COMM7035 SEO, GEO and Search Strategy in the Age of AI
The Complete Study & Assessment Guide · Semester 1, 2026-27COMM7035 Overview
- 3 unit postgraduate course
- Hong Kong Baptist University
- Semester 1, 2026-27
- English medium
- Coursework assessment
What this course is actually about
COMM7035 treats search as strategic communication infrastructure. The subject is not confined to ranking webpages or buying search advertisements.
- Assessed by Concept quizzes, a search visibility analysis, strategy presentations and a final reflection paper.
- Core method Define the audience decision, preserve reproducible evidence, then assign an owned recommendation.
- Hardest distinction A page can be indexed without being retrieved, cited or represented accurately.
- Evidence rule Treat GEO observations as hypotheses and report competing explanations beside any change.
How COMM7035 is assessed
| Component | Weight | Format |
|---|---|---|
| Search Strategy Foundations Quiz / Concept Test | 20% | Five early concept quizzes in the published schedule |
| Search Visibility Analysis | 20% | Group report plus instructor and peer-scored presentation |
| Content Strategy Presentation | 30% | Audience and channel strategy in the updated v2 syllabus |
| Integrated Strategy Presentation | 15% | Team integrated search roadmap in the updated v2 syllabus |
| Final Strategic Reflection and Position Paper | 15% | Individual reflection and position paper |
The LMS labels the available outline Syllabus v2, updated on 5 October. Its five numeric rows total 100%. Another official outline keeps the quiz, visibility analysis and final paper weights but lists a 15% Live Project Workshop Portfolio and a 30% Group Integrated Search Optimization Proposal instead of the v2 middle rows. Check the live course LMS before relying on the current task labels or submission settings.
Assessment structure
Widths reproduce the updated v2 percentages and total 100%.
Current COMM7035 dates
| Date | Item | Control |
|---|---|---|
| 6 October 2026 | Group Search Visibility Analysis | The LMS submission table shows a 12:30 due time and the presentation-day section occurs on the same date. |
Dates are as published in the course LMS section and submission table for this offering. Confirm exact deadlines and submission settings in the live LMS.
What COMM7035 covers
Five chapters follow the captured teaching sequence from search boundaries and audience intent through system mechanics, measurement discipline and technical audit decisions.
Search Landscape and Concept Boundaries
search as communication infrastructure, SEO and GEO boundaries, visibility surfaces, relevance and authoritySearch is a communication environment
This chapter starts by widening the object of study. Search is not only a ranked list reached through a browser.
- Search as distributed communication infrastructure
- Organic, paid, direct-answer and generated-answer boundaries
- Visibility stages from discovery to representation
- Relevance, authority and usefulness as separate diagnoses
- Mechanism-matched evidence for each optimisation label
- Why generated-answer observations remain conditional
Intent, Need States and Audience Journeys
audience needs, query and topic distinctions, intent types, journey stages, demand creation and captureA query is evidence, not the whole need
The words entered into a search box are an observable expression of a deeper task.
- Literal query versus underlying audience job
- Informational, navigational, commercial, transactional and local intent
- Journey stages as changing decision contexts
- Loops, pauses and restarts in audience behaviour
- Demand creation and demand capture as complementary work
- Representative question sets built from decisions
Search and AI Answer Systems
discovery, crawling, indexing, ranking, retrieval, citation, representation and source selectionFollow the source through the pipeline
A source does not travel directly from publication to visibility.
- Discovery, crawling, indexing and serving as separate gates
- Query-conditional ranking and the limits of index evidence
- Question expansion, retrieval, reranking and synthesis
- Retrieval-augmented generation as a diagnostic model
- Citation separated from factual representation
- Owned, earned and third-party source roles
Measurement, Evidence and GEO Claims
objectives and KPIs, search ratios, AI visibility vectors, baselines, controls and qualified claimsMeasurement follows the strategic chain
A metric becomes useful only after the team specifies the objective, audience need, hypothesis and tactic. Reversing that order encourages optimisation for whatever a dashboard happens to expose.
- Objective-to-decision measurement chain
- Inputs, outputs, outcomes and impact
- Counts and ratios with explicit denominators
- Presence, recommendation, citation and representation vectors
- Baselines, benchmarks and comparison conditions
- Prompt logs, repeated runs and coding rules
Technical Accessibility and Audit Decisions
access gates, crawl and index controls, semantic clarity, audit prioritisation, ownership and measurementTechnical search begins with access gates
A page cannot compete if no stable URL exists, users or bots cannot reach it, crawling is blocked, indexing is prohibited, rendering hides the main content or the system cannot interpret what the page represents.
