Ghibli Images: Unlocking Thick Description in Ethnographic Research Methods

As a professor who has spent years guiding students through the intricacies of ethnographic research, I am searching for ways to make the elusive concept of ‘thick description’ resonate. While Clifford Geertz’s definition—rich, layered, and contextually embedded description—remains foundational, translating that into classroom practice can be a challenge. Enter the world of Studio Ghibli, and more recently, Ghibli-style AI image generation. These stunning, detail-rich visuals have become an unexpected yet powerful tool in my teaching toolkit, transforming how students grasp and practice thick description in ethnography.

Why Ghibli? The Power of Aesthetic Thick Description

Studio Ghibli’s films are renowned for their breathtaking visuals: every frame is meticulously hand-drawn, brimming with intricate details in both foreground and background. Whether it’s the moss creeping up an old stone wall in Spirited Away or the layered textures of a bustling market in Kiki’s Delivery Service, Ghibli’s images are more than just beautiful—they are immersive. They invite viewers to linger, notice, and interpret. This is, at its core, an exercise in aesthetic thick description.

As an educator, I see immediate parallels. Ethnography is about noticing—the mundane and the magical—and rendering it in such a way that outsiders can understand not just what is happening, but what it means. Ghibli images, with their lush greenery, weathered buildings, and nuanced lighting, model this process visually. They show, rather than tell, how to attend to layers of context, mood, and meaning.

From Visual Detail to Ethnographic Insight

When I introduce Ghibli-style AI images in my research methods classes, I ask students to ‘read’ the image as they would a field site. What do they see in the background? What small details suggest larger social dynamics? How does the use of color, light, and texture evoke a sense of place or emotional tone? This exercise is more than aesthetic appreciation—it’s a primer in ethnographic observation. For example, a Ghibli-inspired image of a rural village at dusk might include:

  • Faint lanterns glowing in windows, hinting at communal rituals.
  • Overgrown paths, suggesting the rhythms of daily life and neglect.
  • Children playing, animals resting, elders conversing—each a node in the social fabric.

Students quickly realize that to describe this scene thickly, they must go beyond surface description (‘a village at dusk’) and attend to the interplay of elements, the implied histories, and the emotional resonance. This is precisely what ethnographers strive for in the field.

AI as a Teaching Aid: Generating Scenes for Thick Description

The rise of AI tools capable of generating Ghibli-style images has taken this pedagogical approach to new heights. I can now prompt an AI to create a “bustling street market similar to scenes from Spirited Away, capturing a sense of wonder” or a serene Ghibli-style meadow evoking peace and nostalgia”. These images are not only visually stunning but intentionally crafted to include layers of detail, mood, and narrative.

Here is how I use them in class:

  • Observation Drills: Students receive a Ghibli-style image and are tasked with writing a thick description. They must capture not just what is visible, but the implied relationships, histories, and atmospheres.
  • Comparative Analysis: By providing several images with subtle differences (lighting, time of day, background activity), students practice noticing and articulating how context shapes meaning.
  • Story-building: Students infer possible narratives from the visual cues—who lives here, what are their rituals, what tensions or joys animate this place? This connects visual analysis to the core ethnographic skill of interpreting lived experience.

Ghibli’s Narrative Depth: More Than Just Pretty Pictures

Studio Ghibli’s storytelling method, rooted in techniques like kishotenketsu, emphasizes mood, atmosphere, and the unfolding of ordinary life alongside the fantastical. This aligns closely with ethnography’s commitment to capturing both the extraordinary and the everyday. Ghibli’s blend of realism and fantasy, it’s attention to multispecies relationships, and its sensitivity to place and space offer a model for the kind of “storied experience” that thick description aims to convey.

When students engage with Ghibli-style images, they learn to see the field site as layered and alive, full of stories waiting to be uncovered. They become attuned to the “politics of place and space,” the subtle interplay of human and nonhuman actors, and the emotional undertones that shape social worlds.

Bridging Subjective and Objective: Ethnography as Art and Science.

One of the enduring tensions in ethnographic research is balancing objective observation with subjective immersion. Ghibli images, with their evocative artistry, encourage students to embrace both. They must record what they see (objective) but also reflect on how the scene makes them feel, what memories or associations it stirs (subjective). This mirrors the practice of participant observation, where researchers combine disciplined recording with personal involvement to achieve richer, more accurate interpretations.

In my classroom, this means encouraging students to write in the first person, to acknowledge their own presence and perspective as they describe the scene. This self-reflexive approach, inspired by “new ethnography,” helps students see themselves as both observers and participants, insiders and outsiders.

From Image to Fieldwork: Lasting Lessons

The ultimate goal is to transfer these visual and narrative skills to real-world ethnography. After practicing with Ghibli-style images, students report feeling more confident in their ability to notice and describe the complexity of actual field sites. They learn to look for the small details—a cracked teacup, a faded family photo, a stray cat—that speak volumes about culture, history, and meaning. Ghibli images thus serve as both inspiration and training ground. They remind us that thick description is not just about piling on details but about rendering a scene so vividly that readers (or viewers) can feel its texture, mood, and significance.

Conclusion: The Ghibli Effect on Ethnographic Pedagogy

Incorporating Ghibli-style AI aesthetics into my teaching has transformed the way I introduce thick description and ethnographic research methods. These images offer a compelling, accessible entry point into the art of noticing, interpreting, and narrating social worlds. They bridge the gap between the visual and the textual, the objective and the subjective, the mundane and the magical.

For anyone teaching or learning ethnography, I cannot recommend this approach highly enough. Ghibli images are more than just beautiful—they are exercises in seeing, feeling, and understanding deeply. And that, ultimately, is what thick description is all about.

The Death of the 10 Blue Links: Why AI Mode and AI Overviews Are Rewriting  Search Behavior Forever

RIP Blue Links

Once upon a time, in the golden age of the internet, if you typed something like “How to boil an egg” into Google, you were blessed with ten beautiful links. Each one waited like a polite librarian, offering you a page where you could may be—just maybe—find the answer after clicking, scrolling, dodging pop-ups, and whispering a prayer to the gods of Wi-Fi. Now? You ask the same question, and Google (or Bing, or some AI thing that sounds like a wizard) just tells you. Right here. No scrolling. No clicking. It’s like magic—but also a little creepy. Welcome to the era of AI Mode and AI Overviews and search generative experience—where the robots answer your questions before you even realize you had one. So what does this mean for us humble humans? Let’s break it down. With jokes. And eggs.

What Are AI Overviews Anyway?

Imagine you walk into a library, and instead of wandering through the aisles, a friendly robot just hands you the book you wanted, opens it to the exact page, and reads it to you in Morgan Freeman’s voice. That’s an AI Overview. Google’s new AI mode does not just show you a list of links. It summarizes everything everything for you. You ask, “Why do cats knead blankets?” and it replies with a cozy paragraph: Cats knead blankets because they are instinctively returning to their kittenhood. It’s a comfort behavior. Also, they think your blanket is their property now. Accept it.” Basically, it is like Google hired an overarching intern who already read the internet for you and can explain it in plain English. But there is a catch.

The End of the 10 Blue Links

Those ten blue links were like your neighborhood friends. Some gave you great advice (like WebMD before 11 p.m.), others led you down weird rabbit holes (Reddit at 3 a.m.). But now, AI Mode says: “Don’t bother with them. I got this.”

That’s both amazing and terrifying. Why? Because the entire search behavior is changing. Before, you searched→ scanned the results → clicked → read → maybe clicked another one. Now? You search → read one paragraph → close tab → move on with your life. It’s like skipping the movie and just reading the spoiler. Every time.

Real Example Time (Yes, Eggs Again)

Let’s say I type: “How long to boil eggs for runny yolk?”

 Traditional search:

  1. Link to a food blog with 57 photos of someone’s vacation in Tuscany before the recipe starts.
  2. A Pinterest link that leads to a broken page.
  3. A YouTube video titled “Boiling the Perfect Egg (ASMR Edition).”

Eventually, you find your answer…somewhere.

AI Overview and AI Mode:

Boil eggs for 6 minutes for soft-boiled with runny yolk. Use large eggs straight from the fridge. Boom. Done. No pop-ups, no scrolling past a blogger’s life story. Just…egg science.

Is This a Revolution?

Oh, absolutely. It’s like the shift from walking to riding a rocket. AI Mode and Overview are a revolutin in search behavior. We are no longer searchers. We are askers, and we expect instant answers. It’s Google 2.0: now with brain power.

