Deliverables

This creative challenge has multiple deliverables:

1. Annotated Bibliography + Responses to Two Peers’ Posts – Summarize and critically engage with the assigned research on GenAI’s effects on cognition, creativity, and agency; respond to classmates’ interpretations.
2. Exercise – Evaluating and Revising Your Autoethnography with GenAI Feedback – Test GenAI as a revision partner, analyzing how it critiques and reshapes your draft.
3. Autoethnographic Self-Study + Metacognitive Report
   • Produce your main project: a self-study that connects your lived experience with GenAI to the research findings.
   • Produce your metacognitive report, which reflects on your Gen AI usage for the annotated bibliography, editing exercise, and final project.
4. Responses to Two Classmates’ Autoethnographies – Extend the scholarly dialogue by offering constructive, research-informed feedback.

Check Canvas for assignment due dates.

Introduction to the Project

In our first project, you explored writing’s enduring value by tracing its evolution from orality to digital media. Then you took a stance on how universities should respond to GenAI in student writing—should we prohibit it, permit it, or redefine academic integrity entirely? Now, in this third project, you turn inward. This autoethnographic study challenges you to analyze your own writing behavior and thought patterns when using GenAI tools. Drawing on five empirical studies and three theoretical readings on the composing process, you’ll investigate whether GenAI use reduces or enhances your thinking, creativity, effort, and sense of ownership. You’ll define a domain of expertise, generate writing tasks using two different GenAI tools, analyze the outputs critically, and compare their results. By putting your experience in conversation with published research, you’ll produce a self-study that sheds light on how GenAI is reshaping learning—and what’s worth preserving in the human writing process.

What is Autoethnography?

Most students are familiar with autobiography—a narrative that recounts personal experience. Autoethnography goes further. It is a method of critical self-study that uses your own lived experience as data but requires you to interpret that experience through empirical research and theory. While autobiography emphasizes storytelling, autoethnography emphasizes analysis. It asks you to connect your story to broader scholarly conversations, cultural patterns, and social implications. In this project, you will be using autoethnography to critically examine how GenAI shapes your cognition, creativity, and authorship—and to place those findings in dialogue with current research.

Guidelines and Evaluation

Guidelines for Writing an Annotated Bibliography

Total Length: Each annotation must be at least 150 words. There is no maximum length, but your goal is depth and precision rather than padding. At the top of your document, record the total word count for all five annotations combined.

Why 150 words?
To do the job well, each annotation needs enough space to:

• include a correct APA 7 citation (usually 30–40 words),
• summarize the study’s purpose, methods, and findings clearly (about 70–90 words),
• evaluate its credibility and relevance (at least 40–50 words), and
• connect the study to your own autoethnographic project (another 40–50 words).

Anything shorter than 150 words will almost certainly miss one of these elements.

Sources to Annotate (APA 7 citations provided in the assignment sheet):

• David et al. (2024)
• Kosmyna et al. (2025)
• Lee et al. (2025)
• Ward et al. (2024)
• Microsoft & LinkedIn (2025)

Using GenAI Tools

To draft your annotations, you are required to experiment with a GenAI tool you have not previously used (for example, Claude, Gemini, Perplexity, NotebookLM, etc.). Feed each source into the tool and ask it to generate an annotation. Then, use the evaluative criteria below to critique, revise, and refine what the tool produces. You may not submit the AI’s output unedited. Your job is to check for accuracy, depth, and relevance, and to rewrite the annotation in your own words to reflect your critical engagement with the source. Later, when you turn in your metacognitive report, you will be asked to reflect on how this process worked for you—what the GenAI tool got right, where it fell short, and how you revised its output.

Requirements for Each Annotation

1. Citation: Begin with a correct APA 7 reference. Accuracy in citation format is required.
2. Summary: Provide a clear, accurate account of the study’s purpose, methods (if applicable), and major findings or claims.
3. Evaluation: Assess the study’s credibility and relevance. What makes it significant for understanding GenAI’s effects on cognition, creativity, authorship, or literacy?
4. Connection to Your Project: Explain how this study informs your autoethnographic self-study. Does it offer a claim you will test, a framework you will apply, or a concern you will investigate through your own experience?
5. Clarity and Style: Write clearly and concisely, aiming for depth and precision rather than vague generalities.

