Deliverables

1. Project Pitch
2. Response to Peers’ Pitches
3. Progress Report
4. Revision Exercise
5. Final Project
6. Metacognitive Report

Writing Prompt

As a member of a twelve-student undergraduate research team, your task is to contribute to Navigating AI Disruption: A Guide for the University Community. This guide is designed to help students, faculty, administrators, and employers understand and prepare for the disruptive effects of generative AI and superintelligence.

Dr. Adams is most interested in projects that focus on one clear dimension of the problem—such as authorship, composing, learning, creativity, cognition, human agency, copyright, or the job market—and develop an original, well-researched perspective.

If your work is compelling, it will be published in Navigating AI Disruption: A Guide for the University Community, a web poertal designed to educate and spark dialogue about how AI and superintelligence are transforming higher education, authorship, learning, and work—and what those changes mean for human agency, creativity, and integrity.

Scope and Format

Because the genre, media, and methodology for this project are open, you may pursue your research through a range of approaches—a literature review, qualitative or mixed-methods study, policy analysis, or creative work paired with critical reflection.

What matters most is that your project demonstrates an equivalent level of research, depth, and compositional effort to a 1,500-word analytical essay. Your work should show evidence of sustained inquiry, iteration, and revision.

Use APA 7 for in-text citations and reference entries. If your project is multimodal—such as a podcast, video, infographic, or interactive webpage—include a succinct annotated bibliography (3–5 entries) that summarizes and evaluates your key sources.

Introduction to the Project

Throughout this course, you’ve researched how generative AI is transforming writing, learning, and work—and what those transformations mean for human agency, authorship, creativity, and copyright. To help you understand these matters, you’ve read the following articles and research studies:

Aschenbrenner, L. (2024, June). Situational awareness – The decade ahead. Situational Awareness AI. https://situational-awareness.ai/wp-content/uploads/2024/06/situationalawareness.pdf

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922

Bolter, J. D. (2001). Writing space: Computers, hypertext, and the remediation of print (2nd ed.). Lawrence Erlbaum Associates.

David, L., Vassena, E., & Bijleveld, E. (2024). The unpleasantness of thinking: A meta-analytic review of the association between mental effort and negative affect. Psychological Bulletin. Advance online publication. https://www.apa.org/pubs/journals/releases/bul-bul0000443.pdf

Digital Education Council. (2024). What students want: Key results from DEC Global AI Student Survey 2024. https://www.digitaleducationcouncil.com/post/what-students-want-key-results-from-dec-global-ai-student-survey-2024

Eaton, L. (2025). Syllabi policies for AI generative tools. Google Docs.

Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19, Article 23. https://doi.org/10.1007/s40979-023-00144-1

Ellis, L., & Bindley, K. (2025, July 28). AI is wrecking an already fragile job market for college graduates. The Wall Street Journal. https://www.wsj.com/lifestyle/careers/ai-entry-level-jobs-graduates-b224d624

Freeman, J. (2025, February 25). Generative AI and the student experience: Survey findings from over 1,000 UK university students. Higher Education Policy Institute. https://www.hepi.ac.uk/wp-content/uploads/2025/02/HEPI-Kortext-Student-Generative-AI-Survey-2025.pdf

Goldman Sachs Research. (2023, April 5). Generative AI could raise global GDP by 7%. Goldman Sachs. https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent

Hsu, H. (2025, June 30). What happens after A.I. destroys college writing? The New Yorker. https://www.newyorker.com/magazine/2025/07/07/the-end-of-the-english-paper

Kokotajlo, D., Alexander, S., Larsen, T., Lifland, E., & Dean, R. (2025, April 3). AI 2027. AI Futures Project. https://ai-2027.com

Kosmyna, N., Heiss, L., Baker, J., Hassman, L., Shapiro, J., & Benjamins, I. (2025). Longitudinal study on AI-assisted writing: Cognitive adaptation and agency in human–AI co-creation (arXiv preprint No. 2506.08872v1). arXiv. https://arxiv.org/abs/2506.08872

Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. In CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery. https://doi.org/10.1145/3706598.3713778

Mayer, H., Yee, L., Chui, M., & Roberts, R. (2025, January 11). Superagency in the workplace: Empowering people to unlock AI’s full potential at work. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

McKinsey & Company. (2023, July 26). Generative AI and the future of work in America. McKinsey Global Institute. https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america

Microsoft & LinkedIn. (2024, May 8). 2024 Work Trend Index annual report: AI at work is here. Now comes the hard part. Microsoft. https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part

Microsoft. (2025, April 23). 2025: The year the frontier firm is born. Microsoft. https://www.microsoft.com/en-us/worklab/work-trend-index/2025-the-year-the-frontier-firm-is-born

Moxley, J. M. (2023a). The writing process – Research on composing. Writing Commons. https://writingcommons.org/section/writing-process/

Moxley, J. M. (2023b). The embodied writing process – How to tap your creative potential. Writing Commons. https://writingcommons.org/section/writing-process/the-secret-hidden-writing-process/

Moxley, J. M. (2023c). The ultimate blueprint: A research-driven deep dive into the steps of the writing process. Writing Commons. https://writingcommons.org/section/writing-process/writing-process-steps/

Moxley, J. M. (2025, January 13). Universities must compel students to detail how they use AI in assignments. Times Higher Education.

Ong, W. J. (2002). Orality and literacy: The technologizing of the word (2nd ed.). Routledge. https://monoskop.org/images/d/db/Ong_Walter_J_Orality_and_Literacy_2nd_ed.pdf

Sano-Franchini, J. (2025, April 10). Timely, (Un)Disciplinary, and Solutions-Oriented: Remembering and enacting abundance in these times when we just have to keep going [Conference address transcript]. Conference on College Composition and Communication. https://docs.google.com/document/d/1d-LaO7oMoWFBcXgjoyylD0FRqrB1jQZMq9NttPfZOKY/edit

Sano-Franchini, J., McIntyre, M., & Fernandes, M. (2024). Refusing GenAI in writing studies: A quickstart guide. Refusing Generative AI in Writing Studies. https://refusinggenai.wordpress.com

Suleyman, M. (2023). The coming wave: Technology, power, and the twenty-first century’s greatest dilemma. Crown.

Tomlinson, K., Jaffe, S., Wang, W., Counts, S., & Suri, S. (2025, July 22). Working with AI: Measuring the occupational implications of generative AI. arXiv. https://arxiv.org/abs/2507.07935

Wang, H., Dang, A., Wu, Z., & Mac, S. (2024). Generative AI in higher education: Seeing ChatGPT through universities’ policies, resources, and guidelines. Computers and Education: Artificial Intelligence, 5, Article 100326. https://doi.org/10.1016/j.caeai.2024.100326

Ward, B., Bhati, D., Neha, F., & Guercio, A. (2024). Analyzing the impact of AI tools on student study habits and academic performance (arXiv preprint No. 2412.02166). arXiv. https://arxiv.org/abs/2412.02166

Together, these readings provided the intellectual foundation for the four major creative challenges you’ve completed—each addressing a different facet of AI disruption in higher education and society.

In Presentation — Why Human Writing Matters in the Age of AI, you drew on Ong and Bolter to examine writing as a technology that reshapes consciousness, thinking, and power. In White Paper — Generative AI, Ethics, and Academic Policy Recommendations, you evaluated how universities should respond to AI’s integration into student work, engaging Eaton’s post-plagiarism framework and Sano-Franchini’s ethical critique of GenAI. In Autoethnographic Study — The Effects of GenAI Use on Thinking, Composing, and Ownership, you investigated your own writing process, testing empirical claims about effort, creativity, and authorship. In Literature Review — What Is the Future of Work in the Age of Superintelligence?, you synthesized workplace research (McKinsey, Microsoft, Goldman Sachs) to consider how AGI and ASI may require humans to “level up” in creativity, adaptability, and strategic reasoning.

