Course 2026-2027 a.y.

The course is structured around progressive modules that guide students from an understanding of AI cognitive architectures through to the ethical, regulatory, and strategic implications for contemporary marketing. The curriculum integrates theory and practice through in-class behavioral experiments, AI lab sessions, business cases, and a final field project set in a real-world context.

It opens with the cognitive architecture of AI systems (neural networks, transformers, LLMs) and the behavioral biases — training, inferential, and deployment — these generate for marketing practice. It then examines the psychology of human-machine interaction: anthropomorphism, trust, automation bias, algorithmic deference, and parasocial relationships with AI agents. A third area addresses algorithmic influence on consumer behavior, covering recommendation systems, preference formation, personalization as choice architecture, and the impact of LLM-based search on the consumer journey. The course then turns to persuasion, attention, and manipulation, critically analysing dark patterns, deceptive AI, persuasion design, and engagement optimization systems — drawing a clear line between legitimate influence and manipulation. Topics related to AI brand identity and co-creation follow, including emotional AI, affective computing, conversational branding, virtual influencers, and consumer identity. The course concludes with ethics and regulation: algorithmic fairness, marketing bias, and the EU AI Act.

• The cognitive architectures of major AI systems (neural networks, transformers, LLMs) and their behavioral implications for marketing
• The main categories of algorithmic bias — training, inferential, and deployment — and their effects on campaigns, recommendations, and pricing
• The core psychological mechanisms governing human-machine interaction: anthropomorphism, trust, automation bias, algorithmic deference, and parasocial relationships
• How recommendation systems and algorithmic personalization shape consumer preferences and choice architecture
• How LLM-based search is transforming the consumer journey and information discovery
• The conceptual distinction between legitimate persuasion and manipulative design, including dark patterns and deceptive AI practices
• The principles of emotional AI, affective computing, and conversational branding
• The European regulatory framework for AI (EU AI Act) and its professional implications for marketers

• Identify and critically assess algorithmic biases in real marketing systems and evaluate their impact on campaign outcomes
• Apply psychological theories of trust, persuasion, and identity to the analysis of AI-powered marketing tools
• Design brand experiences and conversational touchpoints that leverage human-machine psychology in an ethically responsible way
• Distinguish legitimate persuasion from manipulation when evaluating engagement optimization systems and persuasion design
• Develop a structured AI Audit Report on a real organization, including an analytical framework and strategic recommendations
• Navigate the EU AI Act and assess its implications for specific marketing roles and organizational contexts

• Lectures
• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Collaborative Works / Assignments

Practical exercises (In-class behavioral experiments and AI Lab): "In-class behavioral experiments" are replicable experiments conducted in the classroom — adaptations of classics from social and cognitive psychology (e.g. anthropomorphism measures, automation bias tests) applied to AI contexts. Results are analyzed and discussed collectively. "AI Lab" are hands-on practical sessions in which students interact directly with AI systems to observe their behavior, probe their limits, and produce structured analyses. No programming experience is required.

Case discussions: Real-world cases of companies that have navigated challenges related to human-machine psychology in marketing. The approach is non-prescriptive: the goal is to develop critical thinking skills, not to identify the "right answer."

Guest speakers: The course also features guest speakers from companies active in AI marketing (identities communicated in advance).

Collaborative Works Assignments: The field project consists of a critical analysis of an AI system operating in a marketing context (chatbot, recommender engine, pricing algorithm, or AI influencer), combined with a psychological assessment of its customer touchpoints and a set of strategic recommendations.

In this course, the use of generative AI is not only permitted but actively encouraged as a learning tool. The goal is to develop the critical capacity to work both with and against AI systems — not to avoid them.

The specific rules for each assessment are as follows:

• Field Project: unrestricted use, but not for the critical evaluation — that must reflect the student's own judgment. It is a mandatory requirement to document how and where AI was employed (AI methodology appendix required).
• Written exam: conducted in class without digital tools: AI is NOT permitted. The exam assesses depth of understanding, not the ability to query an LLM.

ATTENDING

• Field project
• Individual written exam
• Class participation

The field project is carried out in teams and involves applying the analytical models learned in the course.

The written exam consists of open-ended questions, multiple-choice/multiple-answer questions, and exercises.

Peer evaluation is optional and limited to only teams that require it.

NON ATTENDING

Individual written exam.

The assessment method for non-attending students is based on a final written exam. It will consist of several open-ended, multiple-choice, multiple-answer, and short case questions that refer to the concepts, models, and cases in the textbooks and other materials available on the learning platform. The open-ended and multiple-choice questions are mainly aimed at verifying the learning of analytical and management abilities and their correct comprehension. The short cases are used to assess students' ability to apply the knowledge they learned from the course material.

1. Articles included in the course reserve;
2. Slides and notes from the instructor.

1. Louwerse Max M., 2025, Understanding Artificial Minds through Human Minds, Routledge;
2. Articles included in the course reserve;
3. Slides and notes from the instructor.