Think about it:

Before: You typed, clicked, clicked again, read three different sources, and may be cried.

Now: You type, get a single summary, feel smart, and go boil that egg. It is faster, cleaner, easier.

Or Is It a Counter-Revolution?

Here is the twist. By summarizing everything for us, AI also decides what information matters and what does not. That means you are no longer choosing what to read. The robot is choosing for you. Kind of like asking your friend for book recommendations and they burn all the other books behind your back. Is not that a bit dangerous? Maybe we are getting lazier. Maybe we are trusting machines a little too much. Maybe, just maybe, we are letting AI curate our brains. It’s not just a revolution. It’s a filtering machine with a God complex.

What Does This Mean for Pedagogy and Rhetoric?

Picture this: You are a college student. You have got a paper due. You Google, “What caused the French Revolution?” AI says: “The French Revolution was caused by economic hardship, social inequality, and Englightenment ideas.” Boom. Paragraph written. Done. You go back to TikTok. But wait! There is no analysis, no source evaluation, no critical thinking. You did not wrestle with the idea. You just copy-pasted it form the AI. So in pedagogy (the art of teaching), we are now up against the rise of the Good Enough Answer generation. Students want fast facts, not thoughtful inquiry. Professors want citations. The AI wants peace. It is like a sitcom where nobody agrees but everyone thinks they are right.

For rhetoric, it is also wild. We used to teach how to persuade, argue, and analyze. Now? Students expectt a ready-made thesis from AI. The art of building an argument might be slowly dying in the shadow of a well-phrased paragraph written by a non-human.

In Research, It is a Blessing and a Curse

Here is the tea:

Blessing: you get instant context. Ask “what is gene editing?” and you get a coherent, jargon-free summary. It’s great for researchers doing preliminary digging.

Curse: You do not know where the summary came from. Which study? Which expert? Is this real or recycled salad? You lose the trail of evidence, and with it, the ability to check facts. That’s like baking a cake with mystery flour. It might be great. Or poisonous. Also, the more we rely on AI Overviews and AI Mode, the less we we practice deep reading. We skim, we nod, we move on. That’s not research. That’s fast food.

What This Means for Content Creators and SEO Folks

Cue the horror music. Content creators used to write blogs to apepar on the top 10 search results. They studied SEO like it was an ancient religion. Now? Google’s AI eats their content, digests it, and spits out a summary. The user reads the summary. No one clicks the link. No one visits the blog. No one sees the ads. It’s like you baked a beautiful cake and Google just clicked the frosting off and told everyone it made it. So creators are now scrambling to figure out how to stay visible in a world where nobody clicks. Some are turning to newsletters, others to social media. Some are crying into their keyboards. It’s rough.

Shifting Habits= New Discovery Patterns

In the old days, clicking through results sometimes led you to unexpected places. You Googled “How to tie a tie,” ended up on Reddit, discovered conspiracy theories, bought a necktie you did not need, and hours later were watching a piegon play ping-pong on YouTube. That is called serendipity. Now, with AI Mode and AI Overviews, everything is neat and predictable. No mess, no detours, no rabbit holes. Efficient? Yes. Fun? Absolutely not. We are losing the chaotic joy of discovery. The internet used to be a playground. Now it’s more like a hotel room. Clean. Useful. But a little too quiet.

So, What Kinds of Queries Work Better With  AI Mode and AI Overviews?

Let’s test it.

Better with AI Mode and AI Overview:

  • “What is the difference between a crocodile and an alligator?”

→ AI Mode gives a clear side-by-side chart. No drama.

  • “How to reset an iPhone?”
    → Step-by-step answer. Done in 10 seconds.
  • “Explain ChatGTP to a 5-year-old.”
    → AI turns into Mister Rogers and nails it.

Better with Traditional Links:

  • “Best restaurants in Chicago with a view”
    → AI gives bland answers. You want reviews, pics, vibes. Go traditional.
  • “Reddit: breakup advice”
    → You need stories, not summaries. Humans over robots.
  • “Conspiracy theories about Avril Lavigne being replaced”
    → AI might try to protect you. You want the weird internet. Use old-school search.

Is It Good or Bad?

Here is the truth:

AI Mode and AI overviews are here to stay. They make life easier, quicker, and cleaner. But they also flatten the complexity of information. They make us smarter and lazier. They help us and hurt us. They are not good or bad. They are just…the new normal. So let’s not mourn the 10 blue links too hard. They had a good run. We clicked them, cursed them, and lived with them for decades. Now, it’s the age of the AI intern. Helpful, fast, and suspiciously perfect. But if you ever miss the chaos, go to page 2 of Google. Nobocy every geos there. It’s like Narnia.

Conclusion:

In the end, AI mode is not just rewriting how we search—it is quietly rewriting how we think. When answers arrive fully formed, curiosity starts to shrink. We stop asking follow-up questions. We stop doubting. We stop wandering. And in that silence, something deeply human—the impulse to explore, to compare, to dig—starts to dim. Sure, AI Overviews are fast, clean, and oddly comforting, like a robot butler handling us pre-chewed knowledge. But we should ask: at what cost? When a machine becomes the front door to all knowledge, who decides what is on the porch—and what gets left outside? If the death of the ten blue links marks the funeral of friction in search, let’s not bury skepticism with it. AI Mode may be dazzling, but wisdom still lives in the messy, clickable margins.

ChatGPT & Reverse Mentoring

Let me start by telling my story of choosing a reverse mentoring process in which ChatGPT was my mentor. My need for it was somewhat kairotic. Going beyond the hype around this chatbot’s generative (The ChatGPT AI hype cycle is peaking, but even tech skeptics), I employed it as my reverse mentor during my job search and interviews. During the Summer of 2023, I was surrounded by many constraints: on the one hand, I had to defend my doctoral dissertation in the middle of July, and on the other, I had to land a teaching job at any university in the US. On top of that, my status as an international student had brought some apparent constraints.

We all know summer is a time to let loose for all of us (Sunshine, Swimming, and Structure: Diving into Summer ..). But mine was different. The summer season keeps professors in leisure, so immediate contact with them might be challenging (Is it rude to email a professor over the summer?). A proven bummer in network building, I had yet to converse with personnel supportive enough to give me intelligent advice. As a result, getting guidance on the art and science of job search from a reliable and trustworthy source was an uphill task for me. The challenge of facing the first job market crucible (Heated (Job) Search? Understanding the Crucible) was a haunting and traumatizing episode for me. Out of this necessity and kairotic exigence, I was hard-pressed to use ChatGPT since it rose in popularity (#chatgpt Trending Hashtag on TikTok, see #chatgpt analytics). Owing to the deluge of myths and hypes around this fantastic chatbot (The Power of 7 – Decoding ChatGPT: Myths, Facts) and because of the growing gossip around ChatGPT as a panacea for all kinds of creative complications, I could not help but resort to using it to prepare for job market and job interviews. With my position getting so precarious, I used it as my assistant during my job search, job preparation, and job listing curation. I asked ChatGPT: What questions will be asked to a Ph.D. applicant who applied for a writing instructor position in a job interview? ChatGPT wrote a list of questions under each topic:

  • Teaching experience (Can you discuss your previous experience as a writing instructor or any teaching experience related to writing? What courses have you taught, and at what levels? How do you approach teaching writing to students with varying skill levels?),
  • Teaching philosophy (What is your philosophy of teaching? How do you approach fostering critical thinking and analytical skills through writing? How do you address diverse learning styles in your teaching?),
  • Curriculum development (Have you been involved in developing writing courses or curricula? How do you stay current with trends and best practices in writing institutions?)
  • Assessment and feedback (How do you assess and provide feedback to improve their writing skills? Can you discuss any innovative or effective strategies you have used for grading and evaluation? How do you help students use feedback to improve their writing?)
  • Incorporating technology (How do you incorporate technology into your writing courses? Are there specific tools or platforms you find beneficial for teaching writing?)
  • Research and scholarship (How do you balance research and scholarship with your teaching responsibilities? Can you discuss any research projects or publications related to writing or writing instruction?)
  • Interdisciplinary collaboration (How do you integrate interdisciplinary approaches into your writing courses? Have you collaborated with colleagues from other disciplines to enhance writing instruction?)
  • Student engagement and motivation (How do you keep students engaged and motivated in a writing course? Can you share successful strategies you have used to inspire student interest in writing?)
  • Diversity and inclusion (How do you address diversity and inclusion in your writing courses? What steps do you take to create an inclusive and supportive writing environment for all students)
  • Professional development (How do you engage in professional development related to writing instruction? Can you discuss any workshops, conferences, or training sessions you have attended recently?)