Possible Topics

Exactly what topic you research—and how you contribute to Navigating AI Disruption: A Guide for the University Community—is up to you. You may extend earlier work or pursue a new line of inquiry. Your project might take the form of a literature review, a mixed-methods or qualitative study, or a creative production (song, poem, short story, video, or digital artwork) accompanied by a critical reflection. Whatever your approach, your goal is to produce an original, research-informed contribution that deepens understanding of how generative AI reshapes creativity, authorship, composing, learning, copyright, and work—and what these transformations mean for human agency.

Topics we’ve already explored in this course

1. Leveling up to compete with AI – Argue that students must actively develop higher-order skills — creativity, strategic thinking, adaptability — to compete and collaborate effectively in an AI-saturated job market. (Connects to: Literature Review on Future of Work)
2. How GenAI alters composing processes – Examine how GenAI changes some aspect of composing, such as invention, drafting, revision, or collaboration. Question and whether these shifts support or erode human agency. (Connects to: Presentation + Autoethnography)
3. Critical thinking, creativity, and self-expression – Investigate how GenAI use in coursework affects cognitive effort, originality, and voice — testing claims from scholarship against your own and others’ experiences. (Connects to: Autoethnography)
4. Authorship, ownership, and copyright – Analyze what happens to the human voice and creative control when corporate AI models train on massive datasets scraped without consent. (Connects to: White Paper)
5. Academic integrity and plagiarism norms – Explore how GenAI challenges plagiarism definitions and develop guidance for ethical use grounded in scholarly discourse. (Connects to: White Paper)
6. University policies in practice – Compare how institutions like Ohio State embrace AI while others prohibit its use, and analyze the effects on student learning and agency. (Connects to: White Paper)
7. Workplace readiness in specific fields – Research how GenAI is being used in a given discipline and outline strategies for students to remain competitive. (Connects to: Literature Review)
8. Discipline-specific innovation – Document creative, ethical ways faculty and students in a given field use GenAI for problem-solving, research, and design. (Connects to: Any earlier project)

New or emerging areas for investigation

• Creating with AI: Autoethnography and Reflection on Human Agency – Build on your earlier autoethnographic study by using GenAI to compose something new—a song, poem, short story, video, or artwork. Document your creative process, record your prompts and revisions, and reflect on how co-creating with AI shaped your sense of authorship, voice, and agency.
• Designing a Qualitative or Mixed-Methods Study of AI Use – Conduct interviews, focus groups, or short surveys with students, faculty, or professionals to learn how they are incorporating GenAI into their work and learning. Analyze patterns in their attitudes toward creativity, ethics, or efficiency, and compare them with themes from scholarly research.
• Investigating the Human–AI Interface – Choose a specific domain—teaching, journalism, art, therapy, or coding—and study how people collaborate with AI in that context. Combine participant observation, reflective journaling, and small-scale data analysis to evaluate how these tools change roles, expertise, and communication.
• Exploring AGI and ASI Scenarios through Research or Storytelling – Examine scholarly and speculative writing about artificial general and superintelligence. You might produce a research-based scenario analysis, short fiction, or video essay that dramatizes how advanced AI could transform cognition, creativity, or moral responsibility.
• AI and Human Relationships – Study how AI companions (e.g., Replika, Pi, Character.ai) reshape empathy, intimacy, or social connection. Use qualitative methods—interviews, self-tracking, journaling—or creative genres such as dialogue, poetry, or memoir to explore emotional and ethical dimensions of digital companionship.
• Economic and Ethical Positioning in the AI Revolution – Investigate how students, educators, or workers can ethically and strategically participate in the emerging AI economy. Combine secondary research on new AI industries with short interviews or case studies of people adapting to these changes.
• Environmental Impact of AI – Research the ecological costs of AI training and deployment—energy, data storage, and water usage—and propose sustainable or “green AI” strategies in a visual report or infographic.
• Concentration of Wealth and Power – Critique how AI centralizes economic and cultural influence within a few corporations. Analyze policy proposals for open-source, decentralized, or community-based AI governance.
• AI Hallucination and Misinformation Ecosystems – Draw on studies like Sun et al. (2024) to categorize and test AI errors. Conduct a small experiment in which you identify, classify, and visualize distortions in AI-generated content, or design a creative piece that dramatizes the consequences of misinformation.
• Bias, Equity, and Inclusion – Investigate how GenAI reproduces or resists bias in language and imagery. Use prompt-based testing, corpus analysis, or interviews to evaluate fairness and representation, and suggest guidelines for inclusive AI use.
• Global Inequalities in AI Adoption – Compare how nations regulate and integrate AI into education or industry. Use international case studies or interviews with students from different backgrounds to explore access and opportunity gaps.
• Cultural Homogenization vs. Diversification – Experiment with training or prompting AI using culturally specific material to see whether it amplifies or erases local voices. Pair your creative results with a short reflection on linguistic or aesthetic diversity.
• AI in Governance and Civic Life – Research how AI shapes democratic participation, public decision-making, and surveillance. Conduct a policy analysis or develop a multimedia explainer illustrating ethical challenges and citizen responses.
• Human–AI Co-Creativity in the Arts – Collaborate with GenAI to produce an original composition, image series, or script, and then write a critical commentary explaining how the partnership expanded or constrained your imagination.

