Why We Undervalue Ideas and Overvalue Writing

A student submits a paper that fails to impress stylistically yet approaches a worn topic from an angle no one has tried before. The grade lands at B minus, and the student learns to be less original next time. This pattern reveals a deep bias in higher education: ideas lose to writing every time.

This bias carries serious equity implications. Students from disadvantaged backgrounds, including first-generation college students, English language learners, and those from under-resourced schools, often arrive with rich intellectual perspectives but struggle with academic writing conventions. Their ideas - shaped by unique life experiences and cultural viewpoints - get buried under red ink marking grammatical errors and awkward transitions. We systematically undervalue their intellectual contributions simply because they do not arrive in standard academic packaging.

Polished academic prose renders judgments easy. Evaluators find comfort in assessing grammatical correctness, citation formats, and paragraph transitions. The quality of ideas brings discomfort - they defy easy measurement and often challenge established thinking. When ideas come wrapped in awkward prose, they face near-automatic devaluation.

AI writing tools expose this bias with new clarity. These tools excel at producing acceptable academic prose - the mechanical aspect we overvalue. Yet in generating truly original ideas, AI remains remarkably limited. AI can refine expression but cannot match the depth of human insight, creativity, and lived experience. This technological limitation actually highlights where human creativity becomes most valuable.

This bias shapes student behavior in troubling ways. Rather than exploring new intellectual territory, students learn to package conventional thoughts in pristine prose. The real work of scholarship - generating and testing ideas - takes second place to mastering academic style guides. We have created a system that rewards intellectual safety over creative risk, while systematically disadvantaging students whose mastery of academic conventions does not match their intellectual capacity.

Changing this pattern requires uncomfortable shifts in how we teach and evaluate. What if we graded papers first without looking at the writing quality? What if we asked students to submit rough drafts full of half-formed ideas before cleaning up their prose? What if we saw AI tools as writing assistants that free humans to focus on what they do best - generating original insights and making unexpected connections?

The rise of AI makes this shift urgent. When machines can generate polished prose on demand, continuing to favor writing craft over ideation becomes indefensible. We must learn to value and develop what remains uniquely human - the ability to think in truly original ways, to see patterns others miss, to imagine what has never existed. The future belongs not to the best writers but to the most creative thinkers, and our educational practices must evolve to reflect this reality while ensuring all students can fully contribute their intellectual gifts. 

Is the college essay dead?

The college essay, once a revered academic exercise, is now facing an existential crisis. It used to be a good tool—a structured way for students to demonstrate their understanding, showcase their critical thinking, and express ideas with clarity . The college essay was not merely about content; it was a skill-building process, teaching students to organize thoughts, develop arguments, and refine language. Yet today, AI  has made the traditional essay feel outdated, as it can generate polished, formulaic essays effortlessly. Policing AI use in these assignments is nearly impossible, and the conventional essay’s value is rapidly diminishing.

Not all essays are created equal, however, and the future of the college essay might depend on the type of skills we emphasize. The expository essay, designed to see if students understand material or can apply concepts, is on its last legs. When AI can churn out a satisfactory response in seconds, it is a clear sign that this form of assessment is no longer viable. The AI does not just pass these assignments; it excels at them, raising an uncomfortable question—if a machine can do it, why are we still teaching it? For these kinds of essays, the challenge is that they often assess recall rather than thinking. They were already on shaky ground; AI is just the final push. 

The essays that may survive, though, are those that demand novelty, creativity, and genuine problem-solving. AI may help in drafting, structuring, or even generating ideas, but it does not replace the kind of original thinking needed to solve real-world problems. It cannot fully simulate human intuition, lived experience, or deep critical evaluation. AI's writing is wooden, and often devoid of true beauty. Essays that require students to synthesize information in new ways, explore original ideas, exhibit artistic talent, or reflect deeply on personal experiences still have value. These essays are not about whether you know a theory; they are about what you can do with it. This is where the human element—the messy, unpredictable spark of creativity—remains irreplaceable. 

