Novela Announces AI Search Simulation (SEO/GEO) for Marketing Education

A hands-on training environment for marketing in the era of AI-powered search, including SEO/GEO/AEO. Available fall 2026.

Novela, the marketing simulation platform used at over 40 universities including Columbia, Northwestern Kellogg, Imperial College London, King's College London, and Oxford, today announced Novela AI Search — its sixth simulation and the first interactive training environment designed to teach marketing decisions in an AI-powered search landscape.

The simulation will be available to partner institutions in fall 2026, with early-access previews opening in July. It is built for undergraduate, postgraduate, MBA, and executive-education courses, as well as for corporate training programmes.

Why are we launching the AI Search Simulation now?

Consumers are shifting from traditional search engines to generative AI for product research and brand recommendations.

ChatGPT, Perplexity, Gemini, Claude, and Google's AI Overviews now sit between brands and customers in ways marketing teams are still learning to navigate.

Recent research in MIT Sloan Management Review on the Information Search Marketing framework, authored by Pettiette and Whitler, documents cases where market leaders with the largest search budgets are being out-positioned by smaller competitors on AI platforms. Gartner projects a 25% decline in traditional search volume in 2026 as AI-native discovery scales.

As a result, the skills required of entry-level marketers are shifting rapidly.

Seventy-seven percent of hiring managers say AI has fundamentally changed what they look for in entry-level candidates, and US entry-level marketing postings have fallen by roughly 35% in the past eighteen months as the role itself reshapes.

Most marketing curricula have yet to catch up. Students graduate fluent in classic SEO fundamentals without hands-on experience in how AI platforms surface, cite, and recommend brands — skills already essential in marketing roles at every level.

"Every marketing team we speak to is asking the same question," said Clark Boyd, CEO of Novela. "How do we show up in AI answers? The frameworks exist. The measurement tools exist. What's been missing is a way for marketers to actually practise making the decisions. That's what this simulation does."

Interested educators and learning-and-development leaders can contact clark@novela.academy.

Learning through consequence

Novela AI Search places students in the marketing manager's chair at a premium consumer brand with an escalating AI-search visibility problem. Students work through research, strategy, execution, and measurement phases, making budget-allocation decisions under genuine constraint and watching consequences play out across platforms.

Every decision is consequential. Each student's choices produce distinct outcomes that Ela, Novela's in-simulation AI research assistant, explains in plain language — connecting specific decisions to specific results, surfacing trade-offs, and pushing students to think about what they would do differently next quarter.

The simulation covers the full scope of modern search marketing, including the emerging disciplines of GEO (generative engine optimization), AEO (answer engine optimization), and GEM (generative engine marketing) alongside the SEO and SEM fundamentals that continue to drive discovery and paid acquisition.

Grounded in current research

The simulation's pedagogical design draws on recent academic work in marketing education and human-AI collaboration, including Pettiette and Whitler's Information Search Marketing framework (MIT Sloan Management Review, 2026), research published in the Journal of Marketing on human-AI hybrid performance, and work in the British Journal of Educational Technology on assessment design in the age of generative AI.

Where it fits in a curriculum

For undergraduate digital marketing courses. AI Search runs inside a standard digital or marketing module alongside other Novela simulations. Pairing it with Novela's Google Ads simulation creates a complete search-marketing block that reflects how the discipline actually works in practice — foundational paid-search skills first, then the AI-native layer that sits on top. Students graduate with practical fluency employers now expect.

For postgraduate and MBA programmes. AI Search works as a single-session module inside digital marketing, marketing strategy, AI marketing, or integrated marketing communications courses. It also fits well in marketing technology, digital transformation, and emerging technology modules. The simulation is already set to go live in five brand new "AI in Marketing" modules this fall.

For executive education. A standalone session for participants already grappling with AI-search visibility in their own organisations. Works well in one-day intensives, week-long programmes, and custom executive leadership formats.

For corporate training. A parallel half-day workshop version for enterprise marketing teams is in development, designed for learning-and-development leaders looking to build AI-search fluency inside marketing and communications functions.

For faculty already running Novela simulations. The full catalogue — Google Ads, Meta Ads, Organic Social, B2B Marketing, Media Planning, and now AI Search — sequences naturally across a 10 to 14-week module. AI Search closes the search-marketing loop.

Availability

General availability: Summer 2026, in time for the fall semester.

Early access: Opens in July for partner faculty who want a first look and a direct line into Novela's design team ahead of launch.

Switching from another provider: Novela typically manages the move inside a couple of days, handling professor onboarding, class-level setup, and in-class debrief materials.

Research partnerships: Decision-level data from Novela simulations is available to faculty researching AI literacy, pedagogy, and digital-marketing behavior.

Frequently asked questions

What is Novela AI Search? A browser-based marketing simulation for universities, colleges, and corporate training programmes. It teaches how to plan, execute, and measure marketing in the era of AI-driven discovery, covering the emerging disciplines of GEO, AEO, and GEM alongside SEO.

Which platforms does it cover? The major generative AI platforms students will be marketing to in their careers: ChatGPT, Perplexity, Gemini, and Claude. It also addresses traditional Google search.

Who is it designed for? Marketing educators teaching undergraduate, postgraduate, MBA, and executive-education courses, as well as corporate training leaders. It suits modules in digital marketing, AI marketing, marketing strategy, integrated marketing communications, digital transformation, and any course updating its search or discovery content for autumn 2026 and beyond.

Does it teach GEO specifically? What about AEO and GEM? Yes to all three, alongside SEO and SEM fundamentals.

Glossary

AEO — Answer Engine Optimization. The practice of optimizing content to be surfaced in answer engines and AI Overviews. Often used interchangeably with GEO, though some practitioners treat AEO as narrower and more focused on direct-answer queries.

AI search. The use of generative AI systems — ChatGPT, Perplexity, Gemini, Claude, and others — to answer user questions directly, in place of or alongside the traditional list of links returned by a search engine.

Citation (in generative AI). The inclusion of a brand, publication, or specific source within an AI-generated answer. A primary measurement unit for GEO and AEO performance.

GEM — Generative Engine Marketing. The paid counterpart to GEO. The practice of securing promotional placements inside AI-generated responses, analogous to SEM in traditional search.

GEO — Generative Engine Optimization. The practice of shaping content, site structure, and authority signals so a brand is surfaced and cited by generative AI systems.

SEO — Search Engine Optimization. The established practice of optimizing a site to rank in traditional search engine results. Remains the foundation of organic discovery and continues to drive many of the signals that AI systems use to determine authority.

Share of Model. A practitioner metric for AI search visibility: the share of relevant AI-generated answers in which a given brand is mentioned or cited, measured across platforms and defined prompt sets. Analogous to share of voice in traditional media planning.

Structured data. Machine-readable markup added to a web page that helps search engines and generative AI systems interpret its content. A significant GEO signal.

Zero-click search. A search query where the user gets their answer on the results page, or inside an AI-generated response, without clicking through to any website. Creates new measurement and attribution challenges.

About Novela

Novela builds interactive marketing simulations used by universities, colleges, and corporations to develop practical marketing skills through structured decision-making. The Novela catalogue covers Google Ads, Meta Ads, Organic Social, AI B2B Marketing, Media Planning, and — from fall 2026 — AI Search. Simulations are designed around current academic research on experiential learning and human-AI collaboration.

Interested educators and learning-and-development leaders can contact clark@novela.academy.