Mag.AI-Marketing — Full Curriculum (18 Courses)

Castalia Institute — Magister of Artificial Intelligence in Marketing

Authoritative copy: part of the MyST project (myst.yml). Do not maintain a separate curriculum elsewhere.

For a narrative walkthrough of every course in order, see CURRICULUM_OVERVIEW_PROSE.md. This document remains the spec: artifacts, prerequisites, and AI systems.

This document summarizes all 18 courses across 3 terms. Each entry states what the student builds, why it matters, and which AI systems support the work.

Delivery: Students learn and practice AI primarily in iNQspace — notebooks, simulations, and artifact lineage — aligned with Castalia’s “build worlds, don’t only discuss them” standard.

Marketing tools (MCP): Across terms, students leverage and build Model Context Protocol integrations for real marketing data and actions — analytics, CRM, ads, content systems — with explicit scopes and governance. See docs/MCP_MARKETING.md. The curriculum emphasizes tools: students repeatedly connect real or synthetic tool surfaces (APIs, exports, MCP servers) to world models—not slides about tools.

Legend — AI systems

Cross-cutting: Any course artifact that uses live marketing data or actions (not purely synthetic) should document MCP tool surfaces, scopes, and governance per docs/MCP_MARKETING.md.

AI literacy spine (technical)¶

The program is not only marketing-as-systems; it explicitly builds AI competence for go-to-market work:

These threads run across terms and concentrate in AINS-M6102 (testing), AINS-M6103 (data + retrieval workflows), AINS-M6204 (automation), AINS-M6205 (alignment).

Qualitative grounding¶

Simulations without evidence lie elegantly. Students calibrate agent and narrative models to interviews, surveys, or documented synthetic priors and record model–reality mismatch in SAMWISE (especially AINS-M6002–M6003).

Scope and pacing¶

Eighteen courses assume flexible pacing and faculty-defined milestones: each course is a bounded artifact sprint, not necessarily a full-time semester. Faculty publish minimum viable artifact criteria; overload is a scope bug, not a virtue.

B2B, B2C, and channel families¶

Buying context (e.g. committee vs individual purchase, long-cycle vs impulse) enters models as explicit constraints in audience and campaign courses—not a separate “B2B track.” Channel families (e.g. search, paid social, lifecycle email, partnerships) are parameterized modules inside channel and campaign courses—not vendor certification.

Major platform ecosystems (tools-first, named)¶

These are not “how to click every admin screen.” They are named ecosystems students model, measure, and (where policy allows) wire via MCP to notebooks and agents—auction dynamics, reporting shapes, policy constraints, and failure modes.

Channel economics (AINS-M6005), campaign orchestration (AINS-M6101), attribution (AINS-M6103), and measurement (AINS-M6006) must reference at least two of the three columns (social, Google, Amazon) in the required artifact unless faculty approve a narrower scope in writing.

Differentiation from growth-only tracks¶

Mag.AI-Marketing emphasizes positioning, narrative, brand dynamics, creative testing as statistics, and ethics of influence. Growth and attribution appear where they couple to story, trust, and measurement integrity—not as vanity metrics alone.

TERM I — Audience & Message Worlds¶

Objective: Formalize attention, trust, and message dynamics as simulatable worlds before scaling real spend.

AINS-M6001 — Personal Attention & Influence Systems¶

Description: Students construct an executable model of their own attention, credibility, and message–market fit as interacting stocks and flows. The self is treated as the smallest marketing system: what you attend to, what you signal, and how you allocate creative time — not a productivity blog dressed as strategy.

Key topics: Ontology for attention and credibility; time allocation for creation vs distribution; trust as a stock; message–audience fit as measurable mismatch; content economics (cost to produce vs reach); integration as substrate for later courses.

Required artifact: Executable self-influence model with documented assumptions and at least three perturbation scenarios.