Why Does This Assignment Matter?

Humanity stands at the crest of a technological wave, one that is surging with unprecedented force, reshaping the very fabric of our existence (Suleyman 2024). Writing, one of our oldest technologies, is a mirror of our aspirations and a tool for our ingenuity. From the etchings on cave walls to the quill’s flourish, from the clatter of typewriters to the internet, each innovation has redefined who we are, what we believe possible, and how we forge meaning.

Generative AI (GenAI) heralds a new chapter in this saga—a wave of such magnitude that it threatens to sweep away cherished notions of authorship, learning, teachingk and human agency itself.

This wave is not a distant rumble; it is breaking upon us now. Gen AI is a force multiplier, a technology that amplifies human capability while simultaneously exposing our vulnerabilities. It promises to streamline our lives, to craft prose with a flick of a prompt, to automate tasks once thought uniquely human.

Yet, its proliferation brings turmoil. In workplaces, the wave is already crashing: projections suggest

• By 2030, 30% of jobs could be automated by artificial general intelligence (AGI). (McKinsey & Company, 2025).
  In education, the disruption is no less profound. Faculty reel as their works—books, articles, the very words of their scholarship—are ingested without consent, commodified into the churning engines of large language models (LLMs). Students, meanwhile, wield GenAI not as a thought partner but as a shortcut, a means to bypass the labor of learning. “I wasn’t tryin’ to read that,” one student declared, demanding concise bullet points over engagement with ideas (Hsu, 2025). Another, when pressed on whether this was cheating, responded with chilling candor: “Of course. Are you kidding me?”

This is the paradox of the coming wave: it empowers and imperils in equal measure. The same technology that could revolutionize education—offering personalized learning, automating rote tasks, and democratizing knowledge—also risks eroding the foundations of intellectual integrity.

Understandably, faculty advocate for resistance.

Some, like Professor Jennifer Sano-Franchini call for resistance. In her 2025 address to the Conference on College Composition and Communication, Sano-Franchini highlights GenAI’s flaws—its tendency to hallucinate, fabricate sources, and homogenize language and deeper misalignments with the values of the humanities. Sano-Franchini argues GenAI is “inherently aligned with white supremacist and eugenic ideologies,” urging the discipline to reject their wholesale adoption. Her call, echoed in a related blog post, advocates for opting out, for refusing to center a technology that undermines our ethical commitments.