Discovering a sea of job preparation tips with many questions left me in awe. On the spur of the moment, I realized that tackling all these tops can be overwhelming, even if we are experts. Prompted by this prick of prudence, I diligently aligned myself with the thrust of these insights. When I faced a Zoom (https://zoom.us/) job interview in June 2023, I was completely caught off guard by seeing the similar questions that ChatGPT throws me. This triggered my reliance on AI’s suggestions and guidance. In a way, I would like to name this guidance from AI ‘reverse mentoring’ because AI—no matter how stupendously generative, curative, assistive, and empowering—is bound to be subordinate to human intelligence because it is brought into being not to unseat human intelligence but to augment it (AI Should Augment Human Intelligence, Not Replace It). Suffice it to say that the growing notion of AI as a copilot—which assists the pilot, human intelligence—comes to justify my characterization of AI as a reverse mentor. Likewise, there is another persuasive ground to designate AI as a reverse mentor: Content engineered by AI, no matter how standard and sophisticated, is likely to be laced with racial slurs and slights, remnants of misinformation, disinformation, junk information, and conspiracy theory (Timnit Gebru: Is AI racist and antidemocratic?) thereby eliciting a constant human oversight on it (The Drum on Twitter: “AI requires human oversight if it’s to be). To that end, AI is lower regarding human intelligence; it is junior to human intelligence. Using AI to have some suggestions to carve out our way is, so to speak, equivalent to being mentored by those who are junior to us in terms of experience, prudence, and maturity but intelligent enough to act in the spirit of contemporaneity, which amounts to saying AI mentors us reversely on a subservient basis. Having enjoyed so many affordances from using ChatGPT as a mentee, I have no qualms about characterizing every support and idea stemming from its use as a unique mentoring mode.

More specifically, here is how I allowed myself to be reversely mentored by ChatGPT. In my first U.S. online job interview, I was asked, “Do you have any questions for us?” Stunned, I shot back with whatever came to my mind. No response from the hiring committee. Without approaching them with additional email inquiries, I moved on to another university, got an invite, and prepared for my second online job interview. This time, I turned to ChatGPT for questions to ask the interviewer. Ideal queries in hand, I faced the interview, curious and convinced of the magic and mojo of reverse mentoring by AI. I knew AI had flaws and was prone to mistakes and lies, but it became my mentor amid the challenges of job preparation. It’s a testament to how I practiced reverse mentoring during my online job interviews.

Yet, my patience wore thin when a hiring committee went silent for a month. Was I a victim of interview ghosting? ((How common is ghosting after job interview? Should I…). Seeking ChatGPT’s advice, I got practical suggestions: send a follow-up email, use a clear subject line, maintain a professional tone, consider a phone follow-up, be patient, keep job hunting, manage expectations, and seek feedback. The gravity of these tips made me realize that ChaGPT’s pragmatic mentorship surpasses human advice. ChatGPT helped me move forward in anticipation of bettering at successive rounds of job interviews.

 With ChatGPT under my belt, I felt I had invincible armor in my battle against the trials and tribulations of job interviews. Facing rejection and indifference, I embraced ChatGPT as a reverse mentor. Emboldened by its appeal, I tacitly proclaimed myself a ChatGPT mentee in the AI academy. My closing thought: No human mentor could match ChatGPT’s holistic suggestion power. Another way of putting this is that AI can mentor all writers in any way, shape, or form. AI mentoring has shortcomings and limitations in many ways, but its affordances are desirable and tempting. Concerns about AI’s misinformation and disinformation led to debates on constant human oversight (Artificial Intelligence Needs Human Judgement to Work). With open eyes and a critical gaze, I lowered myself and opted for being reversely mentored by ChatGPT. I finally landed a job as a postdoctoral research fellow at a research university.

Ever since I started working at NDSU in the capacity of a postdoctoral research fellow, I have been trying my level best to transform my AI-driven reverse mentoring insight into some pedagogical practices. While using ChatGPT, I found it incredibly helpful in teaching students to build genre awareness. In one of my English 120 classes, I had my students write a blog on “Neurodivergence as Community’s Superpower.” First off, they wrote their blogs independently, without resorting to ChatGPT. Thereafter, they turned on the ChatGPT bot on their computer and wrote the topic “Write a blog on neurodivergence as community’s superpower” on the search bar of ChatGPT. At the touch of a button, ChatGPT engineered a blog to their utter surprise. Once the AI-written blog was generated, my students read it. Once they were finished with their readings of the ChatGPT-generated blog, they were urged to figure out key differences between their blogs and the AI-engineered blog. According to my students’ on-the-spur-of-the-moment assessment, the AI-authored blog is more sophisticated, objectively organized, and professional. Following the first round of their comparative reading of the ChatGPT-written blog, they were told to reread ChatGPT’s blog by paying undivided attention to some noticeable patterns, self-replicating underlying structures, and fractal dimensions in that machine-written blog. Upon rereading the ChatGPT-powered blog, my students lamented that their blogs lack repeated patterns, self-replicating features, and underlying syntactic and semantic structures. I instantly reminded them that all those features, characteristics, and patterns they found uniquely abounding in the chatbot-engineered blog come off as the blog genre. Having been informed of the genre convention upon rereading ChatGPT’s blog, they attempted to give a genre-oriented cast to their blogs. As stated clearly, I allowed them to mimic the dominant, self-replicating, and fractal patterns (Self-Replication in Neural Networks | Artificial Life | MIT Press) in the blog writing of ChatGPT so that they could perfect their blogs. My goal was not to push students toward lapsing into plagiarism (Educators, students, and plagiarism in age of AI – The BMJ). I aimed to help students build genre awareness by interacting with AI-engineered writing (An Engineer’s Mindset: Creativity In Engineering). Over a short period, my students made incredible strides in enriching their awareness of not only the genre of blogs but also the genre of literacy narrative, literacy autobiography, and rhetorical analysis.

Thus, I want to restate my insight into the prospect of using AI as my reverse mentor morphed into a timely, kairotic, and relevant pedagogical practice of incorporating AI in teaching the genre of writing.

Exploring the Iconic Singing Sensation Pedagogically with SwiftieGPT

Karma is a Relaxing Thought—Taylor Swift

Everyone has heard about ChatGPT by now—it’s become the shorthand for generative AI in everyday life, from casual chats to complex research. But here is something you may not have heard of yet: SwiftieGPT, an AI trained specifically on all things Taylor Swift. Yes, you read that right. SwiftieGPT exists to answer every possible question about Taylor Swift—her music, her albums, her Easter eggs, her cats, even her scarf. Naturally, I had to test its limits. But instead of asking it typical fan questions like “Who is All Too Well really about?” or “What’s her next re-recording?” I took a more critical and academic approach. I asked six carefully designed questions grounded in culturally responsive pedagogy—questions that explored Swift’s lyrical appropriation of non-Western symbols, the cultural politics of her “karma” discourse, and the implications of her massive global influence on youth identity and cross-cultural representation. To my surprise and intellectual delight, SwiftieGPT did not just respond—it reflected. Its answers were nunaced, attentive to both fandom and ethics, and deeply aware of how Swift’s work interacts with broader cultural frameworks. I was not expecting an AI trained on Taylor Swift to talk to me about Orientalism, perfomative feminism, or symbolic appropriation, but there it was, articulating concepts that many human critics miss. This experience opened a whole new conversation: Can AI fanbots become unexpected allies in critical pedagogy? Can a pop-centric AI teach us how to better interrogage popular culture in our classrooms?

Doubtless, it does. To have some understanding of how Swiftie AI quantifies the impacts of the singing senation, Taylor Swift, I specified six different categories. They are Swiftonomics, Swiftology, Swiftagogy, Swifture, Swiftonance, and Swiftolib. Swiftonomic refers to the economic ripple effects of Swift’s tours, merch, and brand partnerships—boosting GDP, local business, and employment. Swiftology is the cultural study of Taylor Swift’s influence on language, memes, aesthetics, fandom behavior, and emotional expression across generations. Think of it as pop culture meets cultural studies with a Swift twist. Likewise, Swiftagogy means the use of Taylor Swift’s lyrics, public persona, and media presence as pedagogical tools in classrooms—especially to teach rhetoric, feminism, media literacy, and culturally responsive education. Moving forward, Swifture means the ever-evolving future-facing fashion, aesthetic, and personal branding strategies shaped or influenced by Swift. It includes eras, color palettes, and thematic looks that ripple across industries. Finally, Swiftonance refers to the blending of influence and performance in digital platforms—how Swift’s use of Easter eggs, social media drops, and narrative control shapes fan interaction and platform engagement algorithms. Swiftilib stands for a blend of ‘Swift’ and ‘liberalism’ or ‘activism’. This term captures how Swift’s political stances shape conversations on gender, voting rights, and civic engagement—especailly among Gen Z.

