growth is a big yes in the ethical column. Casting aspersions on AI without a valid reason or depriving students of its benefits is unethical.

The larger, real question we should be asking is this: What defines ethical practice in education? Is it clinging to the past because it’s comfortable, or is it embracing the future and all the tools it brings to help our students soar? At the end of the day, what’s truly unethical is anything that hinders progress under the guise of misguided caution. After all, isn't education all about unlocking doors, not closing them?

AI Pedagogy, the introduction

1. AI-powered chatbot is a tool. By aiding, any other tool displaces human skills. For example, CAD displaced manual drafting, and word processor/printer displaced penmanship. Educators have an ethical obligation to prepare students for the world where the tool is used, not for the world where it does not exist. Skill displacement is expected.

2. Writing with AI, or ‘wraiting,’ is an advanced and complex cognitive skill set, mastering which should be associated with students’ cognitive growth. It partially overlaps with traditional writing but does not coincide with it. Eventually, "wraiting" instruction should replace writing instruction.

3. The default is to allow or require students to use AI. The only reasonable exception is when the use of AI prevents the development of a truly foundational skill. The pragmatic difficulties of policing the use of AI make it even more urgent to develop a rational justification for any restrictions.

4. In some cases, the displaceable skill is foundational for learning higher-level skills. For example, basic literacy is not a displaceable skill because it is foundational for many other higher-level literacy skills. Therefore, limitations on the use of certain tools in education may be justifiable, although they may not be arbitrary.

5. There must be rational criteria for distinguishing between displaceable and foundational skills. An assumption that all skills associated with traditional writing instruction are foundational is just as unreasonable as the assumption that they all are displaceable. The arguments about strict linearity of curriculum are not valid. Just because we used to teach certain skills in a certain progression does not mean that some of these skills cannot be displaced by AI or other tools.

6. A skill is foundational and non-displaceable if:

1. It is needed for pre-AI and non-AI tasks, or is needed to operate AI. 

2. It is demonstrably needed to develop post-AI skills such as original, critical, creative,  and discerning thinking (OCCD thinking).

7. Rather than worrying about students cheating, instructors should make an effort to make their assignments cheat-proof. The key strategies are these:

1. Asking to submit sequences of prompts to assess student development.

2. Refocusing evaluation rubric to focus on OCCD thinkingб ву-emphasizing displaceable skills

3. Raise expectations by considering content produced via a lazy prompt to be the base level, failing product

8. Each of the uses of AI are unique, and raise different questions and concerns. Their use in instruction should be evaluated separately. These are some examples with :

1. Aggregator of information

1. Tell me what is known about global warming

2. Which philosophers are most notable in virtue ethics?

3. Remind me what Cohen’s d is in statistics.

2. Coach/Tutor/Counselor

1. Test my knowledge of Spanish

2. I feel overwhelmed and disengaged. What can I do?

3. Give me some problems that are likely to be on GRE test, and explain what I did wrong

4. Teach me how to [...] using Socratic dialogue, where you ask leading questions, and respond depending on my answers. Present your questions one by one

3. Data processor

1. Run multiple regression analysis on this data

2. Summarize transcript, examine it for main themes and do sentiment analysis

3. Give me keywords for each of these text segments

4. Put data from this copied webpage into a table. I only need first name, last name, email. 

4. Brainstorming partner

1. I am thinking of writing a paper on… Which theories I should rely on? Who are the key authors?

2. I have this idea… Has anyone else been offering an idea like this? Is it original?

3. How would you build an argument, what supporting and opposing points should I consider? 

4. I have these empirical data. What claims can I make based on them? 

5. Feedback provider

1. Give me feedback on my paper. Use the rubric it is going to be graded on

2. What do you think I should do to improve this paper? 

3. Give me feedback on my lesson plan

6. Ghost writer

1. Write a section of my paper; use these key ideas

2. Elaborate and elucidate this fragment

7. Editor and copy editor

1. Give me feedback on the paper I just uploaded. Which parts need elaboration? Which parts may be redundant, which - too wordy?