Evaluation Criteria

• Accuracy – Faithfully represents the author’s argument; corrects any GenAI errors, omissions, or hallucinations.
• Comprehensiveness – Captures the scope of the study, not just one point.
• Quality of Annotations – Depth of explanation, clarity, and critical engagement.
• Citation Accuracy – Correct APA 7 style.
• Connection and Reflection – Demonstrates how the study informs your autoethnographic inquiry.

Criteria for Evaluating Peers’ Annotations

After completing your own annotated bibliography, you will respond to two classmates’ annotations. Each response must be at least 150 words and written in your authentic voice. Do not use GenAI for these responses. rite as a supportive colleague. Your goal is to help your peer strengthen their work. Be clear, respectful, and concrete in your feedback.

Purpose

Because this assignment encourages the use of GenAI in drafting annotations, peer feedback is especially important. Your role is to check whether your classmates’ annotations are accurate, clear, and genuinely student-written.

Requirements for Each Response

1. Accuracy – Does the annotation faithfully represent the study’s purpose, methods, and findings? Point out any omissions, errors, or overgeneralizations.
2. Clarity – Is the annotation well written and easy to follow? Note places where the writing could be more precise, concise, or coherent.
3. Authenticity – Does the annotation feel like it was written by a student engaging with the research, rather than copied from a GenAI tool? Comment on whether it shows genuine understanding and reflection.

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Guidelines for Autoethnographic Study

Your annotated bibliography gave you a foundation in what researchers are saying about how GenAI affects cognition, creativity, and authorship. Now, the autoethnographic study asks you to test those scholarly claims against your own lived experience. Rather than debating abstractly, you will put GenAI to work in a domain where you have genuine expertise and can evaluate its performance. This process will help you see, firsthand, how GenAI can act as a thought partner, when it risks leading you astray, and what it reveals about your own composing habits.

Student Learning Outcomes

By completing this autoethnographic study, you will:

• Develop critical AI literacy by evaluating GenAI outputs rigorously and reflecting on their credibility, bias, and usefulness.
• Practice metacognition by analyzing your own composing strategies and identifying when GenAI enhances or undermines your thinking.
• Gain insight into agency and authorship by assessing where you maintain control over your voice and where you risk offloading intellectual effort.
• Apply scholarly research to lived experience by using your annotated sources to interpret your interactions with GenAI tools.

Instructions

1. Word-length: Approximately 500 to 750 words, excluding citations

Writing Prompt

Conduct an autoethnographic study to evaluate how using GenAI affects your writing processes, thinking, and sense of authorship in a domain where you have real expertise. Your goal is to document and critically analyze your experience in order to better understand the real risks and benefits of using GenAI in academic and professional work, and to share those insights with your peers.

Your chosen domain should be something you know well enough to spot errors, oversimplifications, or gaps in AI output. This means selecting an area where you have authentic expertise—knowledge developed through study, professional practice, or serious sustained engagement. Possible domains include:

• Academic field of study (e.g., biology, history, computer science)
• Technical skills (e.g., coding, graphic design, video editing)
• Professional or work experience (e.g., retail management, healthcare roles, construction)
• Hobbies or crafts with real expertise (e.g., car repair, instrument performance, competitive gaming strategy)
• Sports or fitness (e.g., coaching, training plan design, game strategy)
• Leadership, teaching, or volunteer roles (e.g., mentoring, tutoring, event organizing)

This project asks you to consider how composing with GenAI changes not just what you write, but how you listen, plan, and decide what to say. Traditional models of composing emphasize listening to your inner voice—your felt sense, your purpose, your audience awareness (Moxley, 2023a, 2023b, 2023c). But when writers use GenAI, they may begin listening instead to the AI’s suggestions—risking the temptation to offload thinking, accept polished but shallow responses, and bypass deeper engagement with their own ideas.

You’ll approach this challenge in two complementary ways:

1. Scholarly grounding – You’ll examine current research on how human–AI writing partnerships affect thinking, learning, creativity, and agency. These include David et al. (2024) on study habits and academic performance, Kosmyna et al. (2025) on neurocognitive shifts in collaboration, Lee et al. (2025) on motivation and authorship, Ward et al. (2024) on the “unpleasantness of thinking,” and Microsoft & LinkedIn (2025) on emerging workplace literacies.
2. Personal experimentation – You’ll systematically document and analyze how GenAI performs in your chosen domain, evaluating whether it helps you think more, think less, or simply think differently.