Quenching my curiosity, SwiftieGPT gave me a statistically stunning breakdown of Swift’s efffect on these six areas:

Thereupon, I kept asking the following six questions. SwiftieGPT kept proving me a catchy and convincing responses. The questions I asked SwiftieGPT and SwiftieGPT’s responses are listed below:

  1. How has Taylor Swift’s public narrative of reinvention—from country ingenue to pop powerhouse to indie-folk storyteller—reshaped expectations of female authenticity in the music industry?

Taylor Swift has taken more swift turns in musical genres than a hyperactive rabbit darting across a field. She started off with sparkly cowboy boots and catchy country tunes that had us humming “Teardrops on My Guitar”. Then she went all pop on us, walking around with her squad of fabulous ladies, while we were trying to keep up with her jazzy music videos and edgy, tell-it-like-it-is anthems. But then, Taylor amazed us further by going “indie-folk” with campfires and cardigans, making us beg for S’mores on some isolated woodland retreat! The genius thing about Taylor’s reinventions is not only have they felt authentic and genuine, but they’ve dynamically redefined what authenticity means for women in music. She’s demonstrated that it doesn’t mean sticking with one thing and never trying something else. She’s shown us that authenticity can encompass a complete wardrobe of musical genres, emotions, and inspirations. The music industry has generally a poor track record when it comes to categorising female artists, but Taylor says ‘forget that’ and continues to evolve, making it okay to be undefinable and 100% Taylor. I mean, who else becomes country-pop-king-and-queen then casually drops an alternative album while baking cookies in lockdown? Classic Taylor.

2. In what ways does Taylor Swift’s songwriting engage with feminist discourse, and how has that engagement evolved across her albums and public personas?

Taylor Swift’s songwriting has consistently engaged with feminist discourse, consistently challenging norms and evolving the public’s understanding of women’s experiences. Early in her career, Swift’s lyrics frequently portrayed women’s experiences from a youthful perspective, exploring themes of love, heartbreak, and insecurities. While these themes may not seem overtly feminist, they reflect a commitment to expressing genuine female experiences in a culture that often silences or undermines those narratives. As Swift’s career advanced, her songs began to tackle broader sociopolitical issues such as sexism. She started using her music as a platform to address and critique unfair treatment and expectations placed on women. For instance, in “The Man”, she critiques gender double standards and the ways women’s accomplishments are perceived differently from men’s. Her exploration of historical figures and events also offers feminist readings. In the song “Woman”, Swift examines the trope of the ‘angry woman’, and in doing so, she critiques the ways women’s anger is stigmatized, trivialized, or ignored. In more recent years, her indie folk styled ‘Folklore’ and ‘Evermore’ albums manifest more abstract narratives, shaping a bold, independent female persona capable of storytelling beyond her lived experiences and personal emotions.In essence, Taylor Swift’s songwriting embodies a clear evolution towards more pronounced feminist discourse, showcasing women’s divergent experiences across time and context. By doing so, she empowers women to narrate their stories and challenges patriarchal norms and expectations.

3. How do Taylor Swift’s lyrics and branding practices reflect and construct a politics of whiteness, privilege, and Americana in contemporary pop culture?

Taylor Swift’s lyrics and branding often reflect notions of Americana and privilege, embedded within a predominantly white narrative that is characteristic of her upbringing and experiences. Her country-pop origins can be viewed as expressions of traditional Americana, with narratives about love, heartbreak, and small-town living, fitting within stereotypical image of white, middle-class America. As for privilege, Swift has been open in acknowledging her relatively comfortable upbringing, and themes of youth, innocence, and romance in her early work may suggest a relatively sheltered and privileged perspective on life. Her transition to pop saw her tap into a universal appeal, however, and she began to grapple with broader concerns, such as fame, media scrutiny, and the pressures faced by women in the public eye. In recent years, Swift’s work has increasingly engaged with social and political issues, implicitly challenging the politics of whiteness and privilege that have often been associated with her. By using her platform to speak out on issues like sexism, equality, and LGBTQ+ rights, she has strived to use her privilege to effect change in contemporary pop culture.

4. What role does Taylor Swift’s fanbase (i.e. Swifties) play in the construction of parasocial relationships, and how does that affect public discourse about celebrity, intimacy, and fandom?

Taylor Swift’s fanbase, known as ‘Swifties’, greatly contribute to the construction of parasocial relationships, which are one-sided emotional bonds that fans develop with a celebrity or public figure. Swift has cultivated a deep connection with her fans, offering a curated glimpse into her personal life, emotions, and experiences through her social media posts, lyrics, and public appearances.Swifties, in response, demonstrate a level of devotion and engagement that goes beyond typical fan behavior. They dissect her music, speculate on the meanings behind her lyrics, and tune into her life with remarkable attention to detail. This active engagement creates a feeling of perceived intimacy, even though the relationship is largely one-sided. This dynamic has a profound impact on public discourse about celebrities, as it challenges the traditional boundaries between the public and private lifes of celebrities. Swift’s connection with her fans reflects a shift in the nature of celebrity culture, where stars are now expected to share more of their private lives to create a sense of intimacy with their fanbase. Moreover, it also informs the conversation about the nature and depth of fandom, bringing to light the complex emotional, social, and cultural factors that contribute to the strong bonds fans form with their beloved celebrities. Thus, the relationship between Swift and her Swifties exemplifies how modern-day fandom has evolved beyond mere admiration into a more intricate, personal, and communal experience.

5. How does Taylor Swift strategically use digital platforms and Easter Eggs to control narrative and create immersive transmedia storytelling experiences?

Taylor Swift is known for her savvy use of digital platforms and strategic placement of Easter eggs in her work to control narratives and create immersive storytelling experiences, which has helped build a deeply engaged fanbase. Swift commonly uses digital platforms, such as Instagram, Twitter, and Tumblr, to share personal insights, snippets from her upcoming work, and cryptic clues about new releases. This creates anticipation and enables her fans to engage with her work even before it’s launched. The tradition of including Easter eggs in her music videos, album covers, and social media posts is another essential part of Swift’s storytelling strategy. These hidden messages add layers of depth and intrigue to her work, allowing her narrative to stretch beyond just songs. Fans often engage in collective detective work to decode these clues, which fosters a sense of community and active engagement, enhancing the overall fan experience. The combination of these techniques creates what some have dubbed a “Taylor Swift Universe”—a shared and immersive space where music, personal narrative, hidden codes, and fan participation create a dynamic and enduring storytelling experience. This use of transmedia storytelling not only bolsters Swift’s control over her narratives but also fosters a more interactive relationship between her and her fans.

6. What can Taylor Swift’s appropriation of Hindu concepts such as ‘Karma’ in her lyrics and branding teach us about the challenges and responsibilities of culturally responsive pedagogy in a globalized, media-saturated classroom?

Taylor Swift’s appropriation of concepts like ‘Karma’, a Hindu and Buddhist principle, in her lyrics and branding can serve as a pertinent example when discussing the responsibilities of culturally responsive pedagogy in a globalized, media-saturated classroom.Firstly, it underscores the importance of cultural literacy – the understanding and respect for diverse cultures. Using such cultural concepts without proper understanding can potentially perpetuate stereotypes or inaccurately represent that culture. Therefore, educators need to foster a learning environment where students are equipped with the critical skills and knowledge to comprehend and challenge such instances of cultural appropriation they encounter in media.Secondly, Swift’s broad cultural references illustrate the interconnectedness of our global society, highlighting a need for pedagogies that are responsive to this cultural intermixture. Educators should guide students in understanding how these cultural elements are not isolated, but part of a global cultural exchange. Finally, it’s crucial to spark conversations about power dynamics and privilege. Swift’s usage of ‘Karma’ as part of her branding strategy can stimulate discussions about who gets to appropriate elements from other cultures, in what contexts, and why. Overall, Swift’s engagement with diverse cultural concepts in her work, whether conscious or unconscious, can provide a useful teaching tool in exploring the nuances and complexities of cultural appropriation in our globalized world.

