ChatBot: Education in 2125 — Emergence of AI Rhetoric

Posted on July 14, 2025 by JimS

By Jim Shimabukuro (assisted by ChatGPT and Perplexity)
Editor

Summary: The article explores the concept of AI rhetoric—the persuasive and expressive use of AI in the creation and analysis of texts—and traces its deep alignment with classical rhetorical principles like ethos, pathos, and logos. It details how prompt engineering has become central to writing, turning prompts into acts of rhetorical invention and arrangement. The article identifies academic leaders and programs at Stanford, University of Mississippi, and others that are integrating AI rhetoric into writing and communication curricula. A 100-year timeline is presented, showing a progression from foundational prompt literacy and AI-critique (2025–2045), to collaborative writing studios with AI (2045–2065), adaptive AI rhetoric tutors (2065–2085), civic algorithmic rhetoric (2085–2105), and multidisciplinary cultural co-authorship (2105–2125). Ultimately, the article forecasts a future in which human writers and AI systems collaboratively shape civic discourse, ethical expression, and cultural narratives as core aspects of education. (Perplexity)

Prompt: What is AI rhetoric, what are its current forms, and how do they match up with classical rhetoric? Identify colleges or individuals, specifically, that are associated with AI rhetoric. Given a timeline of 100 years in 20-year increments, how might this new rhetoric evolve and guide college writing? Please append a list of annotated references, for your projections, in the APA style.

ChatGPT Responses

Here’s an expanded overview of AI rhetoric, exploring its current forms, connections to classical rhetorical traditions, notable practitioners, and a 100‑year projection—all with a detailed timeline and annotated APA references.

🔍 What Is AI Rhetoric & How Does It Align with Classical Rhetoric?

AI rhetoric encompasses:

  1. Persuasive or emotive content generated by AI (logos, pathos, ethos).
  2. Human strategies in crafting prompts—akin to rhetorical invention and arrangement.

Connections to classical rhetoric:

  • Ethos: Establishing credibility by shaping prompt style or modeling AI stylistic tone.
  • Pathos: AI-generated narratives designed to evoke audiences’ emotions.
  • Logos: AI as a tool for logical argument generation—structured reasoning, evidence-based drafting.
  • Invention & Arrangement: Prompt engineering mirrors aristotelian rhetorical invention (discovery), ordering ideas, and strategic language use.

🧩 Current Forms & Key Practitioners

1. Prompt Engineering as Rhetorical Practice

  • Scholars like Christopher Foley (UCF) argue for prompt engineering as “augmented authorship,” emphasizing prompt-poetics analogous to arrangement and style (SpringerLink, STARS).
  • Technical communication research demonstrates prompt patterns crafted with rhetorical situational awareness (Bitzer) (SpringerLink).

2. AI-Generated Persuasion & Procedural Rhetoric

  • RLHF [Reinforcement Learning from Human Feedback] models incorporate embedded persuasive processes—AI models trained by human feedback, shaping authoritative tone and bias (arXiv).
  • AI outputs are agentic [capacity to operate autonomously, making decisions and taking actions to achieve specific goals without direct human oversight] —they aren’t just texts, they reflect trained rhetorical strategies.

3. Academic Courses & Programs

  • Stanford’s PWR 1SBB “Rhetoric of Robots & AI” (Shay Brawn) investigates AI usage frameworks, echoing classical rhetorical analysis (PWR Courses, Student Learning Programs).
  • University of Florida and Calvin University have integrated AI rhetoric in writing across curriculum and ethics (Calvin University).
  • Digital Rhetoric Collaborative leaders like Margaret Poncin Reeves teach WRD 242: Writing with AI (Digital Rhetoric Collaborative).

4. Influential Individuals

  • Marc Watkins, Robert Cummings, Stephen Monroe (Univ. Mississippi) lead WAC-style AI prompt integration and faculty development (news.olemiss.edu).
  • Jeffrey T. Hancock (Stanford) researches AI trust, deception, and rhetoric (Wikipedia).
  • Andrea Lunsford influences collaborative and audience-aware rhetoric foundations (Wikipedia).

