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Paper A

Implementasi Generative AI untik Digital Marketing & Konten Sosial Media

Iffah Nabila, Sri Widaningsih — 2026 — Journal of Applied Engineering and Social Science (JAESS)

peer reviewed journal article  ·   ·  test this week

Key findings

• Using AI tools like ChatGPT and Copy.ai made it faster to create social media content — the team spent less time writing and designing posts.
• With AI helping, the startup was able to post more consistently — content went up on schedule more reliably than before.
• Audience engagement (likes, comments, interactions) became more stable and predictable after AI was introduced into the workflow.
• Combining AI content tools with analytics platforms (like Meta Business Suite) let the small team make faster, data-informed decisions about what to post and when.

Marketing implications

• If you run social media for a small business with a tiny team, try plugging ChatGPT or Copy.ai into your content calendar process — even a basic AI-assisted workflow can help you post more consistently without hiring more people.
• Pair your AI content tools with a free analytics platform like Meta Business Suite so you can quickly see what is working and adjust — do not just create content, track it in real time.
• If your team struggles to stay on schedule with content, try breaking your month into short 2-week sprints (like this study did) with clear goals for what AI will help produce — structure plus AI together seems to drive consistency more than AI alone.

Paper B

RECONCEPTUALIZING HIGHER EDUCATION MARKETING IN THE ALGORITHMIC ERA: INSTITUTIONAL GENERATIVE AI AND MULTIDIMENSIONAL UNIVERSITY BRAND EQUITY

Nozima Zufarova — 2026 — Zenodo (CERN European Organization for Nuclear Research)

peer reviewed journal article  ·   ·  use cautiously

Key findings

• Universities that built their own custom AI tools (rather than letting students use generic ones like ChatGPT) were seen as higher quality and more prestigious by students — the AI tool itself became a signal of institutional excellence.
• The more students actually used the university's AI tool (measured by real server traffic, not just self-reports), the stronger the connection between using the tool and trusting the university's brand.
• The path from 'using the AI tool daily' to 'feeling loyal to the university long-term' ran through two steps: first students became more aware of the university's brand, then they started seeing the university as higher quality. Only after both steps did loyalty increase. The model explained about 47% of the variation in student loyalty (R² = 0.468).
• This study is the first to empirically test AI tools as brand-building marketing assets in higher education, rather than treating them only as academic integrity risks.

Marketing implications

• If you work in university marketing, consider building a custom-branded AI assistant (rather than just pointing students to ChatGPT). According to this study, having your own AI tool — one that carries the university's name and identity — may make students see the institution as more prestigious.
• Track actual usage metrics (how many students use the AI tool, how often) not just satisfaction scores. This study found that real usage data mattered more than attitudes alone in shaping brand outcomes.
• Think of your AI tools as brand touchpoints, not just IT infrastructure. Every time a student uses a university AI tool, they are forming impressions of the institution — design those interactions with the same care you would give a campus visit or an admissions email.

Paper C

Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows

Miao Liu, Zhizhe Liu — 2026 — arXiv

preprint  ·   ·  test this week

Key findings

• An AI that can correctly quote a key fact from a document does not necessarily use that fact when making a decision. In short documents (~2,000 words), a risk disclosure shifted the AI's 'sell' recommendation by 3.2 percentage points. In longer, realistic documents (~100,000 words), that influence dropped to essentially zero — even though the AI could still quote the disclosure word-for-word when asked directly.
• This 'reads it but ignores it' problem appeared across all three AI model families tested, and also showed up when using real company filings (10-Ks) — not just made-up test documents. Bigger, more capable AI models delayed the failure but could not prevent it.
• How you structure the AI's workflow matters more than which AI model you use. A common approach — breaking a document into chunks and summarizing each one — made the problem worse, eliminating the disclosure's influence even in short documents. What actually fixed the problem: taking the key decision-relevant facts and placing a clear, structured restatement of them right before the AI makes its final judgment, while keeping the source document available.
• Using a fix (targeted structured restatement), the disclosure's influence jumped to 8.5 percentage points even in 128,000-token documents — more than double its influence in the baseline short-document condition. The same AI, the same document, the same information, but a different workflow produced materially different decisions.

Marketing implications

• If your team uses AI to analyze long documents — brand audits, competitor research, customer feedback reports, RFPs — don't trust that the AI 'read and considered' every section just because it can quote it back. Test whether key findings actually show up in the AI's conclusions by asking it to justify specific decisions against specific passages.
• When building AI workflows for content analysis or research, avoid the 'chunk-and-summarize' approach for anything where a specific fact must actually drive a decision. Instead, pull the key facts out explicitly and put them right in front of the AI immediately before it makes its judgment call.
• If you're evaluating an AI research tool, retrieval accuracy (can it find the fact?) is not enough — run a test where you remove a key piece of information and see if the AI's recommendations actually change. If they don't change, the tool isn't really using that information.

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