The deeper issue is not AI itself but the way we have been teaching and valuing writing. For decades, the emphasis has been on producing “correct” essays—structured, grammatically precise, and obedient to the format. We have been training students to write well enough to meet requirements, not to push the boundaries of their creativity. It is like teaching students to be proficient typists when what we really need are novelists or inventors. We have confused competency with originality, thinking that writing formulaic content is a necessary step before producing meaningful work. This is a misunderstanding of how creativity works; mastery does not come from repetition of the mundane but from risk-taking and exploration, even if that means stumbling along the way.

The real future of the essay should start with this recognition. Imagine if instead of book reports or basic expository pieces, students were challenged to write for real audiences—to draft scientific papers for journals, craft poems for literary contests, or propose solutions to pressing social issues. Sure, many students would not reach the publication stage, but the act of aiming higher would teach them infinitely more about the writing process, and more importantly, about thinking itself. This would not just be about mastering the mechanics of writing but developing a mindset of curiosity and originality. AI could still play a role in these processes, helping with the technicalities, leaving the student free to focus on developing and articulating novel ideas.   

The problem with the book report or the “explain Theory A” essay is not just that they are boring; it is that they are irrelevant. Nobody in the professional world is paid to summarize books or explain theories in isolation. These are stepping stones that lead nowhere. Excelling at pointless, terrible genre does not prepare to succeed ad an authentic genre. Instead of teaching students to write these antiquated forms, we should ask them to write pieces that demand something more—something they cannot copy-paste or generate easily with a prompt. Authentic, context-rich, and creative assignments are the ones that will endure. If there is no expectation of novelty or problem-solving, the essay format becomes an exercise in futility. 

AI’s rise does not have to spell the end of the essay. It might, in fact, be the nudge needed to reinvent it. We have the chance to move beyond teaching “correct” writing toward cultivating insightful, original work that challenges the boundaries of what students can do. AI’s presence forces us to ask hard questions about what we want students to learn. If writing is no longer about mechanics or regurgitating content but about generating ideas and engaging critically, then AI becomes a collaborator, not a competitor. It can help with the structure, but the essence—the thinking—must come from the student.

In the end, the college essay is not dead; it is just in need of reinvention. The conventional model of essays as rote demonstrations of knowledge is no longer viable. But the essay that challenges students to think, create, and solve problems—those essays will survive. They might even thrive, as the focus shifts from the mechanics of writing to the art of thinking. The key is to evolve our teaching methods and expectations, making room for a new kind of writing that leverages AI without losing the human touch. Raising expectations is the main strategy in dealing with AI in education. 

Authorship, Automation, and Answerability

In the ongoing debate about the ethical use of AI, two main concerns stand out—one superficial and one profound. The first concern, often highlighted, is about the authenticity of authorship, with fears that AI-generated content might mislead us about who the true author is. However, this worry is largely misguided. It stems from a historically limited, Western-centric notion of authorship that blurs the line between the origin of ideas and the craft of their representation.

Take the legacy of Steve Jobs. He wasn’t celebrated for personally assembling each iPhone, but for his vision and design that brought the device to life. In our industrial world, the act of making things is not inherently authorial—designing them is. Why should it be any different with text, code, or images? If I designed this text, and used advanced tools to produce it, why am I not still the author? The shock many feel towards AI’s ability to generate content is akin to the upheaval experienced by 19th-century bootmakers during the Industrial Revolution. Automation has simply extended its reach into the realms of writing, coding, and art. The craftsmanship is replaced by automation, but the core principle remains: take pride in the ideas, not in the mechanics of their production. There is no inherent authorship in the latter.