Yet resistance alone cannot hold back this tide. The wave is not a choice; it is a force. Microsoft’s 2025 Work Trend Index reveals that 78% of business leaders are already creating AI-specific roles, with 46% automating entire workflows (Microsoft, 2025). McKinsey’s report on “Superagency” projects that 92% of companies will increase AI investments over the next three years (Mayer et al., 2025). These are not predictions but realities unfolding now, driven by the inexorable logic of technological evolution: what can be built will be built. To deny this is to succumb to “pessimism aversion”—the human tendency to shy away from uncomfortable truths, to cling to the hope that the wave might somehow spare us.

But the wave will not spare us. It will reshape education, work, and society itself, bringing both unprecedented prosperity and existential risks. The question is not whether we can stop it—history shows us that no technology, from fire to the internet, has ever been fully contained. The question is whether we can channel it, whether we can forge a narrow path between catastrophe and dystopia. This is the containment problem, the defining challenge of our age. It demands that we confront the risks head-on: the potential for AI to amplify cheating, to erode trust in education, to displace millions of workers. It requires us to rethink how we teach, how we work, how we govern. Containment is not about halting progress but about ensuring that our technologies serve humanity, not supplant it.

For educators, this means embracing AI as a partner while safeguarding the sanctity of learning. It means teaching students to iterate with AI, to use it as a tool for exploration rather than a crutch for evasion. For leaders, it means investing in ethical frameworks, in audits and oversight, to ensure that AI’s power is wielded responsibly. For all of us, it means overcoming our aversion to the hard truths of this moment. The wave is coming—indeed, it is here. If we care for humanity, we must act with urgency, with clarity, and with unwavering resolve to shape its course.

The Problem Space

A tidal wave of generative AI is surging through higher education, reshaping the essence of learning, writing, and work with relentless force. As Mustafa Suleyman warns in The Coming Wave (2023), such technologies amplify human potential but risk eroding core values if left uncontained. Across campuses, 95% of academic leaders express alarm that generative AI threatens academic integrity, with 92% fearing it undermines the deep learning that defines a university’s mission (Watson & Rainie, 2025, https://www.aacu.org/research/leading-through-disruption). Nearly 60% report a surge in cheating, as students copy and paste AI-generated content without the word-by-word, line-by-line iteration essential for intellectual growth (Watson & Rainie, 2025). Faculty struggle to detect such content, and many institutions lack clear policies and resources to guide responsible AI integration, leaving both students and instructors adrift (Wang, H., Dang, A., Wu, Z., & Mac, S., 2024, https://doi.org/10.1016/j.caeai.2024.100326).

This crisis demands urgent action. University leadership emphasizes that students who treat AI as a shortcut—bypassing iterative engagement—cheat themselves of the critical thinking and agency needed for personal and professional growth, a lesson best conveyed through the stories of their peers. Faculty face ethical dilemmas as their work is ingested by large language models without consent, threatening authorship (Wang et al., 2024). With 30% of jobs poised for automation by 2030 (McKinsey & Company, 2025), the university must prepare students for an AI-driven future while safeguarding the creativity and integrity at its core. The challenge is stark: how do we channel this wave to empower learning and authorship without letting it drown the values that define higher education?

Guidelines and Evaluation

Guidelines for a Project Pitch

Prepare a one- to two-page memo (500–750 words) proposing your final project for Navigating AI Disruption: A Guide for the University Community. You are responding to the rhetorical situation outlined in the assignment prompt: Dr. Stacy Adams, your university’s AI Czar, has asked each member of the research team to propose an original, evidence-based contribution to the guide. Your memo should persuade Dr. Adams and your peers that your project is relevant, valuable, and feasible.

At the top of your memo, provide a working title for your project and, directly below it, your word count in parentheses.

Your memo should include the following headings:

Focus – Define the specific problem, opportunity, or question you will address. Explain how it connects to the course’s central theme: how generative AI reshapes authorship, composing, learning, creativity, and human agency. Identify your intended genre (e.g., research article, creative reflection, policy brief, song, poem, short story, screenplay, novel excerpt, video essay, or other digital composition) and your chosen medium (text, audio, video, interactive, or multimodal).