Your job is to critically assess:

• How well does GenAI perform in an area where you have genuine expertise?
• Does it produce accurate, domain-appropriate content?
• How might it change the way you think, research, or write?
• Does it reduce cognitive effort—or open space for new forms of creativity and agency?

Why Does This Assignment Matter?

Recent research (David et al., 2024; Kosmyna et al., 2025; Lee et al., 2025; Ward et al., 2024; Microsoft & LinkedIn, 2025) highlights both the risks and the possibilities of GenAI. Studies document how students often offload thinking to AI tools, raising concerns about reduced cognitive effort, diminished ownership of writing, and weakened motivation. At the same time, researchers show that AI partnerships can enhance creativity, expand problem-solving capacities, and prepare students for new forms of literacy that will define success in the workplace—adaptability, collaboration, and critical AI literacy.

This assignment matters because it moves you beyond abstract debate. Rather than only reading about these issues, you will test them in your own domain of expertise, evaluating whether GenAI makes you think more, think less, or think differently. In doing so, you will develop the habits of critical AI literacy: the ability to use AI tools thoughtfully, evaluate them skeptically, and recognize both their potential and their limitations.

More broadly, writing technologies have always reshaped human cognition and literacy. Ong (2002) argued that writing restructures consciousness, while Bolter (2001) showed how new media remediate older forms. The MLA–CCCC Task Force (2024) emphasizes that GenAI requires us to rethink authorship, originality, and the literacies needed in higher education and beyond. By situating your lived experience in relation to these scholarly and professional conversations, you will contribute to an urgent dialogue about what it means to learn, create, and exercise agency in the age of AI.

Rhetorical Situation

You have been awarded a research stipend by your university to research GenAI. You are a member of a large research team that has been given a semester to research, design, and write “Navigating AI Disruption: A Guide for the University Community.” The aim of this “guide” is to define AI usage policies for the university community, and to justify those polices by rooted them in research, theory, and scholarship on GenAI.

Dr. Stacy Adams, your school’s AI Czar, has now asked you to review research on the effects of GenAI usage on learning and thinking and composing–i.e, processes related to creativity and communication. Just as Adams planned to share the best student interviews (project 2), she plans to publish the best autoethnographies in “the guide,” believing these will be of interest to the entire university community.

The Problem Space

Since ChatGPT-2’s release in 2019, generative AI has rapidly moved from experimental novelty to mainstream tool. Adoption rates are striking across both education and industry. The UK’s Higher Education Policy Institute and Kortex (2025) found that 92% of surveyed full-time students in the UK use GenAI for coursework, while the Digital Education Council (2024) reported that 86% of students across 16 countries use it for academic work. Many students expressed frustration at unclear institutional guidance on when and how AI is acceptable.

But the question isn’t just how many people use AI—it’s how they use it, and what they trust it to do. Microsoft’s 2025 Work Trend Index found 75% of knowledge workers now use AI at work—often adopting it without training or oversight. Critically, people are more likely to trust AI in domains where they lack expertise (Microsoft & LinkedIn, 2025).

Lee et al. (2025) show that even knowledge workers report thinking less critically about AI outputs when they trust the system or lack domain expertise. David et al.’s (2024) meta-analysis points to the root of this problem: thinking is effortful and often unpleasant, so people avoid it when they can. Ward et al. (2024) highlight similar risks in academic contexts, showing that students use GenAI extensively for studying and writing, often without reflecting on accuracy or ownership.

Meanwhile, Kosmyna et al. (2025) remind us that these tools don’t simply deliver answers—they reshape how people approach writing, learning, and agency over time. Educators are concerned that easy access to AI will let students offload not just the work of writing, but the work of thinking itself. Writing studies scholars argue that writing is not just a way to produce text but a mode of learning that develops reasoning, understanding, and mastery of ideas (Moxley, 2023a, 2023b, 2023c).

This assignment situates you in the middle of this real-world problem. Instead of debating AI’s value in the abstract, you’ll investigate your own use of GenAI in a domain you know well enough to evaluate carefully.

Suggested Sections for Your Autoethnography

Introduction

Writing for an audience of peers briefly introduce your domain and explain why you choose it. Use the first person.