But here’s where Mikhail Bakhtin’s notion of answerability helps our understanding of the true ethical stakes. While responsibility is often about fulfilling obligations or being held accountable after the fact, answerability is about our ongoing, active engagement with the world and the people in it. It is not just about who gets credit for the content; it is about recognizing that every action, every word, and every piece of AI-generated content occurs within a web of relationships. We are answerable to others because our creations—whether authored by human hands or machine algorithms—affect them.

The real concern, then, lies in the issue of answerability. AI-generated content often appears polished, convincing, and ready for immediate consumption. This creates a dangerous temptation to release such content into the world without thorough scrutiny. Here is where the ethical stakes rise significantly. AI may produce work that looks and sounds credible, but this does not guarantee that it is unbiased, meaningful, or truthful. It maybe garbage polluting the infosphere at best, or an outward harmful fake at worst. The ease of content creation does not absolve us of the responsibility to ensure its quality and integrity, and more importantly, it doesn’t free us from the answerability we have to the world around us.

This is the message we need to instill in our students, professionals, and anyone working with AI: you are still accountable and answerable for what you produce, even if a machine does the heavy lifting. Releasing AI-generated content without critical evaluation is akin to conjuring a spell without understanding its consequences. Like a magician wielding powerful but unpredictable magic, or a novice driver behind the wheel of a truck instead of a bicycle, the stakes have been raised. The tools at our disposal are more potent than ever, and with that power comes a heightened level of answerability.

In essence, the ethical debate surrounding AI shuold not be about the authorship of the craft but shuold be about the integrity and impact of the output. The real challenge is ensuring that what we create with these advanced tools is not only innovative but also responsible and answerable. As we continue to integrate AI into more aspects of our lives, we must focus less on who—or what—authored the content and more on the ethical implications of releasing it into the world. This is where the true ethical discourse lies, and it is here that our attention should be firmly fixed.

What percentage of my text is AI-generated?

Go ahead, ask me the question. However, I would in turn ask you to specify which of the following kinds of assistance from AI you are interested in.  

1. Distilling information into summaries
2. Revamping and recasting content
3. Polishing grammar, spelling, and punctuation
4. Sparking ideas and crafting titles
5. Conjuring additional arguments or perspectives
6. Spotting potential counterarguments or objections
7. Constructing and organizing content
8. Juxtaposing points from multiple sources
9. Scrutinizing and refining existing content
10. Demystifying complex ideas or jargon
11. Architecting outlines and organizational structures
12. Fashioning examples or illustrations
13. Tailoring content for different audiences or formats
14. Forging hooks or attention-grabbing openings
15. Sculpting strong conclusions or call-to-actions
16. Unearthing relevant quotes or citations
17. Decoding concepts in simpler terms
18. Fleshing out brief points or ideas
19. Trimming verbose text
20. Honing clarity and coherence
21. Smoothing the flow between paragraphs or sections
22. Concocting metaphors or analogies
23. Verifying and authenticating information
24. Proposing synonyms or alternative phrasing
25. Pinpointing and eliminating redundancies
26. Diversifying sentence variety and structure
27. Maintaining consistency in tone and style
28. Aligning content with specific style guides
29. Devising keywords for SEO optimization
30. Assembling bullet points or numbered lists
31. Bridging sections with appropriate transitions
32. Flagging areas that need more elaboration
33. Accentuating key takeaways or main points
34. Formulating questions for further exploration
35. Contextualizing with background information
36. Envisioning visual elements or data representations
37. Detecting potential areas of bias or subjectivity
38. Inventing catchy titles or headlines
39. Streamlining the logical flow of arguments
40. Boosting text engagement and persuasiveness
41. Rooting out and rectifying logical fallacies
42. Imagining hypothetical scenarios or case studies
43. Illuminating alternative perspectives on a topic
44. Weaving in storytelling elements
45. Uncovering gaps in research or argumentation
46. Producing counterexamples or rebuttals
47. Bolstering weak arguments
48. Harmonizing tense and voice inconsistencies
49. Composing topic sentences for paragraphs
50. Integrating data or statistics effectively
51. Devising analogies to explain complex concepts
52. Injecting humor or wit
53. Eradicating passive voice usage
54. Compiling topic-specific vocabulary lists
55. Enhancing paragraph transitions
56. Untangling run-on sentences
57. Articulating thesis statements or main arguments
58. Infusing content with sensory details
59. Resolving dangling modifiers
60. Conceiving potential research questions
61. Incorporating rhetorical devices
62. Rectifying pronoun inconsistencies
63. Anticipating potential counterarguments
64. Embedding anecdotes effectively
65. Mending comma splices
66. Drafting potential interview questions
67. Sprinkling in cultural references
68. Correcting subject-verb agreement errors
69. Designing potential survey questions
70. Adorning text with figurative language
71. Repositioning misplaced modifiers
72. Brainstorming potential titles for sections or chapters
73. Integrating expert opinions
74. Paring down wordiness
75. Exploring potential subtopics
76. Weaving in statistical data
77. Eliminating tautologies
78. Coining potential taglines or slogans
79. Embedding historical context
80. Untangling mixed metaphors
81. Developing potential FAQs and answers
82. Incorporating scientific terminology
83. Fixing split infinitives
84. Generating potential discussion points
85. Blending in technical jargon
86. Expunging clichés
87. Crafting potential calls-to-action
88. Inserting industry-specific terms
89. Replacing euphemisms
90. Extracting potential pullout quotes
91. Interweaving mathematical concepts
92. Eliminating redundant phrasing
93. Compiling potential glossary terms and definitions
94. Introducing philosophical concepts
95. Standardizing formatting
96. Curating potential appendix content
97. Incorporating legal terminology
98. Clarifying ambiguous pronouns
99. Cataloging potential index terms
100. Synthesizing interdisciplinary perspectives
101. Writing long list of AI uses for content generation