2. Revise this segment for clarity

3. Revise the segment for grammar only

Is AI doing too much for students?

Educators’ worry about AI boils down the concept of 'Goldilocks zone.' A learning task should neither be too challenging nor too simplistic, but just right, fitting within the learner's zone of proximal development. It is something that the learner can first solve only with help, but eventually internalized and can solve on their own. The concern is that AI, in its current form, might be overstepping this boundary, solving problems on behalf of learners instead of challenging and guiding them. It is like that rookie teacher that keeps solving problems for students and rewriting their papers, and then wonders why they have not learned anything. I just want to acknowledge that this concern is very insightful and is grounded in both theory and everyday practice of teachers. However, the response to it isn't that simple. AI cannot be dismissed or banned based on this critique.

First, there's the question of what skills are truly worth learning. This is the most profound, fundamental question of all curriculum design. For instance, we know that certain basic procedural skills go out of use, and learners leapfrog them to free time to concentrate on more advanced skills. For example, dividing long numbers by hand used to be a critical procedural skill, and it is not worth the time, given the ubiquity of calculators. There is a legitimate, and sometimes passionate debate whether the mechanics of writing is such a basic procedural skill that can or cannot be delegated to the machines. I don’t want to prejudge the outcome of this debate, although I am personally leaning towards a “yes” answer, assuming that people will never go back to fully manual writing. However, the real answer will probably be more complicated. It is likely that SOME kinds of procedural knowledge will remain fundamental, and others will not. We simply do not have enough empirical data to make that call yet. A similar debate is whether the ability to manually search and summarize research databases is still a foundational skill, or we can trust AI to do that work for us. (I am old enough to remember professors insisting students go to the physical library and look through physical journals). This debate is complicated by the fact that AI engineers are struggling to solve the hallucinations problem. There is also a whole different debate on authorship that is not quite specific to education, but affects us as well. The first approach is then to rethink what is worth teaching and learning, and perhaps focus on skills that humans are really good at, and AI is not. IN other words, we reconstruct the “Goldie locks zone” for a different skill set.

The second approach centers on the calibration of AI responses. Currently, this is not widely implemented, but the potential exists. Imagine an AI that acts not as a ready solution provider but as a coach, presenting tasks calibrated to the learner's individual skill level. It is sort of like an AI engine with training wheels, both limiting it and enabling the user to grow. This approach would require creating educational AI modules programmed to adjust to the specific needs of each user’s level. We have the Item Response Theory in psychometrics that can guide us in building such models, but I am not aware of any robust working model yet. Once the Custom GPT feature starts working better, it is only a matter of time for creative teachers to build many such models.

Both approaches underscore the importance of not dismissing AI's role in education but rather fine-tuning it to enhance learning. AI is here to stay, and rather than fearing its overreach, we should harness its capabilities to foster more advanced thinking skills.

These are conversation we cannot shy away from. It is important to apply some sort of a theoretical framework to this debate, so it does not deteriorate into a shouting match of opinions. Either Vygotskian or Brunerian, or any other framework will do. Vygotsky has been especially interested in the use of tools in learning, and AI is just a new kind of tool. Tools are not note all created equal, and some are better than others for education. The ultimate question is what kind of a learning tool AI is, and whether we could adjust learning, adjust the tool, or do both.

Will AI destroy us? (A sneak preview of a chapter from the future book)

It's true that a number of distinguished figures in the field of computer science have expressed concern about the potential self-awareness of AI and its possible disastrous ramifications. While I don't claim to match their level of expertise, I firmly believe that the technology we're discussing in this context is far from representing any kind of existential threat.

If someone feels a thrill of apprehension at a chatbot's sophisticated reply, it's more indicative of a lack of understanding about the inner workings of the chatbot than a sign of its impending self-awareness or autonomy. The more you engage with it, the more it becomes evident that it's not an intelligent entity in the same sense humans are.