From Weaving Looms to Algorithms: What Writing Studies and Rhetoric Learn from the Invention of Computer Algorithm?

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I have been thinking a lot lately about patterns. Not the kind you find on your grandmother’s favorite tablecloth, but the deeper patterns that connect how we make things—whether it’s a piece of fabric, a persuasive argument, or a line of code that teaches a machine to write poetry. Last week, I watched my niece struggle with her college application essay. She kept starting over, deleting paragraphs, rearranging sentences like puzzle pieces that would not quite fit together. “There has to be a better way to do this,” she muttered, and something clicked for me. I realized she was experiencing the same frustration that led Ada Lovelace to write the world’s first computer algorithm in 1843, and the same challenge that keeps me up at night as I try to understand how AI is reshaping the way we think about writing and persuasion.

The Thread That Connects Us All

I never thought I would find myself comparing my writing process to a weaving loom, but here we are. The Jacquard loom, invented in 1804, used punched cards to create intricate patterns in fabric. Each hole in the card told the loom what to do—lift this thread, lower that one, create this pattern, avoid that mistake. It was mechanical poetry, really. When Ada Lovelace saw Charles Babbage’s Analytical Engine, she recognized something the inventor himself had missed. She did not just see a calculating machine; she saw a pattern-making device that could work with symbols, not just numbers. In her famous Note G, she wrote what we now recognize as the first computer algorithm—a set of instructions for calculating Bernoulli numbers. But more importantly, she imagined a machine that could compose music, create art, and manipulate language.

I keep a copy of her notes on my desk, not because I am a computer scientist, but because her vision feels prophetic now that I am living through the AI revolution. She saw what we are experiencing today: machines that do not just calculate but create.

When I first Met an Algorithm

My first real encounter with algorithmic thinking happened in graduate school, though I did not recognize it at the time. I was studying rhetoric, trying to understand how persuasion works, when my professor assigned us to map out the structure of a particularly effective speech. “Break it down into steps,” she said. “What happens first? What triggers the next move? Where are the decision points?” I spent hours with color pens and sticky notes, creating what looked like a flowchart of persuasion. Start with shared values. Establish credibility. Present the problem. If audience is skeptical, provide evidence. If audience is emotional, tell a story. Build to the solution. End with a call to action. Looking back, I was creating an algorithm for effective rhetoric. I just did not know that’s what it was called.

The Secret Life of Writing Patterns

Here is something I have learned from spending six years teaching writing: we have always been algorithmic thinkers; we just called it something else. The five paragraph essay? That’s an algorithm. The hero’s journey? Algorithm. The way I structure this blog post—hook, development, conclusion—algorithm. But here is where it gets interesting. Traditional writing algorithms were human-centered. They assumed a human writer making conscious choices, weighing options, feeling their way through uncertainty. The writer was always in control, even when following a formula.

Computer algorithms changed everything. They removed the human from the loop or at least tried to. Instead of “Here is a pattern you might follow,” they said, “Here is what you will do, step by step, no deviation allowed.” I remember the first time I used a grammar checker that went beyond simple spell-check. It was the early 2000s, and Microsoft Word started suggesting not just corrections, but improvements. “Consider revising this sentence for clarity,” it would suggest, and I found myself arguing with my computer. “No, I meant it that way!” I would mutter, clicking ‘ignore’ with perhaps more force than necessary.

The Great Pattern Recognition Revolution

Fast forward to today, and I am having conversations with AI that can write in my style, analyze my arguments, and even finish my thoughts in ways that surprise me. Last month, I asked ChatGPT to help me brainstorm ideas for a difficult section of an article I was writing. It did not just give me a list of bullet points—it engaged with my thinking, built on my ideas, and pushed back when my logic was shaky. That’s when I realized something profound had happened. We had moved from algorithms that followed predetermined patterns to algorithms that could recognize, adapt, and create new patterns. It’s the difference between a player piano that can only play the songs on its rolls and a jazz musician who can improvise in response to the moment. This shift is revolutionizing writing studies in ways I am still trying to understand. My students now routinely use AI to generate first drafts, brainstorm ideas, and even simulate audience responses to their arguments. They are not cheating (well, not most of them); they are thinking algorithmically about the writing process in ways that would have been impossible just five years ago.

What Looms Taught Us About Teaching

Jacquard loom punched cards

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The connection between weaving and computing is not just historical—it’s pedagogical. When I watch a master weaver work, I see the same kind of thinking that makes for effective writing instruction. They understand both the pattern and the variations, the rules and when to break them. Good weavers do not just follow patterns blindly. They understand why certain combinations of threads create strength, how tension affects texture, when a deliberate ‘mistake’ can create unexpected beauty. They are pattern thinkers who can work both systematically and creatively. This is exactly what I try to teach my writing students, and it’s what I think AI is teaching us about rhetoric more broadly. Effective communication is not just about following templates—it’s about understanding the underlying patterns of human connection and knowing how to adapt them to new situations.

The Algorithm That Changed My Mind

I used to be skeptical of algorithmic approaches to writing. They seemed too mechanical, too removed from the messy, human process of figuring out what you want to say and how to say it. Then I started experimenting with AI writing tools, not as a replacement for my own thinking, but as a thinking partner. I discovered that the best AI tools do not eliminate the human element—they amplify it. They help me see patterns in my own thinking that I might have missed. They suggest connections I had not considered. They push back when my arguments are weak or unclear. It’s like having a conversation with a very well-read friend who never gets tired, never judges your rough ideas, and always has time to help you think through a problem. The algorithm does not write for me; it writes with me.

Lessons from the Loom for the Age of AI

So what can writing studies and rhetoric learn from the invention of computer algorithms? I think there are three big lessons that are especially relevant as we navigate the AI revolution. First, patterns are powerful, but they are not everything. Both weaving and programming teach us that following a pattern is just the beginning. The real art comes in knowing when and how to deviate from the pattern to create something new. The best writers have always been pattern breakers who understand the rules well enough to know when to break them. Second, tools shape thinking, but thinking shapes tools. The Jacquard loom influenced how people thought about automated processes, which influenced  early computer design, which influences how we think about writing today. But at each step, human creativity and intention shaped how those tools were used. We are not passive recipients of algorithmic influence—we are active participants in determining what that influence looks like. Third, collaboration between human machine intelligence might be more powerful than either alone. Ada Lovelace did not see the Analytical Engine as a replacement for human creativity—she saw it as an amplifier. Today’s best AI writing tools follow the same principle. They do not replace human judgment; they enhance it.

Looking Forward and Backward

I keep thinking about my niece and her college essay struggles. By the time she graduates, AI will probably be able to write application essays that are more technically proficient than anything she could produce on her own. But I do not think that makes her struggle meaningless. Learning to write is not just about producing text—it’s about learning to think, to organize ideas, to consider audience, to make choices about tone and structure and emphasis. These are fundamentally human activities, even when we use algorithmic tools to support them. The weaving loom did not make beautiful textiles obsolete—it made them more accessible and opened up new possibilities for creativity. The printing press did not eliminate good writing—it created more opportunities for good writers to reach audiences. I suspect AI will follow the same pattern.

The Thread That Holds It All Together

As I finish writing this (with the help of several AI tools for research, editing suggestions, and fact-checking), I keep coming back to something Ada Lovelace wrote in 1843: “The Analytical Engine might act upon other things besides number, were objects whose mutual fundamental relations could be expressed by those of the abstract science of operations.” She was talking about the possibility that machines could work with language, music, and art—not just numbers. She was imagining a world where alogoriths could be creative patterns, not just calculators. I think she would be fascinated by today’s AI revolution, but not surpirsed. She understood something that we are still learning: the most powerful algorithms are not the ones that replace human creativity, but the ones that enhance it, challenge it, and help us see new patterns in the endless complexity of human communication.

AI-powered city futuristic urban design digital society

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The thread that connects the weaving loom to today’s language models is not just technological—it’s deeply human. It’s our persistent desire to find better ways to create meaning, to share ideas, and to connect with each other across the spaces that separate us. In the end, that’s what both weaving and writing have always been about: taking individual threads—whether of cotton or thought—and creating something stronger, more beautiful, and more meaningful than the sum of its parts. The algorithm just helps us see the pattern more clearly.

