Scholarly journals can be a huge winner in AI search, but we’ve found that basically no academic journals even use basic SEO, let alone AI-oriented SEO/GEO. We believe this presents a significant opportunity for O/A journals, mid-sized journals, and publishers to compete on an equal footing with prestigious, high-IF journals.

This article explains how and why MacroLingo is sending out this appeal to journals and journal publishers to take advantage. And we’re offering a very limited number of seats for this service.

TL;DR

AI search surfaces answers, not links, and journals missing from those answers lose visibility
Academic articles already fit AI systems well, but poor structure and metadata block citation
Most journals still rely on PDFs and weak SEO, which AI tools largely ignore
Smaller and mid-tier journals can outrank elite titles in AI answers with the right setup
Modest editorial and technical changes decide whether journals get cited or skipped

AI overviews, AI search, and the HUGE opportunities in academia marketing

AI-powered discovery is moving from blue links to answers. Google’s AI Overviews, ChatGPT search, Perplexity, and Bing Copilot now place synthesized responses at the top of the page, often before any traditional result. Google’s rollout alone claimed access for hundreds of millions of users in 2024 with plans to reach over a billion, which signals a structural shift in how readers encounter scholarship. Independent analyses in 2025 report reduced click through rates on classic listings when an AI Overview appears, which means journals that are not cited inside these summaries may be unseen even when they hold strong positions in organic search.

For academic publishers, the upside is real. AI systems prefer clear claims, explicit evidence, robust metadata, and accessible summaries. Journals that deliver these signals get cited as sources inside the answer itself.

MacroLingo helps editorial teams apply SEO and GEO, the generative engine optimization approach, so that articles, abstracts, and releases are eligible for citation in AI answers as well as for ranking in traditional search. This article defines AI search returns, explains why they matter, lays out practical steps for journals, and closes with a pragmatic plan you can act on now.

What are AI search returns

AI search returns are machine-written responses that appear directly on the results page, accompanied by links to sources. Four products dominate today’s landscape:

Google AI Overviews provides a synthesized paragraph with citations to supporting pages and is governed by guidelines that tell site owners how to qualify for inclusion. Journals such as Nature and The BMJ have already been observed in Overviews for clinical queries, showing how authoritative science content can surface in this layer.

ChatGPT search blends a live crawl with answer generation and inserts inline citations that users can expand (OpenAI). Studies from Elsevier journals, for example, appear in ChatGPT search answers on drug safety and environmental science when abstracts are well structured.

Perplexity returns concise answers with numbered citations that point back to the original sources (Perplexity Help Center). PLOS ONE articles and Lancet studies are frequently cited here, demonstrating that open access improves discoverability.

Bing Copilot integrates an assistant into search and presents an organized summary view with sources (Bing Copilot). Reports show Springer Nature content among the citations when metadata is complete and abstracts are clear.

This layer does not replace indexing and ranking. It filters and compresses the web into a short, sourced narrative. The inclusion logic rewards pages that are easy for a model to parse and defend with citations: precise definitions, scannable structure, linked evidence, and machine-readable data.

Why academic journals really should care

Readers often treat answers that appear inside the results page as credible and complete. User research observes that many people now look to AI summaries first, then decide whether they need to click through at all. When a journal is cited inside the answer box, three effects follow:

Visibility at the top: AI returns frequently sit above all organic links, so citation here creates immediate exposure even when the article is not ranked first.
Trust transfer: inclusion signals that an independent system considered the page worthy of citation, which readers often interpret as a quality cue.
High intent traffic: users who do click from an AI return are usually seeking a specific detail, method, dataset, or figure, so their likelihood to engage is higher than a casual browser.

Journals absent from this layer cede discovery to secondary summaries, news sites, or competitors. For example, Science may be cited in an AI Overview about climate models, while a mid-tier journal covering the same topic is invisible. MacroLingo’s view is simple: keep your editorial standards, then add the structures that modern systems can parse at speed.

Current challenges for journals’ SEO and AI search

Common blockers limit visibility in AI answers:

Many articles exist only as PDFs. JAMA and PNAS provide HTML versions alongside PDFs, while smaller society journals often do not.
Metadata is inconsistent. Missing DOIs and ORCIDs make entity resolution harder. Crossref’s guidance focuses on complete metadata records.
Plain language summaries are rare. PLOS has adopted them widely, but many subscription journals lag behind (PLOS ONE review).
XML is underused. Large publishers like Elsevier invest in JATS XML, but smaller independent journals struggle with resources (NISO JATS 1.4).

MacroLingo helps journals of all sizes close these gaps, whether they are global publishers or niche society titles.

Examples from publishers and journals already aligning with AI discovery

Some journals “get it”. They are smart and they are using AI and modern SEO to expand their brand and increase quality submissions.