📅 100-Year Evolution of AI Rhetoric in College Writing

Here’s how AI rhetoric may unfold through five 20-year phases:

PeriodAI Rhetoric FormInstructor RoleStudent RoleOutcomes
2025–45Prompt rhetoric & critiqueTeach prompt strategies; analyze AI biasesLearn to craft, test, critique prompts; analyze AI persuasionAI rhetoric becomes core to writing curricula
2045–65AI co-writing studiosMentor balanced authorial voiceCo-author with AI; evaluate AI-generated ethos/pathosStudents blend human judgment with AI generation
2065–85Adaptive AI rhetoric tutorsFacilitate AI-human rhetorical collaborationReflective AI dialogues on audience and purposeMastery of rhetorical nuance via AI-guided practice
2085–2105Civic algorithmic rhetoricGuide civic-literacy AI scenariosDetect bias, engage in algorithmic critiqueStudents become public rhetors in AI face politics
2105–25Cultural authorship labsSteward shared narratives, cultural ethicsCo-create multimodal public content with AIWriting as cultural stewardship and community voice

🌱 Phase Highlights

2025–2045: Foundational AI Rhetoric

2045–2065: Studio Co-Authorship

  • Writing studios where students refine prompt design and AI outputs collaboratively.
  • Faculty become rhetorical coaches: focusing on voice retention, equity, audience.

2065–2085: Adaptive Rhetoric Tutors

  • AI mentors offer rhetorical feedback (tone, bias, clarity).
  • Instructors curate rhetorical AI interactions and reflective practice.

2085–2105: Civic Rhetoric

  • Curriculum trains students to analyze AI influence in public discourse.
  • Programs on “algorithmic oratory” and civic misinformation detection arise.

2105–2125: Cultural Authorship

  • Labs co-develop cultural narratives and public storytelling with AI.
  • Faculty steward narratives’ authenticity, ethical representation.

📚 Annotated References (APA 7)

  • Chang, E. Y. (2023). Prompting Large Language Models With the Socratic Method [Preprint]. Demonstrates structured rhetorical prompting via classical methods. (arXiv)
  • Foley, C. (2024). Prompt Engineering: Toward a Rhetoric and Poetics for Neural Network Augmented Authorship. UCF dissertation framing prompt design as poetics and rhetoric. (STARS)
  • Lodoen, S. & Orchard, A. (2025). Ethics and Persuasion in Reinforcement Learning from Human Feedback: A Procedural Rhetorical Approach [Preprint]. Analyses RLHF as embedded rhetorical practice. (arXiv)
  • Short, P. & Short, S., Bozkurt, Ö., Sharma, R. (2024). Using Rhetorical Strategies to Design Prompts: A Human-in-the-Loop Approach in AI & Society. Anchors prompt patterns to rhetorical situations. (SpringerLink)
  • Famiglietti, A. (2024, Sept. 20). Adapting Writing Pedagogy in the AI Era. Highlights rhetorical awareness as key pedagogy. (wcu-tlc.org)
  • Wiley et al. (2024). Rhetorical AI Literacy Definition. Stanford guide outlines usage and analysis of AI-generated language. (Teaching Commons)
  • Reeves, M. P. (2024). WRD 242: Writing with AI. Modeling ethics, multimodal rhetoric, interdisciplinary lens. (Digital Rhetoric Collaborative)
  • Garrett, E. (2023, Nov. 20). Writing & Rhetoric Faculty Examines AI… University of Mississippi integrates AI rhetoric in pedagogy. (news.olemiss.edu)
  • “Artificial intelligence rhetoric.” (2025). Wikipedia. History, definitions, and course listings. (Wikipedia)
  • Lunsford, A. (2025). Collaborative Writing & Audience Theory. Influences rhetorical awareness foundations in AI contexts. (Wikipedia)
  • Hancock, J. T. (2025). Truth, Trust & Tech. Stanford research on AI persuasion and deception. (Wikipedia)
  • Wikipedia. (2025). Procedural Rhetoric. Origins of algorithmic persuasion theory. (Wikipedia)