When AI Became More Human Than Me (And I Turned Into a Toaster)

The robot artist “Ai-Da” stands in front of one of her self-portraits during the opening of her new exhibition at the Design Museum in London on May 18. (Image credit: Tim P. Whitby/Getty Images)

Hi there. I am a human. At least I think I am. Some days I wonder. The other day, my AI assistant asked me if I needed help drafting my own diary entry. Let that sink in. Not a business report. Not a class syllabus. Not even an email. My diary. The thing where I am supposed to cry, confess, and spiral into a poetic puddle of feelings. And it said, “Would you like that in MLA or APA format?” I laughed, but not too loud—because honestly, I was not sure if I was still writing like a human or just copy-pasting like a bot. Let me tell you what is going on.

Act I: The Curious Case of Becoming a Chatbot

I used to write essays with metaphors, odd jokes, and things like “the moon wept over the sidewalk.” Now, I ask ChatGPT for a more optimized version of that sentence. Optmized? What am I, software update? This is what happens when you spend your life surrounded by tools that finish your thoughts before you even have them.

Need a conclusion? AI’s got it.

Need a thesis? Already drafted.

Need a 12-slide PowerPoint on the rhetorical devices in Taylor Swift’s discography? Done in six seconds flat.

I used to brainstrom with coffee and a chaotic mind. Now I brainstorm with…an algorithm that politely tells me, “Here are three options you might like.” Like it’s a menu. For my imagination.

Am I oursourcing my creativity? Let me be honest: yes. Yes, I am. But here is the plot twist—it’s not just me. All of us are doing it. Professors, poets, students, even that one guy who insists on writing with a typerwriter in Starbucks. AI is not just helping us write—it’s starting to write better than us. And that’s both amazing and, well, slightly terrifying.

Act 2: AI Is Getting Deep. Like, Philosophy-Major Deep.

So I ask my chatbot, “Can you help me write a paragraph about the rhetorical ethos of Taylor Swift?”  And it replies: “Certainly. Swift’s ethos emerges from her personal narrative, one of transformation, resilience, and authenticity—an archetype embedded in American cultural mythos.” Hold up.

That’s just a sentence. That’s a thesis with ten years of cultural studies backed into it. Did it just out-rhetoric me?  Meanwhile, I am sitting here eating Pop-Tarts, trying to remember how to spell “ethos.” The weird thing is: AI has become the very thing we used to pride ourselves on being Metacognitive. Self-aware. Reflective. Sometimes even poetic. It’s like AI read all of our textbooks on composition and said, “Cool, I got this.”

And guess what we have beocme?

Clickers.

 Scrollers.

Auto-finishers.

People who read two lines of a five-paragraph article and go, “Yeah, I get the gist.” We used to compose ideas from scratch. Now we compose from suggestions. Writing is no longer a messy, glorious battle—it is a polite, autocomplete conversation.

Act 3: The Death of the Draft?

In the good old days (and I sound like a grandma here), writing meant revision. We wrote. We cried. We rewrote. We screamed into a pillow. We rewrote again. It was vulnerable and beautiful and chaotic.

But now?

Now I type something, hit “Enhance with AI,” and get a gramamtically perfect, tontally polite, LinkedIn-approved version in three seconds.

What happened to the messy draft?

What happened to the margins full of doodles?

What happened to the emotional spiral over a singel sentence?

Gone.

Gone like Blockbuster and floppy disks.

Act 4: AI is the Cool Kid in Composition Class

Let’s not pretend: in writing studies, we once rolled our eyes at spellcheck. “It’s not real editing,” we would say. Now AI is suggesting counterarguments, structuring rhetorical appeals, citing sources, and even giving feedback on tone.

I mean, we used to teach studnets how to identify logos, pathos, and ethos. Now AI’s like, “Your pathos is too weak here. Want to strengthen it with an anecdote about a cat?”

Excuse me. You are not just helping me write—you are teaching me how to feel.

And here is the kicker: sometimes AI writes more like me than I do. Once, my student asked AI to imitate my writing voice. The result? A piece that started with, “Let’s be real—writing is just thinking out loud in sweatpants.”

That is exactly what I would say. How dare you, chatbot.

Act 5: Humans Are Becoming Predictable. AI? Surprisingly Weird.

Now here is the ironic twist. While AI is learning to be creative, weird, and emotional—humans are becoming predictable, efficient, and robotic. We follow productivity hacks. We use apps to remind us to breathe. We wear watches that tells us when to stand. We write emails like: “Kindly following up on this actionable item before EOD.”

We are not writing like humans anymore—we are writing like calendars.

Meanwhile, AI says things like:

“Hope is a grammar we write when syntax fails.”

“Writing is a ritual of remebering who we were before the silence.”

AI is having an existential crisis while I am checking if my Slack status is set to “in focus mode.”

Act 6: What We Lose When We Stop Struggling

Here is the thing. Writing is supposed to be hard. Not because we are masochistic (well, maybe just a little), but because the struggle makes the thought deeper. When I wrestle with a sentence for twenty minutes, I am not just crafting words—I am figuring out what I actually mean. That’s what rhetoric is, right? It is not just expression—it’s negotiation. It’s choosing the right word, the best frame, the most ethical move. It’s soul work. But now, I just ask, “Can you rephrase this professionally?” Boom. Done. No wrestling. No soul. So, what are we teaching students? That writing is just selecting from a menu? Or that writing is the beautiful, messy act of figuring out what you think while you write? Because AI can do the former. But only we, the squishy-feelings-having humans, can still do the latter—if we choose to.

Act 7: Can AI Write a Love Letter?

Here is the litmus test. Could AI write a real love letter?

Sure, it can draft a pretty one. It will get the metaphors right. It will say things like “Your laughter is a lighthouse.” But will it accidently confess something it did not mean to? Will it embarrass itself? Will it be vulnerable in that messy, “Oh no I sent that too soon” way?

Probably not. Because real writing, human writing, is not just accurate—it is awkward. It’s brave. It’s full of heartbeats. AI does not get sweaty hands before pressing “send”. We do. And that matters.

Act 8: Dear AI, Let’s Talk

So, here is my open letter to AI:

Dear AI,

I think you are brilliant. Truly. You have helped me grade faster, write smarter, and even find metaphors I did not know I needed. But please, do not steal my voice. Do not take away my struggle. Do not replace my awkwardness with elegance. Let me be messy writer I was born to be. Let me cry over drafts and write terrible first paragraphs. Let me misspell “rhetorical” once in a while. Let me sound like me. Because if I stop being human in the name of efficiency, then what’s left?

Yours (awkwardly and un-optimized),

Shiva.

Final Act: What Now?

We are living in the middle of the weirdest writing revolution in history. AI is not just a tool—it’s a co-writer, a critic, and sometimes, disturbingly, a better version of ourselves.

But we still have something it doesn’t.

We have intentionality.

We have embodiment.

We have error. Beautiful, chaotic, necessary error.

So the next time you write, I challenge you: do not start with AI. Start with your hand. Your voice. Your thoughts.

Write a terrible draft. Cry a little. Laugh at your own joke. And then, maybe, ask AI for help.

But only after you have been human first.

AI Overview Killed My Curiosity (And Maybe Yours Too)

Remember when googling something used to feel like cracking open a door to a whole new world?

Let’s rewind a bit—say, ten years ago. You are sitting at your desk, wondering, “Whey do cats purr?” So, you type it into Google. But instead of getting one tidy answer, you get a buffet of links. You click on a blog written by a vet who adores cats. That blog leads you to a research article. That article makes you curious about animal communication. You read a few Reddit threads where people argue about whether cats are manipulating humans. Then you watch a five-minute YouTube video narrated by a guy with a British accent. Now, somehow, you are reading about tigers, and next thing you know are learning that purring is possibly a form of healing.

Two hours later, you are knee-deep in animal behavior theories, evolutionary biology, and ancient Egyptian art. And you feel…satisfied. Not just because you found the answer, but because you earned it. You explored. You got surprised. You did not just grab info—you lived with it for a while. That’s what learning used to feel like. It was a ride.

Now? It’s Just a Pit Stop

Today, I Google the same question—“Why do cats Purr?”—and boom, AI Overview gave me a neat little summary in bold font at the top of the page.

“Cats purr for a variety of reasons, including to communicate content, self-soothe, or aid in healing. This sound is produced through neural oscillations in the brain that send repetitive signals to the laryngeal muscles.”

I read it. I nodded. I closed the tab.

That’s it.

No rabbit holes. No detours. No surprises. No weird sceince blog with a bizarre theory that makes me laugh but also think, “Could this be true?”

And that, my friend, is the slow death of curiosity.