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.

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. 

My Use of AI Is None of Your Business

Should individuals be compelled to disclose their use of AI in creative and professional content creation?  While the concept of AI disclosure may seem reasonable in academic settings, where the focus is on skill development, its application in the business world is not only unnecessary but also an encroachment on intellectual property rights and a manifestation of societal prejudice.

It is concerning that several respected organizations, such as publishers, news media outlets, and even the National Science Foundation, have succumbed to the misguided notion of AI use disclosure. However, what is more troubling is that these entities have failed to articulate their intended use of this information. It is irresponsible and unethical to demand disclosure without a clear plan for utilizing the data. If the information is to be used against the submitter, it is only fair that this intention be disclosed as well.

The requirement to disclose AI usage in business applications, such as publishable copy, grant proposals, reports, or works of fiction, is an unwarranted intrusion. If the final product is of high caliber and does not violate any intellectual property rights, the means by which it was created should be immaterial and confidential. Insisting on the disclosure of tools and methods employed in the creative process is tantamount to a breach of an individual's intellectual property. Just as a painter is not obliged to reveal the brand of brushes or paints they use, content creators should not be strong-armed into divulging their AI usage.

Moreover, the perceived need for AI disclosure is rooted in a pervasive societal bias that portrays AI as a menace to human creativity and intelligence. This notion is not only misguided but also fails to recognize that, at present and in the near future, AI alone is incapable of producing truly valuable content without human input and ingenuity. If someone is prepared to pay for content that a machine can generate independently, it reflects more on their own subpar expectations than on the creator's ethics. 

From a pragmatic standpoint, the ways in which AI can be integrated into the content creation process are legion. Demanding a comprehensive account of how AI was employed would likely result in a disclosure that dwarfs the original piece itself. Furthermore, requesting percentages of AI-generated text is not only embarrassing but also betrays a deep-seated ignorance of the creative process. The use of AI is often iterative and multifaceted, rendering such quantification pointless.