- Stable URL, access, crawl, index, render and interpretation gates
- Robots rules versus index directives
- Canonicals, redirects and sitemap discovery
- Rendered content and machine-readable semantic clarity
- Structured data as clarification rather than guarantee
- Impact, reach, dependency, feasibility and risk prioritisation
It asks how audiences express needs across conventional engines, AI answer systems, social platforms and other discovery environments, and how organisations become findable, credible, quotable and preferred at those moments.
That wider frame connects organic search, paid search, content, technical access, public relations, reputation, platform discovery and source authority.
The boundaries you need before any tactic
The opening teaching block separates SEO, SEM, AEO, GEO and LLMO.
SEO concerns organic visibility, SEM normally covers paid search and campaign management, AEO focuses on extractable direct answers, and GEO concerns visibility, citation and representation in generated answers. These categories overlap, but they do not operate through the same mechanisms or carry the same evidence.
The course treats GEO as an emerging, contested label and rejects promises of guaranteed citation.
Audience need comes before keywords
The second block distinguishes the visible query from the deeper task behind it.
Informational, navigational, commercial, transactional and local intent provide a first classification, while audience journeys explain how questions change from awareness and exploration through comparison, choice, experience and advocacy. Journeys can loop and restart.
Strong strategy therefore connects a need state to a query, journey stage, platform, source and useful action rather than compiling isolated keywords.
How sources move through search and AI systems
The systems chapter follows a source from existence through discovery, crawling, indexing, ranking or serving, AI retrieval, citation and representation. Passing one stage never proves the next.
Search-enabled AI products may expand a question, collect candidate sources, rerank them and synthesise selected evidence. The source can then be mentioned, cited, summarised, recommended, misrepresented or omitted.
This chain is the foundation for diagnosing visibility without inventing an explanation of a proprietary algorithm.
Measurement is a decision discipline
The course places metrics after objectives, audience needs, hypotheses and tactics. It distinguishes inputs, outputs, outcomes and impact, then combines conventional search evidence with a multidimensional AI scorecard.
Presence, recommendation, order, share of voice, citation, sentiment, positioning and accuracy answer different questions.
Baselines, comparison conditions, repeated prompts and change logs reduce false confidence, while careful wording separates an observation from a causal claim.
Technical SEO is access and governance
The final detailed block asks whether a stable URL exists, whether people and bots can reach it, whether crawlers may fetch it, whether it can be indexed, whether its content renders, and whether entities and facts are clear.
Robots rules, noindex, canonicals, redirects, sitemaps and structured data solve different problems.
A useful audit prioritises barriers by impact, reach, dependency, feasibility and risk, then names an owner and a post-fix measure.
How the coursework rewards this method
The Search Visibility Analysis asks teams to define an audience, create a focused question set, compare one traditional engine alongside two AI answer products, record what appears and receives attribution, and turn findings into evidence-based recommendations.
Its rubric rewards reproducible method, interpretation beyond description, prioritised authority and relevance findings, and actions that follow from evidence. The practice chapter walks through that sequence using an original scenario.
Source and assessment note
The LMS identifies an updated v2 syllabus, but another official outline in the captured materials contains a different split for two middle components.
The guide uses the v2 table and explains the conflict rather than blending the rows. Confirm current submission settings on the course LMS. The examples and practice tasks here are independently authored study material, not University assessment questions.
Worked example · free
Compute a visibility vector before recommending action
- 4Compute presence, recommendation and citation rates with eight as the denominator.
- 2Compute share of voice with twenty tracked mentions as the denominator.
- 4Compare the dimensions and state the next evidence question without claiming a cause.
Key terms
- Search Intent
- The underlying job a person seeks to complete when using a discovery system.
- Audience Need
- The deeper question, worry, desire or practical job behind the visible query.
- Search Visibility
- The ability to appear for relevant questions across results, sources, citations, recommendations and representation.
- Answer Visibility
- The extent to which an organisation is mentioned, cited, recommended or accurately described inside generated answers.
- Retrieval
- The selection of candidate sources or passages that may help a system answer a question.
- Representation
- How an answer describes an organisation, person, issue or source, including accuracy and positioning.
- Entity Clarity
- Explicit naming of things and their relationships so people and systems can distinguish what belongs together.
- Baseline
- A recorded starting value used to judge later movement after planned work occurs.
- Benchmark
- An external or historical reference against which current performance can be interpreted.
- Citation Visibility
- The frequency with which owned pages are cited or linked across a defined prompt set.
- Crawlability
- Whether a crawler can discover and fetch a page or resource.