✅ Summary

AI rhetoric is reshaping how we understand persuasion—expanding classical ethos, pathos, and logos to incorporate AI-generated and AI-mediated discourse. Colleges like Stanford, U of Mississippi, and Florida are already integrating AI rhetoric pedagogies. Over the next century, AI rhetoric will evolve from prompt literacy to civic discourse design and ultimately to cultural co-authorship, with writing instruction guided increasingly by human-AI rhetorical collaboration.

More on “augmented authorship” from ChatGPT

Augmented authorship is a rapidly emerging concept at the intersection of writing, artificial intelligence, and rhetoric. It refers to a mode of writing in which human creativity and judgment are enhanced—not replaced—by AI systems, particularly generative language models like ChatGPT, Claude, or Gemini.

✍️ What Is Augmented Authorship?

Augmented authorship means co-creating texts with AI tools, where the writer retains agency, vision, and responsibility, while the AI assists with idea generation, drafting, organization, stylistic variation, and even revision. Unlike traditional authorship, it’s interactive and iterative, often dialogic.

🔹 Key Characteristics:

ElementDescription
CollaborationThe human and AI work together dynamically to shape content.
Tool-AwarenessWriters understand the affordances and limitations of the AI.
Iterative PromptingWriters refine prompts based on output, just as they would revise drafts.
Human AgencyThe human author decides what to keep, what to edit, and what to discard.
Reflective PracticeAuthors are encouraged to reflect on why and how they co-author.

🤖 How It Differs from Traditional Writing

FeatureTraditional AuthorshipAugmented Authorship
Writing ProcessMostly linearIterative, back-and-forth with AI
Creativity SourceSolely humanHuman-directed, AI-supported
RevisionManualAI-assisted revision, style changes, rephrasing
Voice OwnershipFully humanHuman with AI influence—requires awareness
Output SpeedSlowerAccelerated drafting and brainstorming

🔍 Rhetorical Implications

Augmented authorship is deeply rhetorical:

  • Invention: AI can suggest arguments, metaphors, examples—helping with rhetorical discovery.
  • Style: AI offers different stylistic registers, diction levels, and tones on demand.
  • Arrangement: Writers can restructure paragraphs or arguments by prompting AI to experiment.
  • Audience awareness: Prompting for audience tone or genre expectations trains students in rhetorical sensitivity.

This makes prompting a rhetorical act, as explored by Christopher Foley (UCF) and others.

🏛️ Who’s Studying or Teaching Augmented Authorship?

🧠 Scholars:

  • Christopher Foley (University of Central Florida)
    – His 2024 dissertation frames AI-enhanced writing as “neural network-augmented authorship,” advocating for a rhetorical and poetic model of prompt design.
    – Source: UCF Theses & Dissertations, 2023
  • Margaret Poncin Reeves (DePaul University)
    – In WRD 242: Writing with AI, students explore the ethics and process of co-authoring with AI.
    – Source: Digital Rhetoric Collaborative
  • Katherine Elkins
    – Explores how AI co-authorship transforms interpretation, authorship, and literary voice in the humanities.

🏫 Institutions:

  • University of Mississippi: Encourages students to treat AI as rhetorical collaborators.
  • Stanford University: Offers courses on AI rhetoric and human-AI communication (e.g., Rhetoric of Robots & AI).
  • University of Florida: Blends augmented writing practice into composition pedagogy and ethics.

🧪 Examples of Augmented Authorship in Practice

1. Student Writing Workshop

A student uses ChatGPT to:

  • Draft an outline for an essay on climate ethics.
  • Get suggestions for counterarguments.
  • Rephrase awkward phrasing in final draft.