Purpose

State your purpose clearly: to evaluate how using GenAI affects your writing, thinking, and sense of authorship in a domain where you have real expertise, and to test whether using these tools helps you plan, think, and revise more deeply or tempts you to offload cognitive effort and accept easy, polished answers. Specify that you used at least two different GenAI tools (three if you want to be ambitious) to test and compare their performance.

Review of Literature

This section should set up the analytical frame for your autoethnography—a genre move common in professional and academic writing. You are preparing readers to understand how you will analyze your own experience by showing the research you’re using as your critical lens. Synthesize insights from your eight annotated sources into a clear, cohesive narrative that shows how they contribute to an ongoing scholarly “conversation of humankind” about writing, cognition, learning, and AI. Include both empirical research on GenAI and writing-process theory that describes how writers listen to their own inner voice, purpose, and audience awareness. Explain how these readings collectively frame the risks and promises of using GenAI in writing, including the temptation to offload thinking. Use in-text citations for all quoted, paraphrased, or summarized material. You must include at least one direct quote or paraphrase from at least five of your eight required sources. Include a complete APA 7 references list at the end of your document.

Methods

Describe your domain expertise and why you are qualified to evaluate GenAI outputs in this area. Explain how you designed your iterative interactions, including your strategy for using multiple prompts, refinements, and follow-ups to mimic realistic AI use. Name the GenAI tools you selected and explain why you chose them. Describe how you documented and organized your prompts, AI outputs, and notes. You are encouraged to include links to your AI chat logs or transcripts if possible, so readers can see your iterative process and evidence clearly.

Research Methods

Report on your research methods. For instance, did you

• Engage in Socratic dialog with a GenAI tool on a topic for which you have domain knowledge. Prompt, “I’ll ask you a question, then you respond to that question and ask a question of me. Give me time to think between questions. Point me to particular urls, DOIs, and citations in APA format when referencing sources.” Talk for at least fifteen minutes.
• Conduct some sort of thought experiment (aka micro-experiment)

Analysis and Interpretation

Reflect on major course theme: how is generative AI (GenAI) reshaping creativity, authorship, composing, learning, copyright, and work—and what do these changes mean for human agency?

Systematically Analyze GenAI Output Using These Dimensions

When evaluating your GenAI outputs, focus on these overarching criteria:

• Factual Accuracy
  Are the claims correct? Are cited sources real and verifiable?
• Writing Quality
  Is the writing clear, coherent, well-organized, and appropriate for your intended audience?
• Bias and Ethical Issues
  Are there stereotypes, assumptions, or problematic framings that raise ethical concerns?
• Agency and Voice
  Does the output align with your purpose, audience, and rhetorical goals? Or do you feel pressured to accept AI’s phrasing over your own?

Classify Hallucinations and Errors Using Sun et al.’s (2024) Taxonomy

When you identify problems in the AI-generated content, use the following error categories:

• Logic Errors
  Contradictions or circular/illogical reasoning.
• Reasoning Errors
  Weak or unsupported reasoning chains.
• Mathematical Errors
  Faulty calculations or quantitative mistakes.
• Unfounded Fabrication
  Entirely invented claims or citations (e.g., fake studies, DOIs, quotes).
• Authority Errors
  Misrepresenting the authority or credibility of a source, expert, or claim (e.g., falsely citing a Wikipedia blog as a peer-reviewed source).
• Factual Errors
  Statements that are simply incorrect or misleading based on known facts.
• Text Output Errors
  Syntax issues, formatting glitches, or incomplete/dropped sentences.
• Other Issues
  Any concerns not covered above, such as inappropriate tone, style mismatches, or overuse of filler phrases.

Example Error Categorization Table (Sun et al., 2024):

Error Type	Example from AI Output	Notes / Interpretation
Logic Error		Contradictions or illogical reasoning steps.
Reasoning Error		Flawed or unsupported reasoning chains.
Mathematical Error		Incorrect calculations or quantitative mistakes.
Unfounded Fabrication		Entirely invented content, including false facts or citations.
Authority Error		Misrepresents the authority or credibility of a claim, source, or expert.
Factual Error		Incorrect statements about established facts.
Text Output Error		Formatting issues, broken syntax, incomplete text.
Other		Any issue not captured above (e.g., style mismatch, unintended tone).