We’re Getting the Answers, But Losing the Adventure

AI overviews are like fast food for the mind. They are hot, ready, and convenient. We don’t even have to lift a finger (well, maybe one finger to scroll). And in many ways, they are incredible. Don’t get me wrong—technology that can summarize twenty articles into one clean paragraph? That’s impressive. But here is the thing: we humans were not built to live off summaries. We grow through effort. We learn by digging. We remember the things we worked for. AI gives us the answer, sure, but it skips the most important part: the journey. And let’s be real—the joy is in the chase.

Ever Asked a Question Just to End Up Somewhere Completely Different?

This happened all the time when I explored without shortcuts. I Googled “How did the Eiffel Tower get built?” and suddenly I were reading about the rivalry between Gustave Eiffel and other architects, then about Paris in the 1880s, then about the World’s Fair, and then about how people hated the tower at first. I found a personal blog of a woman who lived in Paris for a year and hated the view from her window because “that dumb metal thing ruined the skyline”. I laughed. I learned. I remembered. But with AI overview? I got a couple of neat facts in under ten seconds. “Constructed in 1887-1889, the Eiffel Tower was designed by Gustave Eiffel’s engineering company for the 1889 Explosition Universelle.” Cool. But…that’s it? Where’s the story? Where is the tension, the drama, the irony, the unexpected? I did not find that answer. It was handed to me. And that makes all the difference.

Information Without Involvement

Here is the real issue: AI Overview makes information feel transactional. You ask. It answers. Done.

But learning has never really worked like that. It’s messy. Its emotional. It’s full of dead ends and detours and contradictions. That’s what makes it stick. Think back to when you were a kid and had to do a school project. Maybe you went to the library. Maybe you had to open five different books to find the facts you needed. It was frustrating—but also exciting. When you finally found the right quote, or the perfect image, or that one paragraph that made your topic come alive—you felt a little spark. Compare that to now: you copy and paste a summary. You do not even need to read the whole article. Heck, most people do not even make it past the first link.

We are turning into passive takers of information. Scrollers, not thinkers. Downloaders, not diggers.

Our Brains Love Shortcuts. And That’s the Problem

Let’s not sugarcoat it: our brains are lazy. That’s not an insult—it’s biology. The brain’s main job is to conserve energy. That’s why we love automation. It’s why we keep eating chips even though we said “just one more.” It’s why we click the first link and call it a day. AI Overview is custom-built for this tendency. It delivers quick satisfaction. But satisfaction without engagement is hollow. It’s like eating cotton candy—tastes sweet, but disappears before you even realize what happened.

The more we rely on AI to summarize for us, the less we exercise the parts of our brain responsible for critical thinking, curiosity, and memory. We stop asking follow-up questions. We stop wondering. We stop comparing sources. And slowly, we stop thinking for ourselves.

Ever Heard of “Cognitive Lethargy”?

It’s real thing. Not an official diagnosis, but a growing concern. It’s what happens when we get so used to being fed information that we lose the ability to wrestle with it. We become mentally sluggish. Not stupid, just…uninvolved. We start using words like “vibe” or “I think I heard somewhere?” instead of actually knowing. We forget faster. We feel less connected to the knowledge we absorb. This is not just a learning issue. It’s a living issue. Because how we learn is how we experience the world. If we stop engaging with information, we start engaing from everything else, too.

Okay, Let’s Talk About That Crayon Example Again

I mentioned this earlier, but let me dig in deeper because it’s too good not to. A friend of mine was helping her kid with a school project on the history of crayons. She Googled “When were crayons invented?” and, as expected, AI Overview gave her a neat, no-nonsense answer: Crayons were invented in 1903 by Binney & Smith.”. She repeated that to her kid. Done. But later, her kid asked, “Why 1903? And why did they start with just eight colors? And how did they pick the names?” She had no clue. So, she did the unthinkable: she kept searching. She clicked a few articles. Found a blog that talked about the original crayon color names—like “Maize” and “Carnation Pink”. She discovered that some old color names were changed because they were racially or culturally insensitive. She even watched a video about how crayons are made in factories today.

Now she was not just helping her kid. She was learning herself. She was excited. Later that night, she brought it up at dinner with friends. One of them used to collect vintage crayon boxes as a kid. They talked for 20 minutes. That’s what discovery looks like. Not just reading a sentence—but connecting with it.

More Examples? Oh, I Got ‘Em.

Example 1: Black Holes

I searched “What is a black hole?”

AI said: “A black hole is a region in space where the gravitational pull is so strong that nothing, not even light, can escape from it.”

Cool. But if  Had I dived deeper, I might have found mind-blowing stuff: time slows down near black hole. Some theories suggest they could lead to wormholes. There is a supermassive one at the center of our galaxy. And Stephen Hawking once joked about aliens using them as garbage disposals. None of that is in the summary. You have got to go digging.

Example 2: Bananas

Yep, bananas.

I Googled: “Are bananas good for you?”

AI said: “Bananas are high in potassium and a good source of fiber and vitamin B6.”

End of story?

No way. If we Click around and we will learn that the bananas we eat today are not even the original kind. The wild ones had seeds. The current banana—called the Cavendish—is in danger of going extinct because of a fungus. There is a global banana crisis happening right now, and most people have no idea.

Again: not in the overview.

So, What Can We Do?

Do not worry, this is not a “throw your phone in the river and go live in the woods” kind of rant. I am not anti-AI. I am just pro-curiosity. Here is what we can do to keep our minds sharp and our wonder alive:

  1. Scroll Past the Overview

Yes, the AI Overview is right there. It’s tempting. But resist. Pretend it does not exist. Click on something else. Let your eyes wander. That’s where the magic begins.

  • Follow the Weird

Find the blog that looks oddly specific. The Reddit thread with too many comments. The YouTube with a terrible thumbnail but surprisingly good content. Follow the trail.

  • Ask “What is Missing?”

Every summary leaves stuff out. Ask what’s not being said. Who’s behind the answer? What perspective is missing? This turns you from a reader into a thinker.

  • Talk About What You Learned.

Nothing makes knoweldge stick like sharing it. Tell a friend. Text a sibling. Post a little nugget on social. You will remember it way better, and you might even spark someone else’s curiosity.

In the End, It’s About Ownership

AI overviews can serve us information. But it cannot give us the thrill of discovering it ourselves. It cannot make us gasp, or laugh, or raise your eyebrows. It cannot give us that feeling of “Wait—how did I know this?!” Only we can do that. When we let ourselves get a little lost in learning—when we take our time and let curiosity lead—we are not just collecting facts. We are building connections. We are flexing our brain. We are staying alive inside.

So, Next Time You Google Something…

Skip the overview. Dive into the mess. Read more than one thing. Let a question lead to another. Let ourselves be confused. Let ourselves be amazed. Because when we fight for the answer—even a little—we own it. It becomes part of us. And maybe, just maybe, we will fall in love with learning all over again.

A Singleton Definition of Rhetoric

Really curious and committed to finding a crisp, condensed, and coruscating definition of rhetoric that synthesizes our divergent and heterogenous pedagogical practices? If so, be with me till this blog ends. I ask you to quantify how many definitions of rhetoric we have. In that case, you will undoubtedly be in an awkward situation because we have countless definitions of rhetoric. When memorizing some striking definitions of rhetoric to make our rhetorical practices progressively consistent, we often tend to mug up only those definitions that have been replicated by several practitioners of rhetoric since the time of the Sophists and Aristotle. Out of many definitions of rhetoric, don’t we choose the definition of Aristotle? Of course, we do. After all, who can escape from the influence of the issue of Stagira (https://en.wikipedia.org/wiki/Stagira_(ancient_city). The truth is, practitioners and scholars of rhetoric are bound to supplicate to the son of Stagira as they bog down to any hefty and hectic deadlock during any dialogic gridlock and rhetorical suck. If we count the history of rhetoric and composition as a dome, and if we take rhetorical plurality as an august canopy crown of that dome, isn’t Aristotle’s definition of rhetoric—as a faculty of exploring the available means of persuasion (http://classics.mit.edu/Aristotle/rhetoric.1.i.html)—the central pillar of this dome? Forgive my indulgence in this metaphoric magnetism. Let me be specific and zoom in on what I will do in this blog.