- Indexability
- Whether a fetched page is eligible to be stored and considered for results.
- Canonical URL
- The preferred URL identified when similar or duplicate versions of content exist.
COMM7035 FAQ
Does the course have a final examination?
The published assessment tables describe weighted coursework and end with a Final Strategic Reflection and Position Paper. The captured LMS materials do not publish a final examination format. Use the current LMS to confirm submission settings and any update to the task structure rather than preparing for an invented exam.
Why are there two different assessment structures in the materials?
The LMS identifies a v2 syllabus updated on 5 October. That version gives 30% to the Content Strategy Presentation and 15% to the Integrated Strategy Presentation. An earlier official outline instead gives 15% to a workshop portfolio and 30% to an integrated proposal. This guide shows the v2 table and flags the conflict.
What is the practical difference between SEO and GEO?
SEO focuses on organic search visibility and the path from query to ranked result, while GEO examines whether generated answers retrieve, cite and represent sources. GEO does not replace SEO: accessible, relevant and authoritative sources still feed retrieval. Keep the mechanism and evidence separate when a project includes both.
How many prompts should a visibility analysis use?
The published group brief asks for five to eight representative queries or questions, tested in one conventional search engine and two AI systems. The stronger set spans meaningful audience intents rather than slight wording variants. Record settings and select priority queries for deeper comparison of appearances, citations, omissions and representation.
What counts as AI visibility in this course?
AI visibility is a vector rather than one rank. The course separates presence, explicit recommendation, ordered position when an order exists, share of voice, owned citation, sentiment, positioning and factual accuracy. A brand can perform well on one dimension and poorly on another, so keep each denominator and coding rule visible.
Does structured data guarantee an AI citation?
No. Structured data can clarify entities, facts and relationships when it matches visible content. It supports interpretation and some search-feature eligibility, but it does not guarantee ranking, retrieval or citation. Test those downstream outcomes separately and avoid presenting schema as a shortcut around useful content or authority.
How should a GEO claim be worded after a before-and-after test?
Report the measured change, the controlled conditions and the alternatives that remain. If citations rose after a rewrite, say they increased in the tested prompt set and that the evidence suggests a relationship, while noting model updates, competitor activity, seasonality or measurement changes. Reserve causal language for a design that isolates the intervention.
Questions students ask in each COMM7035 chapter
- Is every mention in a generated answer a GEO success?
- No. A mention can be inaccurate, negative, unsupported or peripheral. Record presence separately from citation, recommendation, positioning and accuracy. That separation reveals whether the organisation is merely visible or is being used in the way the strategy intended. Chapter 01 · Search Landscape and Concept Boundaries
- Can one query have more than one intent?
- Yes. A short phrase may combine learning, comparison and local action, especially when context is missing. Choose the dominant task for the current analysis, record secondary possibilities, and inspect the result environment. The goal is a justified decision, not a claim that language has one permanent label. Chapter 02 · Intent, Need States and Audience Journeys
- Does a conventional ranking prove that an AI system can retrieve the same page?
- No. The products may use different indexes, partnerships, retrieval timing and selection procedures. A ranking can establish conventional visibility for a defined query, while AI retrieval and citation require their own observations. Keep the result as useful context rather than treating it as downstream proof. Chapter 03 · Search and AI Answer Systems
- Can recommendation rate and citation rate use the same denominator?
- They can when both are defined over the same fixed prompt set, but they still represent different coded events. State the denominator and rule for each. If citation is counted per source rather than per prompt, do not compare the resulting percentages as though they share a unit. Chapter 04 · Measurement, Evidence and GEO Claims
- Can a page blocked from crawling still remain indexed?
- It can under some conditions because systems may know the URL from links or earlier access while being unable to fetch current directives and content. That is why crawl control and index eligibility should not be treated as synonyms. Inspect the evidence available for each gate before choosing a remedy. Chapter 05 · Technical Accessibility and Audit Decisions
How to prepare for the assessments
Build one invented organisation and use it across the guide. First write three audience needs and convert them into a small query set. Next trace each needed source through discovery, indexing, ranking, retrieval, citation and representation. Then calculate one conventional metric and several AI visibility dimensions with the numerator and denominator visible.
Finish by diagnosing a technical barrier and writing an owned recommendation containing problem, evidence, impact, fix, dependency, priority and measure. This sequence rehearses the course's central movement from audience need to evidence and decision. Revise distinctions by explaining them aloud without acronyms, and treat every platform claim as provisional until you can name the test, date and source.
For the group analysis, draft the evidence appendix first, because reproducible screenshots and source records make the later narrative faster and prevent unsupported findings.
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