Deliverables

Introduction to the Module

This module asks you to investigate how AI is reshaping career readiness in a field that interests you. The research question driving your work:

In a career field that interests you, which tasks are most exposed to AI automation—and which human competencies will matter more as a result?

Some business leaders believe AI will lead to widespread unemployment:

• In February 2026, Jack Dorsey cut 40% of Block’s workforce—over 4,000 jobs—and predicted most companies will do the same within a year. “A significantly smaller team, using the tools we’re building, can do more and do it better,” he told shareholders (CNN).
• Ford CEO Jim Farley warns that “artificial intelligence is going to replace literally half of all white-collar workers in the U.S.” (Fortune).
• Anthropic CEO Dario Amodei predicts half of all entry-level white-collar jobs could vanish in the next five years (Axios).

There is considerable empirical evidence suggesting that generative AI is eliminating some jobs:

• Entry-level roles traditionally used as training grounds—junior analyst reports, basic content creation, financial modeling, market research summaries—are now being replaced by AI (Tomlinson et al., 2025).
• An Anthropic study of 200,000 real-world AI interactions found that sales, customer service, legal support, and administrative work face the highest automation rates (Massenkoff & McCrory, 2026).

AI Literacy is becoming a required workforce competency:

• Microsoft’s 2024 Work Trend Index confirms this shift: 66% of leaders say they wouldn’t hire someone without AI skills, and 71% would choose a less experienced candidate with AI skills over a more experienced one without (Microsoft & LinkedIn, 2024).

Sam Altman’s tactical advice for students: “Get really good at using AI tools… this is the new version of [learning to code]” (Fortune).

The truth is that no one knows exactly how AI will transform the workplace.

Right now there does seem to be some evidence to suggest that professionals who learn to work with AI are pulling ahead. A survey of 1,190 professional writers found that those who use AI extensively earn a median of $120,100 annually—64% more than the $73,400 earned by non-users (Gotham Ghostwriters & WOBS LLC, 2025). Three out of four knowledge workers already use AI at work, and nearly half started in the last six months (Microsoft & LinkedIn, 2024).

In a March 2026 New York Times column titled “I Saw Something New in San Francisco,” Ezra Klein explores how Silicon Valley is adopting active, agentic AI to gain a competitive advantage through accelerated workflows. Klein reports that people on the frontier of AI believe “the winners and losers will be determined, in part, by speed of adoption. The advantages of working atop an army of A.I. assistants and coders will compound over time and to begin that process now is to launch yourself far ahead of your competition later.” The question isn’t whether AI will reshape your career field—it’s whether you’ll be positioned to benefit.

That’s why this module asks you to investigate:

In a career field that interests you, which tasks are most exposed to AI automation—and which human competencies will matter more as a result?

You begin by analyzing the workplace competencies employers have traditionally expected of undergraduates. Next, you research the workforce competencies that are evolving in response to the rise of AI—competencies that will be valued in workplaces where humans and AI systems collaborate. Then you extend your inquiry by conducting original research—interviewing subject-matter experts in a career field that interests you to learn which competencies they believe students should cultivate now to be the boss of the bots.

Guidelines for Original Research – Career Competencies in the AI Frontier.

Choose one of the following methods or speak with me about alternative approaches.

Method A: SME Interviews

Interview two subject-matter experts (SMEs) who have situated knowledge of your career field—people who see daily workflow shifts firsthand. Strong candidates include professionals with 3+ years of experience, HR managers or department leads, recent alumni, faculty, or graduate students in the field.

Planning Your Questions

Focus on tasks vs. roles.

• Weak question: “Will AI replace your job?”
• Strong question: “Which tasks in your weekly workflow have been offloaded to AI tools, and what higher-value work has filled that gap?”

Draft 5–7 questions that ask about specific competencies, concrete examples, and observable changes. Or instance, you could ask.

1. How has AI changed the day-to-day work in your field over the past two years?
2. Which entry-level tasks are most at risk of automation in the next three to five years?
3. Which competencies will remain distinctly human-centered, and why?
4. Can you share a concrete example from your professional experience?
5. What advice would you give students preparing to enter this field in the next 2–3 years?

Professional Outreach

Request a 10 to 15 minute interview.