PLOS, several titles, require a short Author Summary written for non-specialists, for example, PLOS Computational Biology and PLOS Digital Health. This plain language field sits next to the abstract and is designed for machine and human scanning.
eLife publishes a plain language eLife Digest with many research articles, providing a concise summary that non-experts can understand, which also gives models a clean paragraph to quote.
Cochrane mandates a standardized Plain Language Summary template for reviews, which improves consistency and machine readability across abstracts and findings.
BMJ journals often require a structured abstract and a short box explaining what the paper adds, which surfaces the takeaways in a scannable format that AI systems can extract.
JAMA Network requires structured abstracts and a brief Key Points section for many article types, practices that make extraction and citation straightforward for AI tools.
Elsevier and Springer Nature operate XML first pipelines and expose JATS XML for open access (OA) content, which improves downstream parsing and reuse by indexing services and AI systems.

These are practical, public examples that any editorial team can adapt. MacroLingo builds similar structures into templates and author guidelines so that your content is ready for AI driven discovery.

How journals can appear in AI Overviews and AI search

A practical plan has four parts:

Improve metadata and structure: HTML versions for all articles, complete Crossref records, ORCID for authors, ROR for institutions. Elsevier and Wiley have invested heavily here, setting a standard.
Publish plain language summaries: PLOS provides good models, and Springer Nature has piloted plain language abstracts in medical fields.
Strengthen authority signals: link to institutions, preregistries, funders. The New England Journal of Medicine connects articles to clinical trial identifiers, making them easier for AI to cite.
Adopt GEO principles: structure articles with headings, tables, and explicit references. Journals that follow NISO recommendations are more machine-friendly.

MacroLingo supports implementation with templates, guidelines, and editorial coaching.

Case examples

BMJ group journals adopted structured plain language summaries (PLS) in 2024. Several BMJ Open studies have since been cited inside Google AI Overviews for patient safety queries.
PLOS Biology press releases with schema markup have appeared in Perplexity results, demonstrating the benefit of open access combined with structured data.
A Japanese university partnered with MacroLingo to adapt abstracts and metadata for AI systems. Within two issues, citations of their engineering journal appeared in ChatGPT search results, drawing international readers.

Business and academic benefits

The payoff spans readership, reputation, and revenue:

Global reach: AI returns expose articles to audiences beyond databases like PubMed or Scopus.
More citations and mentions: AI-cited articles are noticed and reused more often.
Operational resilience: templates and metadata improvements reduce production friction.

MacroLingo designs staged programs that scale from flagship journals to society titles.

Comparison table: traditional SEO vs GEO vs AI search optimization

Factor	Traditional SEO	GEO, generative engine optimization	AI search returns optimization
Primary goal	Rank pages for queries	Make content readable by people and machines	Earn citation inside on page AI answers
Signals	Keywords, backlinks, crawlability	Clarity, examples, evidence in text	Precise claims, structured data, verifiable sources
Page format	Often PDF plus basic HTML	Clean HTML with headings and alt text	Clean HTML with extractable definitions, labeled tables and figures
Metadata	Title, description, canonical	Schema.org types, complete sitemaps	Full Crossref records, ORCID, ROR, funders, datasets
Measurement	Positions and CTR in SERPs	Engagement and reuse	Presence as cited source in AI Overviews, ChatGPT search, Perplexity

Final checklist for journal editors

Do we publish HTML alongside PDF? PNAS and The BMJ do, smaller society journals often do not.
Are DOIs and ORCIDs registered consistently? Elsevier and Springer set benchmarks here.
Do we provide plain language summaries? PLOS is a leader, many others lag.
Do we follow JATS4R recommendations? Larger publishers often comply, smaller ones may need help.
Do we test for AI visibility? Run trial searches on Google AI Overviews, ChatGPT, Perplexity, and Bing Copilot.

Will your journal seize the opportunity?

AI search returns are now part of global discovery. Journals that adapt with HTML, metadata, plain language summaries, and structured data will be cited more often. Those who wait will fade from view. MacroLingo helps journals of all sizes take these steps quickly and sustainably.

How MacroLingo can help your journal(s)

Plain language summaries: Create style guides, draft PLS, and scale them across issues.
GEO content standards: Train editors and authors to structure articles for both humans and machines.
Customized content: Produce blog posts, write content for journal and publisher pages, and localize into other languages. It’s content marketing specifically for academia.
Integrated author support: In cooperation with Sci-Train, at MacroLingo Academia, we can provide training programs that help authors present their work in ways that surface in AI discovery.

With MacroLingo, journals increase citations, readership, and reputation by appearing in the answers that researchers already read.

Interested in learning how to push your journals to the top of search returns? Get in touch.