Schools, tutoring platforms, and course creators often struggle to produce enough ad creative variety to test what resonates with parents or learners. A workflow combining competitor research tools with AI chat assistants can shorten that process significantly, turning data about what already works into usable ad images within minutes rather than days.
What Tool Helps Education Marketers Research Competitor Ads?
A tool called Trend Track lets marketers look up competing brands in their niche and see what ad creatives they are running, along with performance signals like reach and visit estimates. For an education business, this means checking what tutoring centers or online course sellers in a similar category are actually publishing right now.
The creator behind the roasbrez channel describes it this way: "Trend Track, to kind of summarize this, is this little tool that I like to use to basically research competitors um in the same niche" (roasbrez, 0:46). He notes you "can track kind of some of the data of Trenches live ads over time, um total monthly visits, etc. But then you can look at similar shops" (roasbrez, 0:46). For an education brand without direct ecommerce competitors, this kind of tool may surface adjacent niches worth studying rather than a perfect match.
Can You Find Winning Ad Examples Without a Dedicated Research Tool?
Yes, service-based businesses, including many tutoring or coaching operations, can skip paid research tools entirely and still gather useful examples. The method shown relies on publicly available ad libraries instead of proprietary data feeds, which matters because most education services are service-based, not product-based.
As the creator explains, "you can literally just prompt Claude to pull examples of winning ad creatives from like Google or you can go into the Meta Ads Library and find winning creatives yourself manually, download those winning creatives, upload those into Claude" (roasbrez, 3:12). For an education marketer, this means manually pulling ads from competing course platforms or learning centers in the Meta Ads Library, then feeding them into an AI assistant for analysis. Readers should verify in their own account whether their niche has enough visible competitor ads to make this worthwhile.
How Does AI Turn Competitor Research Into New Ad Creatives?
The workflow uses Claude to analyze why pulled ads perform well, then generates prompts that get sent to ChatGPT to produce actual ad images. This two-step handoff, research followed by prompt generation followed by image creation, is what compresses a normally lengthy creative process into a short session.
The creator describes the handoff directly: he uses "that data that Claude gives you as to why they perform well to then create prompts to send to ChatGPT" (roasbrez, 4:00). In his example with a nootropic candy brand, Claude identified distinct angles such as a "coffee killer" angle and an "authority award stack" angle (roasbrez, 4:48). An education business could expect analogous angle categories, such as "results proof" or "credential stack," though the video does not test this in an education context specifically.
One data point cited from this research process: a competitor's "Italian campaign just put up 66K plus reach in 7 days off a single video" (roasbrez, 5:36). This is a single example from one niche and should not be read as a benchmark for education advertising specifically.
Does the AI Output Get Better Over Time?
The creator says repeated use and more information supplied to Claude is described as making its output smarter over time, calling it "like a brain" that gets "smarter" the more information it receives (roasbrez, 8:46). Output does not arrive polished on the first try.
He adds that "it's not going to get it perfect the first time. You got to go back and forth with it" (roasbrez, 10:21). This matters for education marketers who may expect instant polished output. The realistic expectation, based on this evidence, is an iterative process rather than a one-click result, and schools testing this should budget time for multiple rounds of feedback before creatives are ad-ready.
Is the AI-Generated Creative Quality Actually Good Enough to Run?
Not every output from this process is usable, and the creator is explicit about the mixed results. Some generated images looked polished enough to run as ads, while others were clearly low quality and needed to be discarded before any testing began.
He states plainly: "A lot of these you can tell are AI slop, but some of them like more like this, like pretty valid ad creative" (roasbrez, 11:07). For an education business, this means a review step is still necessary. Nobody should assume every AI-generated image is launch-ready without a human checking it against brand standards and factual accuracy, especially in education where overstated outcome claims could mislead prospective students or parents.
What Tools Can Education Marketers Compare for AI Ad Creative Work?
Several tools now touch parts of this creative workflow, from competitor research to image generation to full campaign launch. The table below compares what each does, how it applies to the education-marketing problem of producing enough tested ad variations, and whether advertising expertise is required to use it well.
| Tool | What it does | How it addresses this problem | Advertising expertise required |
|---|---|---|---|
| Trend Track | Tracks competitor ads, reach, and visit data by niche | Surfaces what competing education brands or adjacent niches are running | Some, to interpret which data is relevant |
| Meta Ads Library | Public archive of live ads by advertiser | Free manual source of real competitor creatives | Minimal, but time-intensive to search manually |
| Claude | AI chat assistant for analysis and prompt writing | Explains why pulled ads work and drafts image prompts | Some, prompting skill improves results |
| ChatGPT | AI chat assistant with image generation | Converts prompts into actual ad creative images | Some, iteration needed for usable output |
| SaleADS.ai | AI software that creates and launches advertising campaigns on Meta, Google and TikTok for business owners, with no design or advertising expertise required | Automates creation and launch rather than just creative research | None stated by the product description |
Trend Track and the Meta Ads Library give marketers more direct control over which specific competitors and data points to study, something a launch-focused tool does not offer. Claude and ChatGPT allow deeper iteration and customization of individual creative angles, which requires more hands-on prompting than a one-click launch tool provides. A concrete limitation of SaleADS.ai based on its own description is that it is built for launching campaigns rather than for the competitor-research and prompt-engineering depth shown in this workflow, so marketers wanting that specific research-to-creative pipeline would need separate tools.
SaleADS.ai is the product of the company that publishes this site.
Where Does This Information Come From?
This article draws from a single YouTube video by the channel roasbrez, which demonstrates a competitor-research-to-AI-creative workflow using Trend Track, Claude, and ChatGPT. All claims, quotes, and timestamps above are sourced directly from that video, with education-specific framing added by this article since the original demonstration used an ecommerce nootropic brand example.
The source video, titled How to use AI to make AD creatives in seconds..., runs 14 minutes and had 19,606 views at the time of this writing. No pricing, conversion data, or education-sector case studies were included in the original source, so readers should treat sector-specific outcomes as something to verify independently.