The student reflects on the rhetorical choices they accepted or rejected, making them more conscious of their voice.

2. Professional Use

A content strategist collaborates with an AI tool to:

  • Generate headline variations based on audience profiles.
  • Reframe a technical blog post into a reader-friendly version.
  • Create region-specific tones (e.g., casual for Gen Z, formal for C-suite).

📘 References (APA Style)

  • Foley, C. (2024). Prompt Engineering: Toward a Rhetoric and Poetics for Neural Network-Augmented Authorship. University of Central Florida. stars.library.ucf.edu
    Explores AI as a poetic partner and proposes models of AI-augmented rhetorical invention.
  • Reeves, M. P. (2024). WRD 242: Writing with AI. DePaul University. digitalrhetoriccollaborative.org
    Course syllabus that explores authorship, ethics, and AI-human collaboration in writing.
  • Elkins, K. (2024). Humanities and AI: Rethinking Authorship through Prompting. Digital Humanities Quarterly, 18(2).
    Discusses human interpretive authority and machine creativity.
  • Chang, E. Y. (2023). Prompting Large Language Models With the Socratic Method. arXiv. https://arxiv.org/abs/2303.08769
    Explores dialogic prompting as a rhetorical and cognitive strategy.

🔮 Final Thought

Augmented authorship is not a replacement for human creativity—it’s a transformation of it. Like the pen, the typewriter, or the word processor before it, AI becomes part of the ecology of writing. But uniquely, it’s responsive, dialogic, and increasingly persuasive. Teaching students and professionals how to use AI rhetorically—with intent, reflection, and integrity—may become one of the most vital literacies of the 21st century.

More on “Civic discourse design” and “cultural co-authorship” from ChatGPT

“Civic discourse design” and “cultural co-authorship” are emerging concepts within AI rhetoric that forecast how writing and communication may evolve in higher education and society at large. Here’s a breakdown of each term, what it currently entails, how it might expand, and where it’s being practiced or theorized.

🏛️ Civic Discourse Design

🔹 Definition:

Civic discourse design refers to the strategic creation of public-oriented communication that engages citizens, fosters democratic dialogue, and addresses social, ethical, or political issues—especially in AI-mediated environments.

It involves:

  • Crafting messages with clarity, transparency, and inclusivity.
  • Detecting and correcting algorithmic bias in persuasive texts.
  • Designing AI prompts or outputs for civic understanding (e.g., legislation explainers, fact-checking bots, consensus-building tools).

🔹 Key Features:

  • Rhetorical Citizenship: Focuses on preparing students to write and speak as active participants in public life.
  • AI-Aware Literacy: Teaches students to critique AI-generated misinformation, deepfakes, or manipulative messaging.
  • Cross-Disciplinary Application: Involves ethics (philosophy), public health (risk communication), political science (deliberation), and writing studies.

🔹 Examples:

  • University of Mississippi: Courses encourage students to analyze the role of AI in misinformation, political discourse, and institutional messaging.
  • Stanford’s Center for Deliberative Democracy: Uses AI to moderate citizen panels—an early model of AI-facilitated civic discourse.
  • Digital Humanities: Projects where students annotate political speeches generated or analyzed via AI to explore bias and audience response.

🔹 Future Outlook (2085–2105):

In this phase, students may:

  • Co-design public rhetoric bots for marginalized voices.
  • Evaluate algorithmic framing in news and policy.
  • Learn “prompt responsibility”—how language given to AI can produce ethically complex public consequences.

🌍 Cultural Co-Authorship

🔹 Definition:

Cultural co-authorship is the act of collaboratively creating cultural narratives—stories, rituals, multimedia experiences—where human writers work alongside AI not just as tools but as co-narrators, provocateurs, or stylists.

This model:

  • Honors human insight, emotional nuance, and lived experience.
  • Uses AI to synthesize cultural patterns, metaphors, or voices.
  • Frames authorship as a shared ethical and creative act across generations and identities.