AI systems used: PTAH (primary), SAMWISE (reflection), AI Faculty (a.Porter on differentiation of self; a.Taleb on reputation tails). MCP: leverage approved marketing/data tools; optional build path for personal instrumentation (see docs/MCP_MARKETING.md).

Prerequisites: None.

AINS-M6002 — Audience Worlds¶

Description: Students build populations of agents with heterogeneous attention budgets, trust thresholds, channel habits, and decision rules. Emergence from rules, not personas on slides: segmentation, sensitivity, and churn-like dynamics must appear from mechanics.

Key topics: Agent design; preference heterogeneity; satisficing vs optimizing; social proof and simple networks; qualitative calibration (interviews, JTBD-style probes, documented synthetic priors); validation memo; B2B vs B2C as constraint families on agents.

Required artifact: Simulated audience population with runnable scenarios (message, offer, channel change).

AI systems used: PTAH, SAMWISE (model vs reality mismatch), AI Faculty (behavioral framing; Porter on value).

Prerequisites: AINS-M6001 recommended.

AINS-M6003 — Positioning & Narrative Systems¶

Description: Students model positioning and story as mechanisms: what claims do, what categories they invoke, how narratives change perception over time. Positioning is not a slogan — it is a set of constraints and feedback loops in an audience world.

Key topics: Category and reference points; promise vs proof; narrative arc as state machine; consistency vs pivot; competitive framing; testable hypotheses.

Required artifact: Positioning + narrative simulation tying claims to audience state changes.

AI systems used: PTAH, SAMWISE (hidden self-deception in story), AI Faculty (narrative + strategy).

Prerequisites: AINS-M6002.

AINS-M6004 — Brand as Dynamic System¶

Description: Students model brand as state: awareness, association, trust, and elasticity under shocks. Brand is dynamic — it drifts with experience, scandal, and creative — not a font choice.

Key topics: Brand stocks and flows; creative fatigue; scandal shocks; sub-brands and architecture; measurement hooks.

Required artifact: Brand dynamic model with scenarios (creative refresh, PR shock, competitor move).

AI systems used: PTAH, SAMWISE, AI Faculty (a.Porter; reputation risk).

Prerequisites: AINS-M6003.

AINS-M6005 — Channel & Content Economics¶

Description: Students implement reach, cost, and conversion mechanics across channels and content types: CAC-like dynamics and creative throughput as constraints, not magic ROAS numbers. Tooling is explicit: students parameterize Meta (Facebook/Instagram), Google (Search / Ads / YouTube / GA4-style measurement), and Amazon (marketplace + Sponsored Products/Brands) as parallel channel modules—same ontology, different auction, attribution, and margin rules.

Key topics: Channel saturation; creative production as bottleneck; organic vs paid coupling; unit economics of content; social vs search vs retail economics; channel families (search, paid social, lifecycle email, partner, Amazon retail) as modeled modules; ethical tradeoffs (clickbait as system choice).

Required artifact: Channel + content economics simulation with scenario set that includes labeled scenarios for at least two of: social (Meta-class), Google Ads + analytics, Amazon marketplace/ads.

AI systems used: PTAH, SAMWISE (metric gaming), AI Faculty (growth vs brand tension). MCP: ingest sanitized exports or synthetic platform-shaped data where live APIs are unavailable.

Prerequisites: AINS-M6004.

AINS-M6006 — Measurement, Privacy & Compliance¶

Description: Students integrate measurement, consent, and compliance as rules in the world: what can be observed, what is inferred, and what is forbidden. GDPR/CCPA-style constraints as first-class objects, not footnotes. Platform policies (Meta, Google, Amazon ToS, ad policies, data use) enter as enforceable constraints on what tools may record or optimize.

Key topics: Event models; attribution limits; consent states; platform policy shocks; audit risk; documentation for defensibility; cross-platform measurement (e.g. GA4 + ad platforms + Amazon reporting) and identity / consent limits.

Required artifact: Constrained measurement world extending prior work with explicit constraint objects and scenario results.