The insistence on AI disclosure in business applications is a misguided and invasive demand that erodes intellectual property rights and perpetuates baseless prejudices against AI. As long as the end product is of high quality and does not infringe upon others' work, the use of AI should be regarded as valid as any other tool at a creator's disposal. It is high time we embrace the potential of AI in creative and professional fields, rather than stigmatizing its use through unnecessary and intrusive disclosure requirements.

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.

Writing instructors, why are you surprised by AI?

Why do you look surprised?  Since the 1970s, there has been a shift in your field. This change was not about refining the minutiae of grammar or punctuation. Rather, it was a movement toward valuing the creative process in writing. Think of pioneers like Donald Graves, Lucy Calkins, and Peter Elbow. They were not merely toying with new ideas; they were fundamentally altering how writing is taught, influencing college-level instruction as well.

The advent of AI technology has accelerated a shift that was already underway. Historically, while there was vocal support for creative and critical thinking, the reality often leaned towards assessing grammar and spelling. It was simpler to grade based on these concrete elements. Judging originality and creativity posed greater challenges, especially when justifying grades during student appeals.

However, it is becoming clear that the reliance on traditional assessment is no longer sustainable. It is time to genuinely embrace what has been acknowledged for decades. The focus should shift more towards teaching originality, creativity, authenticity, discernment, and critical thinking. Ideas should be valued over mechanical accuracy.

A crucial aspect of this evolution is teaching students to write with AI assistance. This approach does not diminish writing standards. Instead, it raises the bar for the final product. Students should learn to use AI as a tool to enhance their writing, not as a substitute for critical thinking or creativity.

Dear writing instructors, the time has come to adapt. And you know how to do it better than anyone else. The gradual shift many of you have been working on, is now upon us. This is a moment for re-evaluating, rethinking, and embracing a new phase in education where AI complements and enhances the teaching of writing. The future is here, and it aligns with the trajectory you have been following.

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. 

Originality over convention

Writing has long been a tightrope walk between adherence to convention and the pursuit of originality. Historically, deviating from established norms could brand you as uneducated, while a lack of originality risked the label of being clichéd. This delicate balance has been fundamentally disrupted by the advent of AI in writing, or "wraiting" as I like to call it.

In the pre-AI era, convention held significant value. It was a measure of education and intelligence, a yardstick to judge the clarity and correctness of one's thoughts. However, AI's ability to effortlessly follow these conventions has suddenly diminished their value. Originality has emerged as the sole contender in the arena of writing excellence. 

This seismic shift has understandably ruffled feathers. Many derive a sense of pride and authority from mastering and teaching these conventions. Yet, they now find themselves in a world where these skills are increasingly automated. This change isn't subject to debate or democratic process - it's an unstoppable wave reshaping the landscape.

Ironically, while AI excels in adhering to conventions, it's not inherently original. It can replicate, recombine, and reformat existing ideas, but the spark of true originality still lies uniquely within the human mind. This realization should be a beacon for writers in the AI era. The challenge is no longer about mastering the rules of writing but about pushing the boundaries of creativity and originality.

The implications for education are profound. Traditionally, a significant portion of writing education focused on teaching the rules – grammar, structure, formats. Now, these aspects can be delegated to AI tools. This frees educators to focus more on cultivating creativity, critical thinking, and originality. It's a shift from teaching the mechanics of writing to exploring the depths of imagination and expression.

For those resistant to this change, the path ahead may seem daunting. It involves unlearning the supremacy of convention and embracing a world where originality reigns supreme. However, this change is not a loss but an evolution. It's an opportunity to rediscover the essence of writing as an art form, where the value lies not in the adherence to rules but in the ability to transcend them.

In conclusion, the advent of AI in writing presents an opportunity for a paradigm shift. It's a call to writers and educators alike to redefine what constitutes good writing. As we navigate this new landscape, our focus should shift from convention to creativity, from format to imagination, ensuring that the heart of writing remains a distinctly human endeavor.