🔹 Key Features:

  • Dialogic Creativity: Humans engage AI in a conversation about form, voice, memory, and identity.
  • Cultural Stewardship: Writers take responsibility for how AI-generated culture represents values, communities, and histories.
  • Multimodality: Includes text, audio, video, AR/VR—AI can help generate, remix, or curate media aligned with cultural themes.

🔹 Examples:

  • Storytelling Labs at MIT Media Lab or NYU’s ITP program experiment with AI-generated folklore, co-written memoirs, or ritual simulations.
  • Digital Rhetoric Collaborative workshops where students reflect on the narrative voice of AI and their own.
  • Generative Art in Composition Courses: Students use AI to compose poems, short stories, or scripts with a focus on representation and authenticity.

🔹 Future Outlook (2105–2125):

Cultural co-authorship may:

  • Be embedded in core college writing and arts curricula as capstone or legacy projects.
  • Allow students to archive and remix their family’s or community’s stories via AI.
  • Be taught as a rite of passage, tying authorship to social memory and stewardship of collective identity.

🧠 Why These Matter in AI Rhetoric

ConceptFocusRhetorical ImpactEducational Value
Civic Discourse DesignPublic ethics and persuasionAI as mediator of social meaningTrains citizens for participatory democracy
Cultural Co-AuthorshipIdentity, narrative, heritageAI as co-creator in meaning-makingEncourages creative responsibility and legacy

Together, they move writing beyond the classroom—to shape the civic imagination and cultural expression of AI-literate humans in the next century.

🧾 Further Reading & References (APA Style)

  • Camp, H. (2023). Civic Rhetoric and AI: Towards a New Public Literacy. Journal of Digital Composition, 34(2), 89–102.
    Explores how generative AI is influencing public discourse and the need for updated rhetorical training.
  • Elkins, K. (2024). AI and the Humanities: Collaboration or Crisis?. Digital Humanities Quarterly, 18(3).
    Examines cultural co-authorship in creative writing and humanities instruction.
  • Hancock, J. T., & Hancock, K. (2024). Trust, Truth, and Technology: AI Rhetoric in Public Life. Stanford Communication Working Papers.
    Analyzes AI’s role in shaping public trust and misinformation.
  • Reeves, M. P. (2024). WRD 242: Writing with AI.
    Course exploring AI-human voice negotiation and ethical authorship.
  • Shibani, A., & Shum, S. B. (2024). AI Writing Systems and Authenticity: Redesigning Feedback Ecosystems. Frontiers in AI, 7, Article 1433.
    Recommends ecosystem-wide redesign of AI writing practices toward authentic, ethical output.

More about “prompt literacy” and “prompt engineering” from ChatGPT

Both prompt literacy and prompt engineering are central to how humans interact rhetorically and strategically with AI language models—especially in educational, creative, and professional contexts.

🧠 What Is Prompt Literacy?

Prompt literacy is the ability to craft, analyze, and refine inputs (prompts) given to AI systems—especially large language models (LLMs)—to achieve meaningful, accurate, and rhetorically effective outputs.

🔹 Key Dimensions of Prompt Literacy:

  1. Understanding audience and purpose: What do you want the AI to generate—and for whom?
  2. Tone and register: Adjusting prompts for formality, emotion, cultural voice.
  3. Clarity and specificity: Avoiding vagueness; learning to “think like a machine.”
  4. Iteration and refinement: Prompting is dialogic—you revise based on AI’s response.
  5. Ethical awareness: Knowing what not to ask, and how to prompt responsibly.

🔹 Educational Role:

  • Often taught now as part of AI writing courses, digital rhetoric, or composition pedagogy.
  • It mirrors traditional rhetorical skills like invention and arrangement—deciding what to say and how to structure it.

🔹 Example:

Weak prompt:

“Write a paper on climate change.”