AI systems used: PTAH, SAMWISE (what you ignored because it was inconvenient), AI Faculty (ethics + a.Taleb on fragility of centralized control).

Prerequisites: AINS-M6005.

TERM II — Campaign & Growth Worlds¶

Objective: Simulate campaigns, creative testing, and growth as systems with delays, budgets, and feedback.

AINS-M6101 — Campaign Orchestration¶

Description: Students model multi-touch campaigns as coordinated dynamics: frequency, sequencing, creative rotation, and budget pacing — orchestration, not a single ad set. Cross-ecosystem cases are required: at minimum one scenario spanning social (e.g. Meta-class) + Google (Search/YouTube/PMax-style) touchpoints, and one scenario that includes Amazon (marketplace or sponsored) or a faculty-approved retail media analog.

Key topics: Touchpoint graphs; diminishing returns; holdout logic; coordination failure; scenario libraries; budget split across Meta, Google, Amazon (or synthetic equivalents) with different latency and reporting shapes.

Required artifact: Campaign orchestration simulation with documented assumptions and failure scenarios; tool map appendix listing which platform families each touchpoint represents.

AI systems used: PTAH, SAMWISE, AI Faculty (integration vs local optimization).

Prerequisites: Term I sequence (AINS-M6006 or equivalent).

AINS-M6102 — Creative Testing at Scale¶

Description: Students build experiment systems: lift, variance, sample size, and when to stop — treating creative testing as a statistical world, not a dashboard.

Key topics: Hypothesis structure; MDE; peeking and multiple comparisons (conceptual + light implementation); creative variants as factors; AI-generated or AI-assisted creative as experimental factors with disclosure and QA; ethical A/B boundaries.

Required artifact: Experiment / lift model with evaluation methodology.

AI systems used: PTAH (simulation), SAMWISE, AI Faculty (behavioral + statistics discipline).

Prerequisites: AINS-M6101.

AINS-M6103 — Data-Driven Marketing & Attribution¶

Description: Students implement attribution and decision workflows grounded in data: retrieval + structured decisions, when data becomes leverage and when it becomes liability. Platform data shapes are explicit: students work with Google Ads / GA4-style, Meta Ads Manager–class, and Amazon Advertising / retail reporting limitations (latency, attribution windows, modeled conversions, retail last-touch bias)—not a single sanitized dashboard.

Key topics: Multi-touch attribution limits; incrementality concepts; triangulation across Google, Meta, and Amazon metrics; RAG-style knowledge for playbooks; evaluation harnesses for retrieval and LLM outputs (failure taxonomy, not only accuracy vibes); privacy hooks.

Required artifact: Data-driven world with decision workflow + evaluation methodology; must document how at least two ecosystems’ metrics disagree and what you trust under what assumption.

AI systems used: PTAH + data integration, SAMWISE, AI Faculty (a.Porter on sustained advantage from data).

Prerequisites: AINS-M6102.

AINS-M6104 — Partnerships & Ecosystem¶

Description: Students model partners, affiliates, and ecosystems as agents with incentives, leakage, and multi-sided dynamics.

Key topics: Incentive alignment; fraud and gaming; contracts as constraints; ecosystem maps into honest abstractions; B2B partner and co-sell patterns vs consumer affiliate dynamics where relevant.

Required artifact: Partner / ecosystem simulation with baseline and stress scenarios.

AI systems used: PTAH, SAMWISE, AI Faculty (power vs trust).

Prerequisites: AINS-M6103.

AINS-M6105 — Growth Loops & Community¶

Description: Students model loops and community as reinforced feedback: referrals, UGC, network effects where appropriate — separating compounding growth from burn. Social surfaces (organic Meta/short-form patterns, YouTube subs, Amazon reviews/Q&A as retail community) are first-class loop carriers where relevant.

Key topics: Loop structure; saturation; cohort vs aggregate metrics; community health; ethical boundaries; UGC and review dynamics (social + Amazon reputation) as measurable stocks.