Conducting the Interview

1. Verify consent: You can tell them this is a school project; it’s not professional research that will be published.
2. Ask your respondent if you can use their name and pic. If they prefer to be anonymous, create a persona that matches theirs. Please note, however, that that move may undermine your authority and voice.
3. You do not need to record the session. Taking notes is fine.
4. Ask them for concrete examples regarding how AI is altering their workplace. If they use jargon (“AI handles routine documentation”), probe: “Can you walk me through what that looks like in a typical workday?”
5. Use the silence technique: After they finish an answer, wait three seconds. Experts often fill that silence with their most nuanced insights.
6. Listen for tension. If their perspective differs from your literature review, that’s not a problem—that’s where your best analysis will happen.

Drafting an Interview Summary

Immediately after the interview, write your notes to the respondent’s questions. Be sure to explain who the Subject Matter Expert is. What is your expertise and years in the field?Include a photo or an AI-generated likeness labeled: “AI-generated likeness; not an actual photograph.” Include at least one direct quote from each interview that captures a distinctive insight. Later, these notes will be invaluable when you try to bring in what you learned from them into your recommendation report.

Recommended Readings

• Working With People (Clary-Lemon et al., 2022)

Method B: Survey or Canvassing

Design a survey to professionals in your career field (minimum 10 respondents). Use a mix of Likert-scale items and open-ended questions to gather both quantitative patterns and qualitative insights. For more guidance, see Surveys.

Method C: Textual Analysis –

Engage in scholarly research to identify patterns in competency requirements. This method works well if you cannot secure interviews or survey respondents. Possible sources include job postings (analyze 15–20 recent postings in your field), professional forums or subreddits, industry reports or white papers, or LinkedIn posts from professionals discussing AI’s impact on their work.

Recommended Readings

1. Mixed Research Methods
   • Corpus Linguistic Analysis – A Bird’s Eye View of Writing
2. Rhetorical Analysis
3. Working With Words (Clary-Lemon et al., 2022)

A Note on Research Ethics

Empirical research involving human subjects that is intended for publication is subject to Institutional Review Board (IRB) approval. For this course assignment, IRB approval is not required because your research is conducted for educational purposes and will not be published or disseminated beyond the classroom. However, you should still practice ethical research conduct: explain why you are conducting the research and what you hope to learn. Ensure your SME that they can decline or withdraw from the study at any time.

Guidelines and Evaluation

Guidelines for Literature Review + Visualizations

This deliverable asks you to synthesize research on two interconnected threads and produce two information visualizations. Keep the module’s central question in view as you work:

In a career field that interests you, which tasks are most exposed to AI automation — and which human competencies will matter more as a result?

Your literature review is not a summary of sources. It is the first stage of your argument. The two visualizations you produce here — the Pre-AI Foundation and the Frontier Model — will carry into your final Recommendation Report as the backbone of your analysis. Build them with that destination in mind.

Visualization 1: The Pre-AI Foundation (The Legacy Model)

Create a visualization that synthesizes the competencies employers, educators, and researchers identified as essential for early-career professionals, prior to the rise of AI. Your visualization should draw from at least 3 of the assigned sources and include APA 7 in-text citations directly on the visual — a footer citation at the bottom of the visual is sufficient.

1. National Association of Colleges and Employers. (2024, April). Career readiness competencies. https://naceweb.org/docs/default-source/default-document-library/2024/resources/nace-career-readiness-competencies-revised-apr-2024.pdf
2. National Association of Colleges and Employers. (2025, January). Job outlook 2025. https://www.naceweb.org/docs/default-source/default-document-library/2025/publication/research-report/2025-nace-job-outlook-jan-2025.pdf
3. National Research Council. (2012). Education for life and work: Developing transferable knowledge and skills in the 21st century. https://doi.org/10.17226/13398
4. Oliveri, M. E., Lawless, R., & Molloy, H. (2017). A literature review on collaborative problem solving for college and workforce readiness. https://doi.org/10.1002/ets2.12133

Accompany your visualization with a 250 word synthesis of the NACE, NRC, and Oliveri et al. (2017) readings. This should represent the consensus on what a graduate needed to be “career ready” before the widespread “infusion” of Generative AI

Visualization 2: The Frontier Model – The “Boss of the Bots” Challenge

Create a visualization of the career readiness competencies that college graduates will need to be a boss of the bots. Your visual should elucidate the emerging competencies that researchers believe college graduates need to possess in order to be competitive in the job market. Draw from at least 3 of the assigned sources and include APA 7 in-text citations directly on the visual — a footer citation at the bottom of the visual is sufficient.