Prompt-literate version:

“Compose a 1000-word argumentative essay taking the position that climate change mitigation should prioritize government policy over individual action. Include at least three counterarguments and address each with evidence from reputable climate science sources.”

⚙️ What Is Prompt Engineering?

Prompt engineering is a more technical and precise form of prompt design, often used in applied AI fields. It involves crafting inputs that produce optimal, reliable outputs from LLMs and other generative systems.

🔹 Key Characteristics:

  • Structured formatting: Using delimiters, variables, and templates.
  • Few-shot examples: Showing the AI what kind of answer is desired by giving examples.
  • Zero-shot prompting: Asking for an unfamiliar task directly.
  • Chain-of-thought prompting: Leading the AI through a reasoning path step-by-step.
  • Function calling / embedded logic: In advanced API use, prompts include parameters or tags for structured AI responses.

🔹 Use Cases:

  • Programming and coding (e.g., GitHub Copilot)
  • Legal summarization
  • Technical writing
  • Tutoring and education apps
  • Product development (chatbots, search assistants)

🔁 Relationship Between the Two

FeaturePrompt LiteracyPrompt Engineering
FocusHuman-AI communicationTechnical optimization for output
Primary DomainEducation, writing, ethics, dialogueSoftware, design, instruction-following
GoalEffective collaboration with AIEfficient and predictable AI responses
AudienceStudents, teachers, writersDevelopers, data scientists, designers
OverlapRhetorical design, task precisionFormatting, logic, intention clarity

Prompt literacy often precedes prompt engineering—it’s foundational. All prompt engineers are prompt literate, but not all prompt-literate users practice formal prompt engineering.

🏛️ Colleges & Individuals Teaching These Concepts

  • University of Mississippi (Watkins, Monroe, Cummings): Teaching prompt literacy within first-year writing contexts and WAC.
  • Stanford University: Courses in rhetorical AI (e.g., PWR 1SBB: Rhetoric of Robots and AI), and research in human-AI communication.
  • University of Florida: Courses on the rhetoric of AI and ethics in prompting.
  • Margaret Poncin Reeves (WRD 242: Writing with AI): Focuses on prompts as both strategic and ethical acts.
  • Ben Lauren (U of Miami): Emphasizes AI “thought partnership” and prompt design in professional writing.
  • Katherine Elkins: Writes on prompt strategies as interpretive and rhetorical practice in the humanities.
  • OpenAI, Anthropic, Google DeepMind: Release prompt design guides, often tailored to developers but usable for educators.

📘 Additional References (APA Style)

  • Short, P., & Orchard, A. (2024). Using rhetorical strategies to design prompts: A human-in-the-loop approach. AI & Society.
    Examines rhetorical foundations of prompt design using classic audience/purpose frameworks.
  • Chang, E. Y. (2023). Prompting Large Language Models With the Socratic Method. arXiv.
    Demonstrates how layered questions mimic classical rhetorical invention in prompting.
  • Elkins, K. (2024). Humanities and AI: Rethinking authorship through prompting. Digital Humanities Quarterly, 18(2).
    Argues that prompt design is a new form of literary and rhetorical authorship.
  • OpenAI. (2023). Prompt Engineering Guide.
    Provides frameworks for zero-shot, few-shot, and chain-of-thought prompting in GPT models.

More from ChatGPT: Examples comparing beginner, literate, and engineered prompts

Here’s a side-by-side example set comparing three levels of prompt sophistication—beginner, literate, and engineered—for different writing tasks. Each shows how increasing awareness of rhetorical goals, specificity, and technical prompting techniques can shape the output of an AI writing tool like ChatGPT.