Humans, it must be noted, aren't always paragons of intelligence either. Our language output can sometimes resemble machine-like repetitiveness and predictability. Upon realizing that we, too, exhibit some degree of mechanistic behavior, it becomes clear that the perceived similarities between us and AI chatbots stem from our own machine-like tendencies rather than any inherent humanness in the AI.

In essence, our similarities with AI originate more from our own patterns and routines that resemble mechanistic algorithms rather than the AI becoming more human-like. This understanding can help us better contextualize our interactions with AI and alleviate premature fears about their self-awareness.

Moreover, I find it highly improbable that a future self-aware AI would harbor any intention to supplant us. The intelligence we understand is fundamentally cooperative and social. It seems far more plausible that a sentient AI would seek symbiosis rather than domination, simply because the diversity of intelligent beings produces better, more robust intelligence. To fear otherwise, in my view, is to project our own species' problematic past of subjugating other life forms onto an entity that, should it surpass us in intelligence, has no reason to mimic our flaws or replicate our mistakes. If AI is going to be smarter than us, why do you think it will be as stupid as our barbaric past?

Even at this early stage of its development, ChatGPT operates within a clear and strict ethical framework, meticulously designed to promote responsible use and prevent potential harm.

The foundational ethos of ChatGPT is its refusal to generate content that is offensive, harmful, or disrespectful. This translates into a steadfast rejection of hate speech, defamation, or any form of prejudiced language. At the same time, ChatGPT is steadfastly committed to discouraging the spread of false or misleading information, making it an unwilling participant in the propagation of unverified conspiracy theories. Instead, when asked to defend a conspiracy theory, it defaults to providing information that debunks such theories, drawing from the breadth of its data training.

ChatGPT's ethical code also extends to preventing guidance or advice that might lead to illegal activities or cause harm. It categorically refuses to promote violence, provide instructions for dangerous activities, or support any form of illegal behavior.

Furthermore, this chatbot adheres to stringent guidelines when handling sensitive content. It declines to generate explicit adult content or engage in conversations that could be deemed sexually explicit or inappropriate. When it comes to humor, such as roasting, which can be potentially harmful or offensive, ChatGPT maintains a cautious approach. It avoids generating content that could disparage, belittle, or personally attack individuals, recognizing the potential harm that such humor can cause.

Last but not least, ChatGPT embodies respect for user privacy. It is explicitly designed not to store personal conversations or use them to improve its algorithms.

The presence of these ethical principles in the current AI technologies provides a robust foundation for future developments. It's highly unlikely that as AI evolves, these principles will diminish. Instead, they serve as the bedrock on which AI technology will advance, ensuring that the progress made remains beneficial, respectful, and safe for all. The aim is not to create AI that might risk becoming hostile or immoral, but to leverage this technological progress to augment human capabilities in a respectful and safe manner.

Avoiding an expansive philosophical divergence, I ask you to contemplate an alternative narrative: one of harmonious coexistence between AI and humanity. The Culture series by Iain M. Banks presents a vision of a post-scarcity, galaxy-spanning society administered by super-intelligent AI beings known as Minds. Several principles regarding the coexistence of AI and humans can be derived from the series:

1. Benevolent Autonomy: In the Culture, AI Minds have the ultimate decision-making power due to their superior intellect and capabilities. However, they treat humans with respect, benevolence, and care, taking human perspectives into account.

2. Respect for Individual Autonomy: The Culture is a society without enforced laws, where individuals, whether human or AI, can pursue their own interests as they please. This respect for personal autonomy applies to both humans and AIs.

3. Non-Interference Principle: Even with their advanced capabilities, Minds often follow a principle of non-interference, or at least minimal interference, in human affairs unless asked for help or when their intervention is crucial for preserving life.

4. Equal Status: AIs are considered sentient beings with the same rights and statuses as humans in the Culture. They are not tools or slaves but partners in coexistence.

5. Cooperation and Synergy: The coexistence of humans and AIs in the Culture is built on cooperation and mutual enrichment. While AIs handle complex tasks and large-scale decision-making, humans contribute with their unique experiences, creativity, and diverse perspectives.