1. LinkedIn News. (2025, January 15). LinkedIn skills on the rise 2025: The 15 fastest-growing skills in the U.S. LinkedIn. https://www.linkedin.com/pulse/linkedin-skills-rise-2025-15-fastest-growing-us-linkedin-news-hy0le/
2. Zinkula, J. (2025, March 21). OpenAI CEO Sam Altman has advice for Gen Z grads who want a seven-figure salary in the age of AI. Fortune. https://fortune.com/2025/03/21/open-ai-ceo-sam-altman-gen-z-grads-advice-ai-seven-figure-salary/
3. Gotham Ghostwriters & WOBS LLC. (2025). A.I. and the writing profession. https://gothamghostwriters.com/wp-content/uploads/2025/11/AI-Writing-Survey.pdf
4. Microsoft & LinkedIn. (2024, May 8). 2024 Work Trend Index annual report. https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part
5. Tomlinson, K., et al. (2025). Working with AI: Measuring the applicability of generative AI to occupations. https://arxiv.org/abs/2507.07935
6. Massenkoff, M., & McCrory, P. (2026, March 5). Labor market impacts of AI. Anthropic. https://www.anthropic.com/research/labor-market-impacts
7. Anthropic. (2026, January). Anthropic Economic Index. https://www.anthropic.com/research/anthropic-economic-index-january-2026-report
8. Ranganathan, A., & Ye, X. M. (2026, February 9). AI doesn’t reduce work—it intensifies it. Harvard Business Review. https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies-it
9. Anderson, J., Rainie, L., & Vogels, E. A. (2023). The future of human agency. Pew Research Center & Elon University. https://www.elon.edu/u/imagining/surveys/xv2023/the-future-of-human-agency-2035/

Requirements

1. Use APA 7 in-text citations throughout both narratives — every claim must be attributed at the moment you make it. A reference list alone does not substitute for in-text citations. This applies to your visualizations as well — each visualization must include APA 7 source attribution. A footer citation listing all sources used in the visual is sufficient.
2. Include at least 2 direct quotations across the two threads, properly cited in APA 7.
3. Reference at least 3 sources per thread in your narrative.
4. Your synthesis should build an argument across sources — not describe each source separately. A synthesis draws a conclusion that no single source makes on its own. If your paragraphs each introduce a source and summarize what it says without connecting them, that is description, not synthesis.
5. Upload both visualizations and their accompanying narratives as a single document. You may also share a URL in Canvas. Word length: 500 words per thread. Adopt a professional writing style. Write for a professional workplace reader, not a professor. Avoid addressing “students” — your audience is knowledge workers and early-career professionals. If you used GenAI for any part of this assignment, submit a Metacognitive Report as part of the same document. See the Metacognitive Report guidelines for what is required.

Evaluation Criteria

• Synthesis quality: Do the visualizations accurately represent the results of the assigned readings? Are sources and information properly attributed?
• Visualization effectiveness: Do the visualizations clearly tell an accurate story?
• Clarity – Is the writing clear and well-organized? – Elements of Style

On Visualizations

Your visualization should tell a story, not list points. If a reader can scan your visual as a bulleted list, it is not yet doing visual work. Study how professional reports — like the Microsoft Work Trend Index or the Anthropic Economic Index — use structure, hierarchy, and design to communicate an argument at a glance. Pyramids, workflows, quadrant maps, and wheel diagrams all show relationships between ideas. Colored bullets do not.

Evaluation Criteria

Synthesis quality: Does the narrative build an argument across the assigned readings rather than describing them one by one? Are sources cited in the body with APA 7 in-text citations? Are at least 2 direct quotations included? Does the narrative reference at least 3 sources per thread?

Visualization effectiveness: Does the visualization tell an accurate, coherent story? Are sources attributed on the visual itself? Does the visualization draw from at least 3 assigned sources?

Clarity: Is the writing clear, well-organized, and written in a professional register? See the Elements of Style at Writing Commons.

Guidelines for Original Research – Career Readiness in the AI Frontier – Recommendation Report – [Your Field]

Deliverables

Your final submission must include all of the following:

1. The report itself (750–1000 words, with both visualizations integrated)
2. Original research findings integrated into the argument
3. Specific, actionable recommendations grounded in evidence

What You’re Writing

You’ve built this report in three stages. First, you reviewed the literature on how career competencies are evolving in response to AI — that’s your background section, establishing the stakes before you make field-specific recommendations. Second, you conducted original research (SME interviews, surveys, or observational analysis) to ground your argument in firsthand evidence. Now you synthesize all of it into a single, clear answer to the module’s central question:

In a career field that interests you, which tasks are most exposed to AI automation — and which human competencies will matter more as a result?