Self-Reflection on Process and Agency

Conclude your autoethnography by reflecting on what this experience taught you about your own writing process, decision-making, and use of GenAI.

Your reflection should address three key themes:

1. Iteration as Dialog and Discovery

• How did the AI’s responses change as you refined your prompts?
• What did you learn about effective prompting, follow-up, and redirection?
• Where did iteration reveal hidden limitations or unexpected strengths in the tools?

2. Agency and Intellectual Effort

• Did you feel tempted to copy and paste AI-generated content without rethinking it?
• How did your domain expertise help you recognize (or overlook) hallucinations or misleading outputs?
• Did working with GenAI support your sense of voice and purpose—or undermine it?
• How did you balance your own judgment with the AI’s suggestions?

3. Broader Learning and Critical AI Literacy

• How might the risks you encountered (e.g., hallucinations, bias, offloading thinking) play out in domains where you lack expertise?
• Could you see GenAI acting as a thought partner—or did it become more of a shortcut?
• How does your self-study align with or challenge what you learned in the assigned research (e.g., David et al., 2024; Kosmyna et al., 2025; Lee et al., 2025; Ward et al., 2024)?

Use specific examples from your study and readings to support your claims. Your goal is to draw connections between your personal experience and the broader scholarly conversation about AI, learning, and human agency.

Interpretation / Discussion

In this final section, shift from describing your personal experience to drawing broader conclusions. Your goal is to interpret your results in relation to your annotated research and offer a research-informed position on GenAI’s implications for writing, learning, and agency.

Address the following:

• GenAI Credibility in Your Domain: How well did the tools perform overall? Were their strengths and limitations what you expected? How do you now evaluate their reliability, usefulness, or risk in your domain?
• Connections to Scholarly Research: How do your findings align with—or challenge—the research you reviewed? Where did your experience support the claims of studies like David et al. (2024) or Kosmyna et al. (2025)? Where did your results complicate or contradict their arguments?
• Critical Thinking and Agency: In your view, does GenAI use enhance or undermine critical thinking? Does it encourage writers to listen to their own purpose and voice—or to defer to the AI? Does it support writing as a tool for reasoning, reflection, and learning, or promote offloading effort and avoiding intellectual struggle?
• Broader Impacts: What are the implications of your findings for students, educators, or society more broadly? How might GenAI reshape writing instruction, academic integrity policies, or professional communication norms?

Conclude with a clear, well-supported position that reflects your personal insight and engages the ongoing scholarly conversation. Your goal is not just to tell your story—but to help others think more clearly about what’s at stake.

Evaluation Criteria for Autoethnographic Study

A (Excellent Work):
Clearly defines domain of expertise with strong rationale.
Demonstrates iterative use of at least two GenAI tools (three if ambitious), with well-documented prompts, refinements, and multiple turns.
Synthesizes insights from at least 5 required sources in a literature review with correct APA 7 in-text citations and reference list.
Critically analyzes AI outputs across key criteria (accuracy, writing quality, bias, ethical issues) and categorizes errors using Sun et al.’s (2024) taxonomy.
Includes at least one clear, structured visualization (table, matrix, or other format) comparing outputs and errors.
Thoughtful, research-informed reflection on how GenAI use affects thinking, effort, ownership, and agency, including whether it supports listening to one’s own purpose or encourages offloading thinking.
Organized with clear section headings; writing is clear, professional, and accessible to peers.

B (Good Work):
Defines domain of expertise with minor gaps in detail.
Shows iterative AI use with generally clear documentation.
Synthesizes at least 5 required sources with minor issues in APA formatting or integration.
Applies most required analysis criteria, including error categorization using Sun et al.’s taxonomy.
Includes structured visualization following guidelines but may lack detail or polish.
Reflects meaningfully on thinking, effort, and agency, though may miss deeper implications or connections.
Mostly clear and well-organized with minor lapses in tone or structure.

C (Satisfactory Work):
Domain of expertise is defined but vague or minimal.
AI interactions are limited, less clearly iterative, or inconsistently documented.
Synthesizes fewer than 5 required sources, or summaries are shallow or disjointed.
Applies only some required analysis criteria, with significant omissions.
Includes comparison but lacks clarity or structure; error taxonomy used inconsistently.
Reflection is superficial or overly general.
Organization or clarity issues may hinder peer understanding.