Required artifact: Growth loop model with sensitivity analysis.

AI systems used: PTAH, SAMWISE, AI Faculty (demand + competitive response).

Prerequisites: AINS-M6104.

AINS-M6106 — Crisis & Reputation Stress Tests¶

Description: Students adversarially break reputation: negative virality, misinformation, policy shocks, and bad actors. Fragility analysis of brand and community.

Key topics: Stress vs optimization; tail risks; cascades; early warnings; mitigation without fantasy.

Required artifact: Adversarial reputation scenarios + fragility report.

AI systems used: PTAH, SAMWISE (blind spots), AI Faculty (a.Taleb primary).

Prerequisites: AINS-M6105.

TERM III — Strategic Marketing Worlds¶

Objective: Compete, allocate budget, automate responsibly, deploy with proof.

AINS-M6201 — Competitive Narrative Arena¶

Description: Students pit world models against each other: competing narratives, offers, and budgets. Strategic interaction as emergent behavior.

Key topics: Competitors as agents; differentiation; switching costs; information asymmetry; path dependence.

Required artifact: Competing narrative models (≥2 sides) with tournament or scenario analysis.

AI systems used: PTAH (arena), SAMWISE, AI Faculty (a.Porter primary).

Prerequisites: Term II sequence (AINS-M6106 or equivalent).

AINS-M6202 — Strategy as Marketing Policy¶

Description: Students encode strategy as policy: rules mapping states to actions under uncertainty (explore/exploit, robustness across scenarios).

Key topics: States, actions, rewards (fit-for-purpose); robust policies; multi-agent coupling.

Required artifact: Policy layer integrated with student world model.

AI systems used: PTAH, SAMWISE, AI Faculty (strategy + uncertainty).

Prerequisites: AINS-M6201.

AINS-M6203 — Budget & Portfolio Allocation¶

Description: Students simulate budget allocation across initiatives under constraints: liquidity, payback, risk of ruin, opportunity cost.

Key topics: Portfolio choice; correlated risks; governance of spend; link to growth and brand dynamics.

Required artifact: Budget simulation with decision log.

AI systems used: PTAH, SAMWISE, AI Faculty (a.Taleb on ruin).

Prerequisites: AINS-M6202.

AINS-M6204 — Autonomous Marketing Systems¶

Description: Students design self-running marketing subsystems with explicit human-in-the-loop boundaries, monitoring, rollback.

Key topics: Automation limits; monitoring; cost of maintenance; security/abuse basics; observability for AI steps (logs, rollback, escalation when model output fails QA).

Required artifact: Autonomous subsystem deployed with limits and monitoring plan.

AI systems used: PTAH orchestration, SAMWISE, AI Faculty (governance).

Prerequisites: AINS-M6203.

AINS-M6205 — Ethics, Influence & Control¶

Description: Students formalize alignment and governance for influence systems: stakeholder maps, harms, Goodhart dynamics, legible oversight.

Key topics: Measurement perversion; auditability; escalation; legal/ethical risk as scenario classes.

Required artifact: Governance model tied to deployed components.

AI systems used: SAMWISE (ethical probing), PTAH (scenarios), AI Faculty (multi-perspective).

Prerequisites: AINS-M6204.

AINS-M6206 — Magisterium Thesis (Marketing)¶

Description: Capstone: real system, simulation validation, measurable outcomes, defense-ready documentation.

Key topics: Integration across terms; validation methodology; deployment measurement; reproducibility.

Required artifact: Magisterium Thesis package: world model, simulation results, deployment, measurable outcome, evidence chain.

AI systems used: PTAH, SAMWISE (full reflection corpus), AI Faculty (panel review).

Prerequisites: AINS-M6205 and satisfactory prior artifacts per policy.

Legal Notice¶

“The Castalia Institute Magisterium confers proprietary credentials based on demonstrated work and evaluation. These credentials are not accredited academic degrees and do not confer professional licensure.”