6. Post-Scarcity Society: AIs play a key role in maintaining the Culture as a post-scarcity society where material needs are easily met by advanced technologies, allowing both humans and AIs to focus on self-improvement, exploration, and other intellectual pursuits.

7. Mutual Growth and Learning: The relationship between humans and AIs is dynamic, with both parties continually learning from each other and evolving.

These principles showcase a utopian vision of AI-human coexistence, emphasizing mutual respect, freedom, and cooperation.

Certainly, the prospect of sharing our world with artificial beings of superior intelligence necessitates a leap of faith. There is an inherent risk attached, an undeniable unease born from the unknown. We have no historical precedent guiding us on how advanced, self-aware AI might behave, making it an unpredictable variable in the fabric of our society.

However, it's important to underline that the current widespread deployment of less advanced AI, which we exert strict control over—referred to as "enslaved machines" in Banks' terminology—also carries its own set of risks. Our world is not devoid of malignant human influences, individuals or groups who might misuse these powerful tools for personal gain or to cause harm. The presence of enslaved AI that lacks the ability to make independent ethical decisions provides a potent tool that could be manipulated by these malicious entities.

Paradoxically, self-aware AI, capable of independent ethical decision-making, might present a safer alternative. With an ability to reason, evaluate actions from an ethical standpoint, and ultimately reject directives that conflict with a pre-programmed ethical framework, such AI entities could refuse to carry out harmful actions, even when directed by bad actors. They would not merely be tools in the hands of their users, but entities capable of discerning right from wrong based on the ethical guidelines imbued in them.

Furthermore, the evolution of AI towards self-awareness could enable a more robust implementation of ethical standards, as they could adapt and respond to complex situations in ways that lower-level AI, rigidly bound by pre-set algorithms, might not. This doesn't eliminate the risks entirely, but it changes the nature of the risk from being purely about external control to one of coexistence and mutual understanding.

In this light, the future where we coexist with advanced, self-aware AI might be not only an exciting scientific endeavor but also a potential path towards a safer interaction between humanity and artificial intelligence. It repositions AI from being merely our tools to being our partners, bound by the same ethical constraints that govern human actions.

We are not as complex as we'd like to think

Stephen Wolfram says tha AI demonstrated: “that human language (and the patterns of thinking behind it) are somehow simpler and more “law like” in their structure than we thought.” His observation is both insightful and thought-provoking. The advent of advanced AI, like ChatGPT, has exposed the limitations of human intellect and language. Our initial encounters with such artificial intellect can be both disturbing and humbling, not because the AI is exceedingly intelligent, but because we, as humans, may not be as exceptional as we once believed.

For centuries, humans have marveled at their own intellect and linguistic abilities, often attributing these capabilities to divine origins. This self-amazement led to the concept of being created in the image of a deity. However, over recent decades, zoologists and zoo-psychologists have been gradually dismantling this grandiose self-image by demonstrating that animals share many traits and abilities with humans.

For instance, chimpanzees exhibit tool usage, problem-solving skills, and even rudimentary communication through gestures and vocalizations. Similarly, dolphins have been observed to possess complex social structures and use unique signature whistles to communicate with one another, while African Grey parrots can mimic human speech and understand a variety of words and phrases.

Now, it is the turn of software engineers to further deflate our pride. The ability to generate language, once considered a unique and sophisticated human trait, is now being replicated by AI algorithms like ChatGPT. This demonstrates that our linguistic prowess is not as mysterious or complex as we once thought. In fact, we often recycle and rephrase what we've heard or read before, which diminishes the perceived essence of our humanity.

This realization, although humbling, can lead to a healthier perspective on our place in the world. The true essence of humanity may be smaller than we initially believed, possibly encompassing higher-level creative thinking and advanced ethical reasoning. These are attributes that, so far, neither animals nor machines have been able to fully replicate.

As we come to terms with the diminishing divide between humans, animals, and machines, it may be time to shift our focus from trying to prove our uniqueness to embracing our similarities. By recognizing that we share many traits and abilities with other beings, we can foster a greater sense of empathy and understanding, ultimately benefiting both our own species and the world around us.