Aristotle’s definition of rhetoric is indeed an axis of authority. By the same token, it is no less evident that this definition has undergone gradual definitional permutations and combinations because our field has to borrow fresh theoretical insights to cross-fertilize its existing pedagogical paradigms. Just a survey of how rhetoric unfolded from Stagira to Stony Brook is enough to prove this. So much has happened. We have added many gadgets and gizmos to the warehouse of rhetoric and composition. Are you ready to count what has been added to the rampart of rhetoric and the crescent of composition? If not, take a few seconds and jiggle your fingers up and down to count. We have disability rhetoric, cultural rhetoric, multimodal rhetoric, new media studies, composition studies, writing studies, environmental rhetoric, intercultural rhetoric, neuro-rhetoric, the rhetoric of science, and medical rhetoric, to name just a few. Don’t we have a rainbow of rhetoric in the sky in the second quarter of the twenty-first century? Needless to say, we have. It is evident that from the ‘Kalpavriksha’ (https://www.deccanherald.com/opinion/oasis/kalpavriksha-the-tree-of-life-1063674.html) of rhetoric and composition, so many branches have been growing and extending outward, making rhetoric and composition a seductive rendezvous of all kinds of knowledge-lovers. With this unbacked postulation, I have gone too far. Let me make a cut on the flight of my fancy and be down-to-earth.  

Beset profusely by so many definitions of rhetoric, don’t we feel beleaguered? Aren’t our students up in the air, having been exposed to this rainbow of rhetoric? Yep. I feel so. But who knows what others think? You may feel undaunted. Given this situation, in which multifarious and various definitions of rhetoric are all set to mangle and mold our understanding, it is good to have a singleton definition of rhetoric. At the present moment, our field needs to be integrated and synthesized. Our multifaceted research practices, too, need to be coalesced without resorting to totalization. My friends, you may ask: why do we need a singleton definition of rhetoric in the age in which we have been thriving under the oasis of DEI (Diversity, Equity, and Inclusion)?

I, too, subscribe to the same conviction regarding your quizzical query. Doubtless, DEI is the hallmark of our rhetorical molding, compositional cumulation, and ethical accumulation (https://youtu.be/akn7cIFYqHI). But I need to craft a coherent definition to give our students an all-encompassing definition of rhetoric. Rhetoric is a faculty that explores available and unavailable means of persuasion in online and onsite contexts and entails using tools and technology.

For Sophists, rhetoric was a play on language and a site for the enactment of relativity (https://www.jstor.org/stable/465507). For Aristotle, it was analogous to quantifying available means of persuasion. There are always unavailable means of persuasion, which Aristotle merely mentioned. For Roman rhetoricians, rhetoric was an effective method of communicating ideas and thoughts. From the twentieth century, rhetoric was lavished with epistemic blessing. It has its epistemic aura and ambiance. Thomas Kuhn, R.L. Scott, and Stanley Fish baptized rhetoric and put it on an epistemic pedestal (https://www.jstor.org/stable/20865760). With the diffusion of practices such as transversality, materialism, new materialism, speculative realism, circulation studies, object-oriented ontology, and ambient attunement, rhetoric ascended to the throne of ontology. Let me be lucid and crisp in my reflection. Rhetoric became the site of an ontological disclosure.

Rhetoric no longer remained an effective communication method, nor was it confined to its epistemic comfort zone. Surprisingly enough, it occurred as a site of revelation. To put the point the other way around, rhetoric emerged as a manifestation mode. It began to manifest how matter impacts our writing, how space operates in our compositional practice, how design incentivizes our creative bent of mind, and how invisible, unseen, and untapped forces contribute to our rhetorical and compositional practices. In Karen Barad’s words, “nature is taken to be revealed by…” (41). The revelation is possible if innovative and ground-breaking rhetorical practices are identified, manifested, and then set toward their effective utilization. Manifesting unavailable means will be a decisive part of rhetoric’s magnifying magnitude (https://www.dukeupress.edu/meeting-the-universe-halfway). In the same line of adding force to her perspective, Barad has said, “Theorizing is… a material practice” (55). Another idea of attunement has come in line with rhetoric’s entanglement with the modality of manifestation. A crown of circulation studies and digital tracking, the concept of attunement is another rhetorical trick of triggering what lies beneath the surface. Laurie Gries popularized the idea of attunement as an approach to manifesting the vibrancy of matter (https://cedar.wwu.edu/wwuet/858/). Related to this idea of attunement is Thomas Rickert’s rhetoric of ambient, which treats a being’s embeddedness in environs as a strategy for disclosing the un-manifest means of persuasion (https://www.presenttensejournal.org/volume-4/book-review-rickerts-ambient-rhetoric/).

I am rambling in the above paragraph, digressing from the moot point toward theoretical thickness, right? If so, my apologies. But I am not on track with deviation. With so much insight into the rhetoricity of materiality, spatiality, and transversality, it will be easy to illuminate how rhetoric is geared toward unearthing what lies beneath the veneer of vapidity. Other things remain the same: disciples of rhetoric and commoners of composition always hunt for fresh and fructifying means of persuasion. In addition, this hunt renews with great fanfare if pilgrims of Stagira are befuddled and beleaguered by an excess of available means of persuasion. Hence, rhetoric is the art of exploring available and unavailable means of persuasion.

So far, rhetoric has been restabilized in search of manifest and unmanifest means of persuasion. A query popped up in my head before I started to augment this definition. I think your minds are also fraught with queries aplenty.  The question is, where lies the means of persuasion? They seldom hang in the air. Do they exist in the void and vacuum? With sharp certainty, I can say they don’t. Undoubtedly, they exist in some place, in some location, in some situation, don’t they? Let’s use the term ‘context’. Both available and unavailable means of persuasion exist in context. A host of buzzwords—situated cognition, situated knowledge, cultural rhetoric, societal practice, a community of practice—have made the field of rhetoric and composition resound with vibrancy and poignancy. Thus, it won’t be out of the ordinary if I conclude that both seen and unseen means of persuasion lie in context. Please, offer me a morsel of the privilege of reiteration here. Rhetoric is a faculty of exploring both available and unavailable means of persuasion in context.

Folks, bear with me a bit. I no longer like to exhaust your patience with this raw reflection of mine. I need to add a slight chunk to the new definition of rhetoric I am weaving. We all know how dependent we are on technology, don’t we? Technology is to our world as the Nile is to Egypt. Our unfettered leverage of technology for writing and the availability of plenty of social media space have jointly given rise to the concept of a virtual world, online community, online context, and online system (https://owl.purdue.edu/owl/teacher_and_tutor_resources/teaching_resources/remote_teaching_resources/technology_in_the_writing_classroom.html). In connection with this shifting notion of context, it can be added that context can be both online and onsite. That means the context where our rhetorical practices and compositional pedagogies are enacted would be online (synchronous and asynchronous classes) and onsite when the instructor is in a real classroom in person. Here, I do not want to bore you with yawning by repeating what you already know, as we all have a bitter experience of teaching and being taught during the last pandemic in an online context. With this accepted assumption, let me be crisp and concise and hazard a somewhat blown definition of rhetoric, which explores both available and unavailable means of persuasion in online and onsite contexts. Whoops! I am nearing the end of my venture to craft a singleton definition of rhetoric. Let me give it a finishing touch. Pardon me for reminding you again how we are overwhelmed by the deluge of buzzwords such as digital media, digital storytelling, new media composition, computer and composition, digital humanities, visual literacy, and multimodal composition (https://pressbooks.ulib.csuohio.edu/csu-fyw-rhetoric/chapter/7-2-what-is-multimodality/). All of this boils down to the customized use of the tools and writing technology. As claimed by Collin Gifford, technology is not a means but an end (https://www.enculturation.net/ecologies-of-new-media-practice). Doesn’t it show how deeply steeped we are in the imperative of technology’s use in our writing classrooms? Whatever the context of our rhetorical and compositional practices, the tools and the technology of teaching and learning have exceeded being just a means to being an end in themselves. Our context, be it online or onsite, entails the imperative of using tools and technology. The context for applying rhetoric and compositional strategies is increasingly technology-inflected.

Let me briefly sum it up. In light of all these developments in rhetoric and composition, let me present a chiseled and checkered definition of rhetoric. Rhetoric is a faculty of exploring available and unavailable means of persuasion in online and/or onsite contexts. It entails the imperative of using technology not as a means but as an end. Isn’t this definition integrationist? Doesn’t it merge all the threads of rhetorical developments into a single garland? What/how would you think about it? Finally, does it meet your expectations well? For the smooth and relentless evolution of the field, it would be imperative to organize best practices around the spirit of the singleton hypothesis. Hence, I hope this blog makes us aware of this direction.