Before we talk about AI writing — let's see where the class stands right now.
AI doesn't replace the writing process — it changes where your effort goes. Instead of staring at a blank page, you're editing, directing, and shaping output that already exists.
Give the AI a clear brief and let it produce a full draft. The draft won't be perfect — that's not the point. The point is to get past the blank page. Your job shifts from writing to editing, which is almost always faster. Drafting is where most communication students start using AI, and it's one of the highest-value applications when done with a strong prompt.
Common uses: First draft of a press release, news story lede, show notes, ad copy, bio, caption set, social post series.
Paste in your own writing and ask the AI to tighten it, fix grammar, improve clarity, or cut it to a specific length. This keeps your voice in the piece while using AI for the mechanical parts of editing. It's often more effective than asking AI to write from scratch, because it starts from your ideas and structure.
Take one piece of content and adapt it for a completely different channel or audience — without rewriting from scratch. A press release becomes a social caption. A formal bio becomes a casual podcast intro. A news story becomes an email newsletter blurb. AI can make these shifts in seconds; a human would spend thirty minutes on each one.
Paste in a long document — a transcript, a research report, a meeting recording — and ask the AI to pull out the key points, quotes, or facts you need. This is one of the most reliable AI use cases because the source material is already accurate; the AI is just reorganizing it, not inventing anything.
These workflows apply across every professional writing format you'll encounter in communications:
AI writing breaks down in predictable ways. Learning to recognize these failure modes is what separates a professional who edits critically from one who just pastes and submits.
When a prompt lacks role, audience, and constraints, AI defaults to a safe, featureless middle ground. The result sounds like the opening paragraph of a Wikipedia article — technically correct but completely devoid of personality or purpose.
AI does not look things up. It generates plausible-sounding text based on patterns in its training data. When you ask it about specific people, dates, statistics, quotes, or events it wasn't trained on — or trained on inaccurately — it will confidently produce wrong information. This is called hallucination, and it has ended careers.
AI takes your prompt literally. If your brief is incomplete, the output will be too. The AI won't ask what you meant — it will fill the gaps with its best guess, which is often the most generic option available. Context that feels obvious to you is invisible to the model.
Pick your track and your task. Use Claude, ChatGPT, or Gemini to create something real. We'll review your output together — look for the three failure modes you just learned.
This prompt is already built using the seven-element framework from Lesson 2. Copy it, paste it into Claude, ChatGPT, or Gemini, and add your own details where you see [brackets].
If AI wrote 80% of your press release, who authored it? This is no longer a hypothetical question — it's one your future employer may ask you directly.
Authorship has always meant more than just typing the words. It means being accountable for the accuracy, fairness, and intent of a piece. When AI generates your content, you are still responsible for every word — even the ones you didn't write.
The question isn't whether you used AI. The question is whether you can stand behind the work as if you wrote every word yourself. If the answer is no — you haven't done enough editing.
These are real scenarios you will face in internships, freelance work, and staff positions. Industry norms are still forming — but the direction is clear: more transparency, not less.
You briefed the AI, it wrote the structure, you filled in real quotes and facts, you edited the voice. Standard in PR now. Disclosure varies by firm — but know your firm's policy before you submit.
AI summarized documents or structured the lede. You reported, verified, and wrote the voice. Many outlets now require a disclosure line. The AP has a standing policy — look it up before your first internship day.
You wrote it; you asked AI to tighten the grammar and cut 100 words. Most style guides don't require disclosure at this level — but your organization might. When in doubt, ask.
You brainstormed with AI but wrote everything yourself. Generally no disclosure required — this is no different from bouncing ideas off a colleague. But don't let "I used it for ideas" become a convenient defense when you actually used more.
If you wouldn't put your name on something a human had written for you without reading and editing it — you shouldn't put your name on something AI wrote for you without reading and editing it either. The byline is yours. The responsibility is yours.
New terms introduced in this lesson on AI writing workflows, authorship, and professional media production.
Asking an AI to rewrite existing content in a different register, formality level, or style for a new channel or audience. Tone-shifting is one of the most practical AI writing skills — the same core content can become a formal press release, an Instagram caption, an email newsletter blurb, or a podcast teaser without starting over. The key is specifying the new audience and platform precisely, not just saying "make it more casual."
A production process that uses more than one AI tool in sequence to complete a project. For example: use NotebookLM to analyze a research document → feed key findings into Claude to draft a press release → use Canva Magic Write to adapt the release into social graphics copy. Multi-tool workflows let you use each tool for what it does best, rather than forcing one tool to do everything. They also distribute your dependence, which matters as individual tools change or go down.
The process of improving AI output through successive follow-up prompts, treating each response as a step toward the final product rather than an all-or-nothing result. Professionals rarely accept a first AI draft unchanged. They refine the voice, correct the facts, adjust the structure, and push back when the output misses the mark — often across three to five rounds of prompting before the content is ready to use.
The distinctive personality, perspective, and style that makes a writer's work recognizable. Voice is what AI most consistently fails to replicate — it defaults to a generic, safe register that sounds like no one in particular. Developing your own voice as a writer is one of the most durable professional skills you can build, precisely because it's the hardest thing for AI to mimic. When you edit AI output, your goal is to put your voice back into it.
The line crediting the author of a news story, article, or media piece, typically appearing at the top: "By [Name]." A byline is not just a name — it is a claim of authorship and accountability. When you put your byline on a piece, you are telling readers that you are responsible for its accuracy, fairness, and integrity. The byline question is at the center of AI disclosure debates in journalism: if AI wrote most of it, what does your byline actually mean?
A statement attached to a piece of content indicating that AI tools were used in its production and describing how. Disclosure norms vary widely by outlet, firm, and platform — but they are evolving rapidly toward greater transparency. In academic work, Susquehanna University requires disclosure on any AI-assisted assignment. In professional practice, check your organization's policy before assuming any level of AI use is acceptable or that none requires disclosure.
When an AI model generates text that is factually incorrect but presented with the same confidence as accurate information. Hallucination is not a bug that will eventually be fixed — it is a structural property of how large language models work. They predict likely-sounding text, not verified facts. In communications work, hallucination is a professional liability: an invented quote, a wrong date, or a fabricated credential can cause real harm to real people. Every AI output must be fact-checked before it is used.
Lesson 3 · Moving from Ideas to Media