Turnitin Is Selling us Snake Oil, or Why AI Detection Cannot Work

The notion of measuring "AI-generated text" as a fixed percentage of an academic submission is fundamentally flawed. This metric implies a homogeneous substance, akin to measuring the alcohol content in a beverage. However, my recent survey suggests that academic integrity associated with AI use is far from homogeneous. The survey asked educators to evaluate the ethical implications of using AI for twelve different tasks in writing an academic paper, ranging from researching to brainstorming to editing to actually writing full sections.

The findings revealed significant variance in responses. While many respondents were comfortable with AI aiding in brainstorming ideas, they expressed reservations or outright disapproval of AI writing entire paragraphs or papers. This disparity underscores a critical issue: there is no consensus in the academic profession on what constitutes acceptable AI assistance in learning. More strikingly, within each individual's responses, there was considerable variation in how different AI uses were assessed.

Consider the implications of a tool like Turnitin reporting "50% AI-generated" content. What does this figure actually represent? It lacks context about how the AI-generated content was incorporated. For instance, a paper could be largely original, with only minor edits made by AI at the end, potentially showing a high percentage of AI contribution. Conversely, a student might contribute minimally to an essentially AI-written paper, making slight modifications to reduce the AI-detected percentage. Both scenarios could yield vastly different percentages, yet the ethical implications are markedly divergent.

The pursuit of better detection technology misses the point. The issue is not with the detection capabilities but with the construct itself. The very idea of "AI-generated text" as a unified concept is problematic. Just as a depression inventory measures various symptoms that converge on the underlying construct of depression, our methods for evaluating AI in academic work must recognize the diverse and context-dependent nature of its use. The current approach, which treats all AI contributions as equivalent, is akin to judging a book's genre by counting its words. I which Turnitin and other commercial "AI Detectors" would show just a little more integrity and stop selling us the snake oil. They must know for sure that their claims are bogus, because AI-generated text is not a valid construct to be measured. 

Instead of focusing obsessively on detecting AI-generated content, we need to shift our perspective. We should expect and require students to use AI as part of their learning process. The challenge then becomes developing assignments that not only measure the content knowledge but also the meta-AI skills and competencies necessary to navigate and leverage these tools effectively. This approach acknowledges the complexity of AI's applications and ensures it is used responsibly, promoting a learning environment that respects both the potential and the limitations of artificial intelligence.

In Education, AI is an emergency

On one hand, AI presents an exhilarating leap forward, a kind of magic wand that promises to transform how we learn and teach. On the other hand,  this glam surface lies a grittier reality—one where the very essence of learning could be at risk.

In education, the core value lies in the process itself. The act of wrestling with ideas, constructing arguments, and stumbling over complex problems is where true learning happens. If a student turns to AI to write an essay, they might technically meet the assignment's requirements, but they've bypassed the intellectual struggle critical to internalizing knowledge. This worry has only deepened in the wake of the pandemic, which already strained educational norms and exposed glaring disparities. Introducing AI into this mix feels like throwing a wrench into an already delicate machine, risking the dilution of the educational experience and fostering a generation more adept at using tools than thinking independently.

Addressing this is no minor feat. It is not about rejecting AI's benefits outright, but rather about steering its use with a careful hand. Educators must become architects of a new curriculum that anticipates AI's influence and actively incorporates it in enriching ways. Perhaps this means designing projects where AI is expected to be used by the projects are still challenging and generate growth.

However, such a transformative approach to curriculum development is a colossal task, varied across academic disciplines and leveels of education. Educators need robust support systems, time to experiment and innovate, and backup from policies that understand and address these unique challenges. Governments and educational leaders must be partners in crafting policies that nurture educationally effectiveand responsible AI use.

As I reflect on this development, I am struck by the immensity of the challenge before us. It is not just about adapting to a new tool; it is about redefining the very foundations of how we teach and learn. It is about finding a way to harness the power of AI without sacrificing the soul of education. This is a journey that will require bold experimentation, deep collaboration, and a willingness to embrace the unknown. But it is a journey we must undertake, for the stakes are too high to ignore. The future of education hangs in the balance, and it is up to us to shape it with wisdom, courage, and a steadfast commitment to the human experience of learning.

Public Money, Private Glory?

At tech events, where AI CEOs bask in the adoration, there's a conspicuous absence in the narrative: the role of public funding and research. These sectors haven't just sprung up through the ingenuity and perseverance of a few brilliant minds; they're the culmination of substantial public investment. Yet, you'd be hard-pressed to hear a word of thanks to taxpayers or governments at these glittering presentations.

The problem with this omission is twofold. Firstly, it promotes a misleading story of technological development—one where breakthroughs seem to happen through sheer brilliance rather than collaborative, incremental progress supported by public funding. This narrative can skew public perception, suggesting that technological advancement might somehow spontaneously occur without structured support. It makes the process seem more magical than methodical, glossing over the reality that innovation is usually more marathon than sprint, and certainly not a solo race.