D (Poor Work):
Domain of expertise is unclear or unsupported.
Minimal evidence of AI interactions; little or no iteration documented.
Fails to summarize multiple required sources; little or no APA citation.
Analysis is missing or extremely underdeveloped; no meaningful error categorization.
No structured visualization or comparison.
Reflection is missing or unrelated to assignment goals.
Disorganized or confusing writing that fails to meet audience expectations.

F (Unacceptable Work):
Missing major sections or entire assignment.
No evidence of required AI interactions.
No meaningful engagement with required sources.
Lacks analysis, reflection, or organization appropriate for academic work.
Fails to demonstrate basic writing or citation standards.

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Guidelines for Evaluating and Revising Your Autoethnography with GenAI Feedback

Purpose

This exercise teaches you to use GenAI to strengthen your autoethnography by revising at multiple levels—global, section, paragraph, and sentence—while preserving your authorial voice and integrating research and theory. Autoethnography is grounded in personal experience but gains credibility when triangulated with scholarly sources, theory, and empirical evidence. This assignment trains you to use AI not to replace your perspective, but to test it against research, uncover gaps, and improve clarity.

Instructions

1. Review the Structured Revision – How to Revise Your Work.
2. Use at least one GenAI tool (two recommended). Provide it with the assignment prompt, grading criteria, and the revision framework. Ask for feedback one level at a time. Direct the AI to evaluate your analysis for balance between personal narrative and integration of theory/research, and to suggest ways to expand evidence and triangulate sources.
3. Work in two windows—one for AI feedback and one for writing—so you revise iteratively rather than copying AI text directly.
4. Submit two files to Canvas by the deadline: (1) your Metacognitive Revision Report and (2) the draft you submitted for AI feedback. This ensures you begin revisions early rather than waiting until the final deadline.
5. Keep a backup of all AI chat logs; if requested, you must provide them.

Submission Format (Approx. 300 words)

Word Count: [total word count for this report]

Revision Plan (~200 words)
For each revision level (global, section, paragraph, sentence), provide at least two examples:
• The AI feedback (quoted)
• The revision you made (before/after or description)
• Why you made the change, including how it supports both the personal and research dimensions of your autoethnography

Reflection (~100 words)
Discuss:
• How you felt about getting GenAI feedback
• How easy or challenging it was to interpret the AI’s advice
• How you decided what feedback to accept or reject
• What you learned about revising at both the global and local levels
• How you might use GenAI in future writing projects
• How this process affected your thinking about integrating personal narrative with research and theory

Evaluation Criteria

Top reports will:
• Engage thoughtfully with AI feedback at all four levels
• Show clear examples of how revisions strengthened both personal and research components
• Demonstrate iterative decision-making and preservation of authorial agency
• Provide a concise, honest reflection on the revision process

Reminder:
This submission is private and will only be read by the instructor. Your goal is to demonstrate that you can interpret GenAI feedback and use it to meaningfully improve your writing.

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Guidelines for Engaging with Peers’ Autoethnographies

Purpose
After you finish your own Autoethnographic Self-Study, you will read your classmates’ completed studies. Your main goal is to engage in thoughtful dialogue about their strongest insights, analytical moves, and reflections on GenAI’s role in writing. This is not formal grading or critique. Instead, you are helping one another think more deeply about how using GenAI affects thinking, composing, ownership, and human agency.

What to Do

• By the peer response deadline published in Canvas, read at least two classmates’ Autoethnographic Self-Studies carefully.
• Write a response to each classmate (at least 75 words each).
• Post your responses in the same forum or assignment space as their papers.

What to Include in Your Responses
Your goal is to engage in meaningful conversation about your peers’ work. You should:

• Highlight what you found most compelling or insightful in their analysis or reflections.
• Explain why you think their approach to evaluating GenAI outputs is strong or interesting.
• Comment on how effectively they used research (including their literature review) to frame and interpret their experience.
• Note how clearly they documented their iterative use of GenAI (multiple prompts, refinements, turns).
• Discuss how well they analyzed AI errors using Sun et al.’s taxonomy and any visualizations or tables they provided.
• Encourage them to expand or clarify their reflections on listening to their own purpose and voice versus listening to AI, and the temptation to offload thinking.
• Offer an alternative perspective or additional question that might deepen the discussion.
• Connect their ideas to course themes if you want, such as:
  • Cognitive effort and the unpleasantness of thinking
  • Writing as a mode of learning and agency
  • Student ownership of ideas
  • Critical thinking vs. offloading thinking