Assessing writing with AI

Writing with AI is a complex skill that overlaps with traditional manual writing, but it is not the same. Many instructors struggle to grasp this new skill because it is unfamiliar to them. Teaching something you haven't mastered is challenging, leading to noticeable unease at all educational levels. Even those eager to incorporate AI in teaching, often open to new innovations, face this difficulty. The issue essentially lies in redefining the objectives of writing instruction. If the belief is that students should ultimately write independently, then traditional practice is paramount, leaving no role for AI tools. However, the more challenging conceptual shift is recognizing the need to teach students how to write with AI. This is like the transition from penmanship to typing. We lose something in this shift: the beauty, the discipline, and the rigorous exercises of handwriting. I recall diligently practicing letter formations in my first-grade penmanship class. Although I was never adept at it and gladly transitioned to typewriters when they became accessible, I understand the pain of losing the esteemed art of writing, cherished for centuries. This pain, particularly acute for those who have spent decades mastering and teaching writing, must be acknowledged. Yet, this shift seems inevitable. We are dealing with a technology that is being adopted faster than any in history, and it is not a passing fad. The benefits are too clear. We face a stark paradox: educators use AI to create lesson plans and assessment rubrics, yet often bar their students from using the same technology. This is unsustainable and awkward. 

As a profession, we are only taking the first steps in integrating AI into writing instruction. Here's another baby step: I revised Sacramento State University's Undergraduate Writing Portfolio Assessment criteria, considering the new skill of "wrating." 

Writing Placement for Juniors Portfolio (WPJ)

5 - Exceptional Wraiter: Demonstrates mastery in "wraiting," producing AI-assisted compositions at a publishable level in their respective discipline. Showcases exceptional skill in generating rich, engaging prompts and collaboratively refining AI outputs. Exhibits a deep understanding of AI's strengths and limitations, skillfully navigating these in producing original, high-quality work.

4 - Strong Wraiter: Effectively employs AI tools in "wraiting," producing texts of high quality that reflect a sophisticated understanding of AI's capabilities. Demonstrates the ability to create rich prompts and engage in the iterative process of refining AI-generated content. Shows a clear grasp of AI's strengths and limitations, using them to enhance original thinking and critical evaluation.

3 - Competent Wraiter: Demonstrates a solid understanding of "wraiting," using AI tools to assist in writing tasks. Capable of creating effective prompts and engaging in the process of refining AI outputs. Shows awareness of the strengths and limitations of AI in writing, but may require further guidance to fully exploit these in creating high-quality texts.

2 - Developing Wraiter: Beginning to understand the role of AI in "wraiting." Can generate basic AI-assisted texts but requires further instruction in creating effective prompts and refining outputs. Shows potential in understanding AI's strengths and limitations, but needs more practice to integrate these effectively in writing tasks.

1 - Emerging Wraiter: Early stages of grasping "wraiting." Struggles with effectively using AI tools, often producing clichéd, uninspired texts that lack human input and originality. Needs substantial guidance in understanding AI's capabilities, constructing prompts, and refining AI-generated content.

0 - Incomplete Portfolio: Portfolio does not demonstrate the basic competencies in "wraiting" or effective use of AI in writing tasks. Requires additional work to understand and skillfully employ AI tools in the writing process. What do you think?

If a robot can beat your writing assignment, it is time to rethink it

The rise of machines, with AI bots like OpenAI's ChatGPT replicating human-like text generation, compels us to question our education system's foundations. Is reflective or expository writing a valid assessment when AI can mimic average student work? The answer isn't straightforward. However, this crisis provides an opportunity to redefine our understanding of writing and its relation to thought.

The advent of AI challenges us to de-emphasize the mechanics of writing, such as grammar, style, and syntax, that a machine can master. Instead, we should focus on more complex aspects of writing that are inherently human: original thought, artful language, and narratives that resonate with the human experience. This shift, although jarring, is necessary. It signals not just a technological revolution, but a cultural, intellectual, and educational upheaval.