Secondly, this narrative concentrates excessive admiration—and thus influence—in the hands of tech leaders. Celebrated as visionary and almost superhuman, these individuals often come to wield significant power, not just over their companies but within society itself. Yet, while they may be exceptional in their fields, they frequently lack broad education in social sciences and humanities, or experience in broader human affairs, areas crucial for understanding the implications of the technologies they unleash. This can lead to decisions that prioritize innovation over social impact considerations or public welfare, a risky imbalance.

The superstar culture in technology isn't just an issue of misrepresentation. It has practical consequences, potentially leading policymakers and the public to undervalue the importance of ongoing governmental support for research. If tech advancements are viewed as products of individual genius rather than results of public investment and collaboration, governments and voters might feel justified in cutting funds to these areas, mistakenly believing the private sector will fill the gap. This could slow innovation and shift the global tech landscape, especially towards countries that maintain robust public funding for research.

Acknowledging the role of public funding in technology isn't about diminishing the achievements of tech leaders—it's about painting a more complete and accurate picture of innovation. This more nuanced understanding could foster better-informed decisions regarding funding, education, and policy, ensuring the ecosystem that nurtures new technologies remains dynamic and well-supported.

Ultimately, recognizing the collective contributions to technological advancements isn't just about giving credit where it’s due. It's about ensuring a balanced narrative that neither idolizes the individual innovator nor underestimates the foundational role of public investment. By correcting this imbalance, we can encourage a more sustainable, equitable approach to technology development—one that's grounded in reality and attentive to the broader implications of rapid technological change.

AI revolution minus massive unemployment

The conversation on AI often revolves around efficiency and cost reduction, typically translating into fewer jobs. However, a pivotal shift in perspective—from cutting workforce to enhancing and expanding workforce capabilities—can redefine the role of AI in the corporate world. This approach not only preserves jobs but also adds significant value to customer experiences and broadens the spectrum of services and products a company can offer. 

The traditional method of dealing with technological disruption—laying off workers and hiring new ones with the necessary skills—is not only a waste of human capital but also disregards the cultural knowledge embedded within an organization's existing workforce. Retraining keeps people within the organization, allowing them to shift roles while retaining and applying their invaluable understanding of the company's ethos and operations in new ways.

The first step in a proactive workforce transformation strategy is to map out the anticipated skills and roles that will be in demand. This is not just about foreseeing the obsolescence of certain skills but identifying emerging opportunities where AI can augment human capabilities. For instance, with the rise of AI-driven analytics, there is a growing need for professionals who can interpret and leverage these insights into strategic decisions, enhancing business intelligence far beyond current levels.

Once future needs are mapped, the next step is to develop a compelling incentive structure for retraining. Traditional models of employee development often rely on mandatory training sessions that might not align with personal or immediate business goals. Instead, companies should offer tailored learning pathways that align with career progression and personal growth, supported by incentives such as bonuses, career advancement opportunities, and recognition programs. This approach not only motivates employees to embrace retraining but also aligns their development with the strategic goals of the organization.

With AI's capacity to handle repetitive and mundane tasks, employees can redirect their efforts towards more complex, creative, and meaningful work. This shift enables businesses to expand their service offerings or enhance their product features, adding significant value to what customers receive. For example, financial advisors, freed from the tedium of data analysis by AI tools, can focus on crafting bespoke investment strategies that cater to the intricate preferences and needs of their clients. Similarly, customer service representatives can use insights generated by AI to provide personalized service experiences, thereby increasing customer satisfaction and loyalty.

AI not only optimizes existing processes but also opens new avenues for innovation. For instance, in the healthcare sector, AI can manage diagnostic data with high efficiency, which allows healthcare providers to extend their services into preventive health management and personalized medicine, areas that were previously limited by resource constraints. In the retail sector, AI-enhanced data analysis can lead to the creation of highly personalized shopping experiences, with recommendations and services tailored to the individual preferences of each customer, transforming standard shopping into curated personal shopping experiences.

For successful implementation, organizations must foster a culture that views AI as a tool for empowerment rather than a threat to employment. Leadership should communicate clearly about the ways AI will be used to enhance job roles and the benefits it will bring to both employees and the company. Regular feedback loops should be established to adjust training programs based on both employee input and evolving industry demands, ensuring that retraining remains relevant and aligned with market realities.

By focusing on retraining the workforce to harness AI effectively, businesses can transform potential disruptions into opportunities for growth and innovation. This approach not only preserves jobs but also enhances them, adding unprecedented value to the company and its customers, and paving the way for a future where human ingenuity and artificial intelligence work hand in hand to achieve more than was ever possible before.

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.