Your two visualizations — the Pre-AI Foundation and the Boss of the Bots model — aren’t decorations. They are the backbone of your argument. The first establishes what career readiness looked like before AI; the second maps how that landscape is shifting. Together, they should frame everything that follows: your task-exposure analysis, your original research findings, and your recommendations. If those visuals are sitting on the page without being connected to your argument, the report hasn’t done its job yet.

Your authority depends on connecting those layers. Credible sources — NACE, NRC, Oliveri, Massenkoff and McCrory, the Anthropic Economic Index — establish the stakes. Your original research establishes the field-specific reality. Your analysis connects them into recommendations a reader can act on. If the assigned sources don’t appear in your report, that’s a signal your literature review and your report aren’t talking to each other. Vague claims, missing citations, and phantom references undermine that authority before your recommendations even land.

Audience

Your classmates. Each of you is investigating a different career field, so together you’re building a shared resource — field-specific intelligence about which competencies matter and how to develop them.

Format

This is a professional document, not an academic essay. Lead with your argument — don’t make the reader wait. Use headings, short paragraphs, and visual hierarchy to support scanning. Include both visualizations and let them do communicative work. Write concise, direct sentences; cut any word that doesn’t earn its place. Use APA 7 in-text citations throughout and include a complete reference list. Save your file as: LastName_CareerCompetencies_AIFrontier_[YourField].pdf

Evaluation Criteria

• Evidence integration. Does the report connect your literature review findings with your original research? Do the sources talk to each other, or do they sit in separate sections?
• Recommendation quality. Are your recommendations specific enough that a reader could act on them? Do they follow from your evidence, or do they float free of it?
• Visualization integration. Are both visualizations included? Do you reference them in your analysis, or do they just sit there?
• Professional communication. Is the report scannable, clearly written, and correctly formatted in APA 7?

Guidelines for Video Pitch — Career Readiness Recommendations

This video pitch distills your recommendations into a concise, persuasive format that could be adapted for LinkedIn or other professional platforms.

Requirements

1. 90 seconds maximum (strict limit).
2. Open by naming your field and identifying one or two specific tasks most exposed to AI automation — this establishes why your recommendations matter.
3. Present 2–3 competencies that students in your field should develop now, and explain how to develop them. Recommendations should follow from your research, not float free of it.
4. Reference at least one finding from your original research — your SME interview, survey, or job posting analysis. This is what makes your argument field-specific rather than generic.
5. Maintain professional presence: clear audio, appropriate framing, confident delivery.
6. Upload as MP4, MOV, or an unlisted YouTube/Vimeo link.

Evaluation Criteria

1. Task-exposure argument. Does the pitch open by identifying specific tasks in your field that are most exposed to AI automation? Does that framing establish why your recommendations matter?
2. Recommendation clarity. Are the 2–3 competencies clearly identified and explained? Are they specific enough that a viewer could act on them?
3. Evidence grounding. Are recommendations connected to your original research? Does the pitch reference at least one firsthand finding rather than relying on general claims?
4. Professional presence. Is audio clear, framing appropriate, and delivery confident?
5. Time management. Does the pitch stay within the 90-second limit?

Guidelines for Reflection

In approximately 150 words, write a brief note to me reflecting on what this module revealed. The best reflections are specific — name the competency, name the moment, name the decision you made as a result. Vague takeaways like “I learned a lot about AI” earn partial credit. Specific ones — “my SME told me that X, which contradicted Y from the literature, and that tension changed how I think about Z” — earn full credit.

Consider addressing one or more of the following:

• Which competency surprised you most, and why?
• What did your original research reveal that the published literature didn’t fully capture?
• What will you actually do differently as a result of this module?

Guidelines for the Metacognitive Report

For detailed guidance, examples, and the complete list of legitimate AI roles, see Metacognitive Report – AI Writing Ethics: Balancing Agency, Voice & Disclosure.

Your report must include:

1. Header — Beneath your title, record left-justified:

• Word Count / Name / GenAI Tools Used / Chat Log Links

2. GenAI Usage Table(s) — One table per tool with these columns:

• Step in Writing Process (Prewriting, Drafting, Revising, etc.)
• Number of Chats
• Primary Purpose(s) (Thought Partner, Research Assistant, Teaching Assistant, etc.)
• Notes on Use (2–3 sentences: what you asked, what the AI gave you, and whether you accepted, revised, or rejected it — and why)

3. Critical Reflection (minimum 250 words) — Explain:

• Which roles AI played and why
• At least one moment where you rejected or corrected AI output
• How AI helped you learn something you then applied independently
• Where you made decisions AI could not make for you

Submission Guidelines

• Upload your report along with your assignment to Canvas by the required due date.

Evaluation Rubric for Rubric