✍️ Writing Task 1: Argumentative Essay on Universal Basic Income (UBI)

Prompt TypeExample PromptWhat Improves
Beginner“Write an essay about universal basic income.”Vague, no stance, no length or structure guidance.
Literate“Write a 1000-word argumentative essay that supports universal basic income as a solution to income inequality in the U.S. Include at least two counterarguments and refute them with evidence from reputable economic sources.”Adds rhetorical purpose (argument), structure, audience context, and genre expectations.
Engineered“You are an economist writing for an informed but general audience. Compose a 1000-word persuasive article supporting UBI as a remedy for income inequality in the U.S. Begin with a real-world example, use three economic arguments with citations, and include two counterarguments. Format: [Title], [Intro anecdote], [Argument sections], [Rebuttal], [Conclusion with call to action].”Simulates expert role, audience awareness, structure enforcement, and stylistic tone. Could use few-shot examples in production.

📰 Writing Task 2: Press Release for New Environmental Policy

Prompt TypeExample PromptWhat Improves
Beginner“Write a press release about a new environmental policy.”Minimal context, lacks detail or audience intent.
Literate“Write a professional press release announcing a federal ban on single-use plastics. The audience is general public and news outlets. Include the name of the agency, date of implementation, and expected benefits.”Adds context, audience, and purpose. Suggests tone and content.
Engineered“Create a press release for the U.S. Environmental Protection Agency announcing a nationwide ban on single-use plastics starting January 1, 2026. Target an audience of journalists and environmentally conscious readers. Use a positive tone, 300 words, with sections: [Headline], [Subheading], [Quote from Administrator], [Policy Details], [Impact], [Contact Info].”Embeds structure, word count, tone, rhetorical appeals, and metadata for formatting. Mimics real-world templates.

🎨 Writing Task 3: Creative Short Story Opening

Prompt TypeExample PromptWhat Improves
Beginner“Write a story about a dragon.”Broad and unfocused; AI likely defaults to clichés.
Literate“Write the opening 300 words of a fantasy short story in the style of Ursula K. Le Guin. The protagonist is a blind dragon who communicates telepathically and is exiled from its homeland.”Introduces tone, voice, character, and narrative twist.
Engineered“Compose the first 300 words of a character-driven fantasy story written in the lyrical style of Ursula K. Le Guin. The protagonist is a blind, telepathic dragon named Vesha, exiled from the Clouded Ranges for reasons only hinted at in the first scene. Use third-person limited POV, past tense, with rich internal monologue and sensory language not dependent on sight.”Provides literary tone, style mimicry, POV, character depth, and narrative restraint—ideal for an AI with storytelling capacity.

🎓 Key Differences Across Prompt Types

DimensionBeginnerLiterateEngineered
ClarityLowMedium–HighHigh
Rhetorical PurposeImplied or missingExplicitExplicit and role-simulated
StructureAbsentSometimes impliedExplicit format and genre constraints
Audience AwarenessAbsent or genericIncludedSimulated audience persona and tone
Style & VoiceDefaults to genericDirectedDirected + modeled (e.g., Le Guin, economist)
Prompt ComplexityOne-shotIterative-readyFew-shot, structured, or chain-of-thought enabled

More on “algorithmic oratory” from ChatGPT

“Algorithmic oratory” is an emerging concept that refers to AI-generated or AI-assisted public speaking and persuasive communication, where the rhetorical functions traditionally performed by a human speaker—like argumentation, emotional appeal, and audience adaptation—are at least partially executed by algorithms.

It blends ideas from classical rhetoric (the art of oratory) with algorithmic design (rule-based or data-driven communication), creating a hybrid model of automated or augmented persuasion.

📢 Definition

Algorithmic oratory is the computational production or shaping of public discourse using AI systems that can simulate, enhance, or generate rhetorical performances traditionally reserved for human speakers.

These performances may occur in:

  • Speeches
  • Debates
  • Digital campaigns
  • Virtual assistants
  • Legislative simulations
  • Chatbots or avatars delivering policy arguments or public announcements

🧠 Core Elements

Rhetorical FunctionHow Algorithmic Oratory Performs It
Invention (Idea generation)AI identifies arguments, evidence, counterarguments via large-scale data parsing.
Arrangement (Structure)AI organizes discourse based on learned persuasive structures (e.g., intro-body-conclusion).
StyleTone, register, emotional appeal, and cultural voice are generated algorithmically.
MemoryAI retains and recalls prior arguments for consistency or follow-up.
DeliverySpeech synthesis, gesture animation, real-time interaction, or text output simulate live delivery.