Purpose – State your communicative goal. What do you want your audience to understand, feel, or do after engaging with your work? What contribution will your project make to Navigating AI Disruption?

Audience – Identify your primary and secondary audiences. Consider who will benefit most from your work and how you will adapt your tone, medium, and style to reach them effectively.

Significance – Explain why your topic matters now and why it’s worth investigating or expressing. Address the stakes for the university community—how it affects students, faculty, administrators, or employers—and situate your work within current conversations about AI. Support your rationale with at least three credible sources in APA 7 (course readings are acceptable, but include at least one new source).
If your project is creative or experimental, you may instead describe how you’re engaging with new AI tools, datasets, or artistic precedents that inform your process.

Methods – Explain how you will explore your topic.

• If your project is research-based, describe your methods (textual analysis, interviews, surveys, content analysis, mixed methods, etc.).
• If your project is creative, describe your process: what GenAI tools, prompts, or artistic strategies you will experiment with, how you will iterate, and how you will reflect on authorship, originality, or agency.
• In either case, explain how your approach supports your purpose and aligns with your genre and audience.

Schedule – Provide a simple timeline outlining key steps from now to the final due date.

Rubric for Evaluating the Pitch

Criterion	100%	85%	75% or Below
Clarity & Organization	Polished, focused, easy to follow; clear headings; professional tone; within 500–750 words.	Mostly clear and organized; minor lapses in flow or formatting.	Unclear or disorganized; missing sections or sloppy presentation.
Responsiveness	Addresses all sections (Focus, Purpose, Audience, Significance, Methods, Schedule); defines genre, medium, and contribution.	Covers most sections but one underdeveloped or missing.	Incomplete or off-topic; several elements missing.
Significance & Research	Explains why topic matters; connects to broader stakes; integrates ≥3 credible sources or AI tool references (APA 7).	Shows relevance but limited depth or uneven source use.	Minimal rationale; lacks credible evidence.
Feasibility & Specificity	Realistic, well-defined plan with clear methods and timeline.	Plausible but needs more detail or milestones.	Vague or unrealistic; unclear methods or timeline.

Guidelines for Evaluating Peers’ Pitches

After reading all classmates’ project pitches, rank the top ten proposals—from most promising (1) to least promising (10).

Instructions

1. Copy and number the list of ten titles in your response post.
2. Write one sentence of justification next to each title.
3. Base your reasoning on the following criteria:
   • Clarity and Feasibility – Is the project clearly defined and realistic for the final assignment?
   • Originality and Significance – Does the idea offer a fresh perspective or address a meaningful issue?
   • Potential Impact and Contribution – Could this project make a valuable addition to Navigating AI Disruption?
   • Alignment with the Rhetorical Situation – Does the proposal address Dr. Adams’s goals, the guide’s purpose, and the intended audience?
4. Keep your tone constructive and professional. Evaluate ideas, not individuals.
5. Your rankings will help identify which projects have the most traction with the research team.

Rubric for Peer Responses

This assignment will be graded “complete” when students follow the correct format. Example:

1. “AI and the Studio: Co-Creating Music with Machines” – Clear, feasible, and creatively explores agency in artistic collaboration.
2. “Deepfake Literacy in the Classroom” – Highly significant and relevant to the guide’s goal of AI education.

Guidelines for Progress Report

Purpose
This exercise gives you the opportunity to deepen and extend your project pitch. You will layer new research, examples, and reflection onto your earlier work to clarify how your project contributes to Navigating AI Disruption: A Guide for the University Community. Use this step to refine your focus, expand your evidence, and define the creative or investigative path you will follow.

Title and Word Count
Place your project title at the top of the document. Include your total word count beneath it. References and citations do not count toward the word limit.