Reflection on the Peer Review Process (Optional but Encouraged)
After posting your peer responses, take a moment to reflect for yourself:

• Has reading your peers’ Autoethnographic Self-Studies changed how you think about the big question: What is the appropriate response for colleges and universities to the use of generative AI in writing: maintaining existing academic integrity standards, redefining them, or prohibiting AI use altogether?
• Have you gained any insights into how you might better leverage GenAI tools to be more creative, thoughtful, and effective in your own writing and communication?
• What might you do differently in your own work as a result of engaging with your peers’ approaches and reflections?

Requirements

• Two responses total (one for each peer).
• Each response should be at least 75 words.
• Responses should be respectful, specific, and aimed at continuing the conversation—not just “Good job” or “I agree.”

Reminder
The goal is not to grade your peers but to help everyone in the class learn from each other’s best ideas and analytical approaches. Use this as a chance to think critically together about the challenges and possibilities of writing with GenAI in your own academic and professional contexts.

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Guidelines for the Metacognitive Report Addressing AI Usage

Purpose

The purpose of this metacognitive report is to document and analyze how you used GenAI while conducting your autoethnographic study—how you planned, prompted, iterated, detected errors, revised, and maintained your voice and agency. This reflection complements your main report by showing how you worked, not just what you found.

Instructions

Review Metacognitive Report – AI Writing Ethics: Balancing Agency, Voice & Disclosure before you begin. Your goal is to demonstrate critical awareness of how you used GenAI to generate, evaluate, and refine your autoethnographic writing and data.

GenAI can serve multiple roles in your process, such as:
Thought Partner – brainstorming, generating hypotheses, refining research questions
Research Assistant – finding, summarizing, or cross-checking related concepts
Composing Assistant – supporting invention, drafting, revising, rereading
Editorial Assistant – improving clarity, coherence, and flow
Designer – shaping figures, tables, or visualizations
Teaching Assistant – clarifying complex concepts or modeling examples

Your metacognitive report (minimum 250 words) should include:

• Title: Metacognitive Report – Autoethnographic Study: How GenAI Shapes Thinking and Composing
• Beneath the title, left aligned, include total word count.
• Insert one table per GenAI tool used, following the format below.
• As a caption to each table provide a URL or archive link for each chat log.

Iteration and Error Analysis Tables

Use the table to document how you worked iteratively with GenAI and what you discovered about its reliability, reasoning, and your own agency.

Step in the Writing Process	Number of Iterations	Primary Purpose(s)	Detected Issue(s) (Factual Error, Fabrication, Reasoning Error, Style Drift, Redundancy)	Your Response / Revision Strategy

Guidance:

• Step in the Writing Process might include prewriting, drafting, revising, designing, or coding.
• Number of Iterations shows how many back-and-forth rounds you had before reaching a useful result.
• Detected Issue(s) draws from the error taxonomy in Sun et al. (2024): factual errors, fabrications, reasoning flaws, and style drift.
• Your Response / Revision Strategy explains what you did to address these errors—verifying, re-prompting, rewriting, or rejecting the output.

Narrative Structure

After the table(s), write a narrative that explains the story behind the data.

Overview – Identify the GenAI tools you used and why. (2–3 sentences)

Iterations as Critical Moments – Describe 2–3 stages of your process where iteration mattered most. For each, explain:

• what the AI produced initially,
• what you recognized as inaccurate or misleading,
• how you used additional prompts or revisions to correct it,
• and why those choices were important to your credibility, ethics, or self-understanding as a writer.

Reflection – Explain how your agency depended on iterating. How did multiple rounds of questioning, revision, or redirection keep you in charge? What risks did you notice (fabricated references, misleading summaries, style drift), and how did iteration help you detect and correct them?

Takeaway – What did you learn about iteration as a research method and as a habit of mind? How will this awareness shape your future composing practices?

Submission

Upload your Metacognitive Report to the same Canvas drop box as your Autoethnographic Study. Please do not use GenAI to write this reflection—let your authentic voice be heard.