The AI revolution illuminates a harsh truth: traditional education and assessment methods are no longer sufficient. The challenge is to teach students not just to write well, but to think originally, create artfully, and understand deeply. In the face of the machine, we must thrive in domains that are distinctly human.

In this technological metamorphosis, we're compelled to redefine our work and our value. Perhaps we are not merely creators, analysts, or workers. Perhaps we are artists, thinkers, dreamers. And perhaps, in this transition, we will find our survival and our redemption.

We must revisit traditional teaching methodologies, challenging the efficacy of our current writing assignments. As educators, a simple test can provide clarity: input your exact writing assignment into ChatGPT. If the AI produces a B-grade paper, it's time to rethink.

One option is to replace the writing assignment with an alternative assessment, such as oral exams, multiple choice, or short answer tests. Another option is to transform the assignment to require students to engage with AI, like ChatGPT. This approach would involve designing assignments that test not just writing ability, but also the capacity to evaluate AI outputs critically, discern the nuances differentiating human intellect from AI, and incorporate these insights creatively. In response to ChatGPT, dramatically raise your expectations of student writing, for now they have a lot of help.

AI's advent should be viewed not as a threat, but as an opportunity to explore new pedagogical frontiers. Our learning community is leading this exploration into AI-integrated education, and we invite all to join this journey.

In essence, AI's rise can help us rethink education. We must focus not just on subject mastery, but also on developing original thinking, creativity, and nuanced intelligence. In the face of the machine, we can still shine in our distinctly human domains, thriving in this technological metamorphosis. The future of education, interwoven with AI, might look different, but it promises a realm of possibilities, allowing us to redefine our unique human value.

Wraiting vs. writing

Wraiting is the new writing, only it has AI in it. I bet that a few years down the road, we will all be doing more wraiting than old-fashioned writing. And some of us will be better at it than others because doing it well requires considerable skill. Don’t complain then that I did not warn you, and you fell behind.

Just to give a glimpse of the new world, consider these wraiting tips. It is the tip of the iceberg, for there are a lot more nuances to it than I know about, and even more that I do not. Here are four key roles that AI can play in wraiting:

Brainstorming: One of the main roles that AI can play in wraiting is in the brainstorming stage of the writing process. It can help writers generate new ideas, provide suggestions for topics to explore, and even conduct initial literature reviews (only for well-explored topics). These tools can also be used to create outlines and plan the structure of a piece of writing, making it easier to organize ideas and stay on track.

Critiquing your ideas: Another important role that AI can play in wraiting is as a critical partner to chat about ideas with. With chatbot-style interactions, the AI can engage in a conversation about the writer's ideas, ask questions, provide feedback, and offer suggestions. This can help writers refine their ideas, explore new directions, and gain valuable insights into their writing.

Turning dense chunks of ideas into full paragraphs and segments: By using natural language processing algorithms, wraiting tools can analyze the structure and meaning of sentences and suggest improvements that can help writers better articulate their ideas. This can be especially useful for writers who struggle with writer's block or who find it challenging to organize their thoughts into cohesive paragraphs. AI can look for additional arguments, examples, metaphors, and references to support or challenge your claims.

Editing: Finally, AI can play a key role in the editing process of wraiting. From grammar and spelling to structure, flow, style, genre, and audience analysis, wraiting tools can help writers identify areas for improvement and provide suggestions for making changes. AI-powered editing tools can also help writers save time and effort by automatically correcting common errors and suggesting alternative phrasing.

Wraiting is not easy. AI-powered chatbot has several limitations, some of which are very serious, while others are simply annoying. Learning them will save you from disappointment and frustration; it is a part of the skill. But that would be a topic for another blog. In the meanwhile, build your wraiting skills by trying. It is investment in your future. For educators, there is additional significance. We should start teaching students how to wrait soon.