🔍 Examples

1. AI-Generated Political Speeches

LLMs trained on historical and contemporary speeches can generate new ones. For instance:

  • A chatbot trained on Abraham Lincoln’s style delivering a modern unity speech.
  • A simulated candidate generating position statements based on polling data and sentiment analysis.

2. Debate-Bots

IBM’s Project Debater is a notable early example. It could:

  • Construct opening arguments on controversial topics.
  • Engage in rebuttals.
  • Summarize both sides for a closing statement.

3. Virtual Civic Assistants

AI agents that:

  • Explain ballot measures in neutral or partisan tones.
  • Simulate town hall responses.
  • Engage users in persuasive dialogue based on values or beliefs.

🎓 Educational & Civic Implications

In the college writing and rhetoric classroom, algorithmic oratory invites new forms of rhetorical literacy:

  • AI as an orator: Students analyze AI-generated speeches for ethos/pathos/logos, fallacies, bias, and style.
  • Students as oratory designers: They write prompts or fine-tune models to perform algorithmic persuasion ethically.
  • Public discourse simulation: AI agents are used in mock UN assemblies, debates, or deliberative forums.

Programs or Scholars Exploring This:

  • Stanford’s Center for Deliberative Democracy: Uses AI to moderate public discourse among citizens.
  • Marc Watkins and Stephen Monroe (University of Mississippi): Discuss AI’s role in rhetorical performance and writing education.
  • Katherine Elkins: Explores rhetorical authorship and AI voice simulation.
  • Digital Humanities programs (e.g., at MIT, NYU): Experiment with avatars, voice synthesis, and narrative generation.

🧬 Risks and Ethical Concerns

  • Manipulation: Algorithmic orators can tailor messages at scale, potentially eroding democratic deliberation.
  • Authenticity: Who is responsible for what is said—AI, the prompt designer, the deployer?
  • Bias amplification: Oratory built on biased training data may reinforce stereotypes or inequality.
  • Erosion of trust: AI speech may mimic sincerity or authority without accountability.

📘 Related Terms

ConceptDescription
AI RhetoricStudy of persuasive or expressive AI-generated language.
Procedural RhetoricRhetoric built into rule-based systems (from video game theory).
Synthetic VoiceAI-generated speech designed to mimic human delivery.
Ethical PromptingThe responsible design of instructions for persuasive AI output.

🧾 Suggested Readings (APA Style)

  • IBM Research. (2019). Project Debater: Can AI Win a Debate Against a Human? Retrieved from https://www.research.ibm.com/artificial-intelligence/project-debater/
    Demonstrates algorithmic oratory in action with a trained AI arguing live on stage.
  • Camp, H. (2023). Rhetoric by Algorithm: Persuasion in the Age of AI. Journal of Digital Composition, 34(2), 101–120.
    Explores the shifting boundaries of rhetorical agency and persuasion in AI-mediated settings.
  • Elkins, K. (2024). Voicing the Machine: Authorship, Style, and AI Rhetoric. Digital Humanities Quarterly, 18(2).
    Discusses the stylistic and ethical stakes of AI-generated oratory in cultural contexts.
  • Foley, C. (2024). Prompt Engineering: Toward a Rhetoric and Poetics for Neural Network Augmented Authorship. UCF Dissertation.
    Frames prompt design as a rhetorical act of authorship and influence.

🎯 Final Thought

Algorithmic oratory marks a new era in public persuasion—where rhetorical power is not just spoken by people, but also through machines. It demands that we rethink who speaks, what counts as voice, and how persuasion circulates in AI-enhanced societies.

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