Introduction
Write 1–2 paragraphs expanding on your pitch. Define the problem, opportunity, or question you are addressing and explain why it matters to the university community (students, faculty, administrators, employers). Explicitly connect your topic to the rhetorical situation described in the final project guidelines—Dr. Stacy Adams’s role as the university’s AI Czar and her goal of using student and faculty perspectives to shape institutional AI policy. You may draw on earlier course readings and creative challenges, but if your topic requires additional material, identify where you plan to find it.

Model Texts or Examples
Identify at least one text, project, or production you are using as a model for your work. Investigators across disciplines rely on models to guide and inspire their design choices—qualitative researchers analyze previous case studies to understand framing and representation; autoethnographers study narrative structure and tone; artists and designers study prior works to learn how form and style shape audience response. You may list more than one model if you are drawing from multiple sources or mixing genres. Provide APA 7 references (with URLs when available). In 2–3 sentences for each, explain what you find effective about the model and how it informs your approach.

Research and Inquiry Update
Describe the work you have conducted to deepen your understanding of your topic, genre, medium, and methods. Depending on your project, this may include scholarly or professional sources, interviews, creative experiments, or exploration of AI tools and datasets. Use APA 7 format for any published sources you cite and note which materials build on earlier coursework and which are new discoveries.

Genre, Medium, and Methods
Briefly restate your intended genre (e.g., research article, policy brief, creative reflection, song, poem, short story, screenplay, novel excerpt, video essay, infographic, podcast) and medium (text, audio, video, multimodal). Describe your planned method—textual, empirical, autoethnographic, creative, or mixed—and note any adjustments since your pitch. Clarify how your approach will help you explore the project’s clarity and feasibility, originality and significance, potential impact, and alignment with the rhetorical situation.

Work Still Needed and Schedule
Identify what remains to be done—additional research, writing, design, data collection, revision, or collaboration—and provide a short schedule of the major steps you will complete between now and the final due date. Be realistic and specific.

References
List all sources cited in APA 7 format. References do not count toward the word limit.

Evaluation Rubric: Progress Report

Criterion	100%	85%	75% or Below
Depth & Development	Expands substantially on the pitch; shows clear layering of new insight, evidence, and reflection.	Extends pitch but with limited elaboration or new depth.	Minimal development; repeats earlier work.
Engagement with Models & Inquiry	Effectively analyzes model texts and documents meaningful inquiry into topic, genre, medium, and methods.	References models or inquiry but with limited explanation or specificity.	Models or inquiry missing or superficial.
Clarity & Coherence	Writing is clear, focused, and well-organized; tone professional; APA 7 citations accurate; includes title and word count.	Generally clear but may have minor lapses in organization, tone, or citation.	Unclear, disorganized, or careless with tone, format, or citation.
Rhetorical Awareness & Planning	Demonstrates alignment with the guide’s goals, audience, and institutional context; provides realistic plan for remaining work.	Acknowledges rhetorical situation but without depth or complete schedule.	Little awareness of rhetorical context; missing or vague plan for next steps.

Guidelines for Revision Exercise — Revising Your Final Project with GenAI

Purpose
Use two different GenAI tools to evaluate and refine your nearly complete draft. You’ll test responsiveness to the assignment and the final rubric, compare perspectives, and prepare for publication in Navigating AI Disruption: A Guide for the University Community.

Required Reading (Before You Draft Prompts)
Structured Revision – Higher-Order vs. Lower-Order Concerns (course handout). Use this framework to request feedback and to organize your reflection.

Working Title and Word Count
At the top of your draft, include your project title and total word count in parentheses.

Draft Submission
Upload a substantial draft that includes your introduction, main sections, and a preliminary conclusion. It must be developed enough to support meaningful HOC/LOC feedback.
This draft itself is not graded for quality; however, points will be deducted if it is missing or too incomplete to review.

