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Research Article

Cheating or Competing? University Students' Experience of AI Marketing and What It Means for AI Literacy Programming

added 2026-08-28 · updated 2026-09-19

Synthesis: Using a scalable action research model, Sobo and colleagues examined how AI tools are marketed to US college students and how those students experience the promotions, collecting and analyzing 131 social media ads, 48 student interviews, and field notes from three interns at student-facing AI companies. Interviewees framed AI use as a practical necessity shaped by grading systems, peer norms, and AI's digital ubiquity; while many associated AI with cheating and worried about dependency and learning forfeitures, most felt compelled to adopt it to stay competitive—an internalized entrepreneurial imperative that favored product over process. The authors recommend destigmatizing legitimate AI use, enabling more open student–teacher exchange, and teaching marketing literacy as a regular part of AI hygiene.

Key Findings

  1. Ads were not central to adoption. Of 131 unique ads from 99 companies (only Grammarly and Perplexity ran more than five), students reported that ads rarely swayed them; the obvious utility of AI for schoolwork, peer norms, and digital ubiquity underwrote opting in.
  2. Peer norms and "keeping up" drove use. Word of mouth was called "the biggest promotion that AI has," sustained by FOMO and fears of falling behind ("am I at a disadvantage? Am I missing out?"), supported by an infrastructure where AI already appears inside familiar software.
  3. Official legitimizers and confusion coexisted. Some classes required AI and campuses ran "correct use" campaigns, yet instructors who encouraged AI in class still often banned it in syllabi, and a ban was the perceived norm—creating vexing inconsistency.
  4. Manipulative "business tactics" were accepted as business as usual. Students noted fee-based upgrades, free-trial hooks timed to finals, "intentionally vague" language implying utility for cheating, and product placements, framing them as normal commerce.
  5. Strong stigma and hiding persisted. Half (51%) of students in the authors' 2024 survey said they would be embarrassed if their academic AI use became known; students hid use from teachers and feared being (even wrongly) flagged as cheaters.
  6. Distrust slowed adoption of official tools. Despite 82% of students saying they used ChatGPT in Fall 2024, only 41% had claimed free campus ChatGPT.edu accounts by end of Spring 2025, with official communications sometimes read as "a ruse to catch cheaters."
  7. Intern embedment functioned like student confederation. Student interns were pushed to "sell, sell, sell," network with campus clubs, and were promised cash payouts—a recruitment model the authors liken to tobacco marketing and, in one intern's words, "a pyramid scheme."
  8. Critical analysis rarely went deep. Most students cast use through the lens of choice and self-enterprise ("work smarter, not harder") with little reflection on the sociocultural values and norms purveyors leverage to drive adoption and dependency.

What this means for practice

  • Learners. Read the pitch before adopting the tool: 39% of ads marketed study-card generation or chatbot tutoring and 40% touted keeping up with new AI, yet interviewees said the ads rarely swayed them — peer norms and fear of falling behind did.
  • Learners. Treat free trials timed to finals and "intentionally vague" language implying utility for cheating as marketing signals, and check what a product is actually for before using it.
  • Instructors. Publish clear, consistent dos and don'ts with legitimate use cases and Privacy information, because 51% of students said they would be embarrassed if their academic AI use became known and only 41% had claimed free campus accounts despite 82% using ChatGPT.
  • Instructors. Model openness by stating the AI output you bring into your own teaching and mandating explicit AI-use statements in syllabi, so that disclosure rather than hiding becomes the norm.
  • Administrators. Fund process-oriented teaching with workable student–teacher ratios, since the competitive "get with it or get left behind" logic students internalized favors product over process.

Limitations

  • The project relied on rapid methods with convenience sampling — 131 social media ads collected from 99 companies, 48 student interviews, and field notes from three student interns — which the authors state limits scientific generalizability.
  • The analysis did not attend to variables their own survey flagged as potentially relevant, namely major, ethnic/racial identity, and gender, so differential experiences of AI advertising remain unexamined.
  • The analytic methods entailed some subjectivity, and the intern-embedment component rests on field notes from three volunteers trained in the interview method rather than on systematic observation.
  • The 51% embarrassment figure and the 82% ChatGPT use versus 41% account-claiming figures come from the team's own 2024 campus survey, not from the 131-ad and 48-interview dataset analyzed here.

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Citation

Sobo, E. J., Goldberg, D. M., Hauze, S. W., & Frazee, J. P. (2026). Cheating or competing? University students' experience of AI marketing and what it means for AI literacy programming. Annals of Anthropological Practice, 50.

Metadata

LevelHigher Ed
Intended audienceLearners
Research methods and evaluationDesign-Based Research, Mixed-Methods Research
Equity, ethics, and responsible useAI Use and Disclosure Statements, Ethics
Foundations of AI in educationAcademic Integrity, AI Literacy, Critical Thinking, Framing AI Use for Students
SourceHistoryEditChat

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