Use Two GenAI Tools
Choose two distinct tools (e.g., ChatGPT, Claude, Gemini, Copilot, NotebookLM, Grok). Ask each tool to assess your draft from two perspectives:

1. Responsiveness to the Assignment – Does the draft fulfill the purpose, audience, and rhetorical situation for Navigating AI Disruption: A Guide for the University Community?
2. Evaluation Against the Final Project Rubric – Ask the tool to assess your project using these four categories: clarity and feasibility; originality and significance; impact and contribution; rhetorical alignment.

Also request targeted feedback on:

• Higher-Order Concerns (HOCs): clarity, coherence, organization, depth of analysis, and rhetorical alignment.
• Lower-Order Concerns (LOCs): style, grammar, tone, and mechanics.

Compare and Analyze Feedback
Note where the tools converge or conflict. Decide what to adopt, adapt, or reject—and why.

Reflection (400–500 words)
Upload a separate reflection document. At the top, include your reflection title, word count, and URLs to the two chatlogs.
Use these headings to organize your reflection:

• What you learned about your draft – Key insights, separated into HOCs and LOCs.
• Feedback you accepted – 2–3 changes you made and why they strengthened your project.
• Feedback you rejected or modified – 2–3 suggestions you declined and why.
• Impact on your process – What using two tools revealed about strengths, gaps, or biases.
• Next steps – The 1–2 priorities before final submission.

Submission Instructions

1. Upload your nearly complete draft (with APA 7 citations included).
2. Upload your reflection (400–500 words) with title, word count, and chatlog URLs at the top.

Evaluation Rubric: Revising Your Final Project with GenAI

Criterion	100%	85%	75% or Below
Prompt Responsiveness	Uses two distinct GenAI tools; prompts address assignment responsiveness and final rubric categories; both chatlog URLs included and accessible.	Uses two tools but one area underdeveloped (e.g., weak prompts or incomplete chatlogs).	Uses one tool only or omits chatlogs; prompts fail to address assignment or rubric.
Draft Submission (Completion)	Substantial draft uploaded with introduction, main sections, and preliminary conclusion; sufficient for HOC/LOC review.	Draft uploaded but missing a major section or analysis depth.	Draft missing or too incomplete for meaningful feedback.
Reflection Quality	400–500 words; separates HOCs/LOCs; explains accepted and rejected changes with reasons; outlines clear next steps.	Reflection complete but lacks precision or depth; next steps vague.	Reflection superficial, incomplete, or missing required elements.
Clarity & Professionalism	Documents are well-organized, concise, and polished; headings, word counts, and tone professional; APA in draft.	Generally clear with minor lapses in tone or mechanics.	Unclear or disorganized; missing headings or word counts; careless presentation.

Guidelines for the Final Project

Please review the Writing Prompt and Introduction to the Final Project above. Those sections define the rhetorical situation, genre options, and expectations for research and originality. Then review the Final Project Rubric below, which will be used to evaluate your work.

Final Project Rubric

This rubric applies to all approved project types—traditional scholarly studies, autoethnographies, interviews or surveys, creative compositions, and multimodal works. Projects will be evaluated on rhetorical and intellectual quality rather than on specific method. Evidence may take the form of textual analysis, self-observation, participant data, creative experimentation, or design research.

Category	Description
Clarity & Feasibility	The project presents a coherent, well-organized argument or design. The purpose and scope are realistic and appropriate for the chosen genre and method. Evidence or supporting material—whether textual, empirical, or creative—is clearly presented and contextualized.
Originality & Significance	Demonstrates creative or analytical originality. Advances understanding of how GenAI reshapes creativity, authorship, learning, work, or human agency. Significance may stem from scholarly insight, personal reflection, empirical findings, or innovative design.
Impact & Contribution	Makes a meaningful contribution to Navigating AI Disruption: A Guide for the University Community. Addresses issues of practical or theoretical importance and engages the intended audience (students, faculty, administrators, or employers).
Rhetorical Alignment	Responds effectively to the rhetorical situation—Dr. Adams’s goals, the guide’s purpose, and the intended audience. Tone, genre, medium, and ethical choices are appropriate. Integrates sources, data, or creative elements with integrity and transparency.