AI applied to social networks

Description:

This course introduces students to the strategic and practical application of artificial intelligence in the planning, production, distribution and optimisation of social media content. It combines contextual analysis, strategic thinking, experimentation with AI tools and a critical approach to the limits, risks and responsibilities associated with their use.

Throughout the term, students will work in teams to develop a social media strategy for a brand or project, integrating AI into research, audience definition, content creation, workflow automation and performance analysis. The course is designed for a 14-week on-campus format with 42 contact hours and a strong emphasis on applied learning and decision-making.

Previous Knowledge:

Students are expected to have basic knowledge of social media, digital communication and content strategy.
They should also understand fundamental concepts related to audiences, brands, digital platforms and the use of data in decision-making.

Objectives:

To understand the strategic role of artificial intelligence in the planning, production, distribution and optimisation of social media content.
To apply AI tools to audience research, content creation, workflow automation and social media performance analysis.
To design a social media strategy for a brand or project by integrating artificial intelligence into the different stages of the decision-making process.
To critically assess the limitations, risks and ethical responsibilities associated with the use of artificial intelligence in social media environments.

Contents:

Unit 1. Context and foundations of AI for social media: core concepts, platform evolution and impact on content creation and consumption.

Unit 2. Strategic foundations of social media: internal and external analysis, benchmarking, audience definition, positioning and content territories.

Unit 3. Content creation with AI: ideation, copywriting, visual and audiovisual production, and brand consistency.

Unit 4. Content systems and planning: editorial calendars, format reuse, scalable processes and workflow organisation.

Unit 5. Distribution and growth: algorithms, formats, trends, publishing logic and reach optimisation.

Unit 6. Analysis and optimisation: key metrics, data interpretation, iterative learning and AI-supported decision-making.

Unit 7. Automation and efficiency: process automation, community management and digital workflow design.

Unit 8. Ethics, limits and risks: authorship, authenticity, technological dependency, bias and basic regulation.

Methodology:

The methodology combines theory-practice sessions, guided demonstrations, AI tool workshops, real case analysis, debates and project tutorials. A single applied project acts as the backbone of the course and is developed iteratively throughout the term.

Training activities include short faculty inputs, in-class practical exercises, benchmark and profile audits, prompt and workflow development, interim reviews of deliverables and oral presentations. Active participation, critical reflection and the ability to justify every AI-supported decision are expected.

Evaluation:

Assessment is continuous and values both the final outcome and the quality of the working process, the strategic justification of decisions and the responsible integration of AI.

To pass the subject, students must obtain a minimum final grade of 5.0/10 and at least 5.0/10 in the final project. Activities not submitted by the stated deadline will be recorded as not submitted, unless there is a justified reason in line with School regulations.

In accordance with School academic regulations, the subject includes highly significant and moderately significant assessment activities depending on their weighting, compulsory nature and impact on the final grade.

The extraordinary resit, if applicable and subject to academic coordination, will consist of revising or resubmitting the evidence indicated by the lecturer. Ongoing class participation and process-based activities cannot be fully recreated after the term and therefore have limited recoverability.

The School's coexistence, academic honesty and integrity regulations apply to all course activities. Institutional reference documents include the Copies Regulation (https://www.salleurl.edu/en/copies-regulation) and the academic regulations page with the campus coexistence framework (https://www.salleurl.edu/es/estudios/masters-y-postgrados/informacion-academica/normativa-academica). Any improper use of sources, digital tools or automated assistants may lead to the review or invalidation of the task.

Use of AI tools: If AI tools are used in any activity, students must include a paragraph stating the purpose for which AI was used, the tools employed and the prompts or instructions used to obtain the result. Failure to do so constitutes a breach of academic honesty policies.

Assessment activity	Weight	Description
Final AI-enabled social media project	40%	Design, implementation and optimisation of a social media strategy for a brand or project.
Interim submissions	30%	Milestones covering research, strategy, content, editorial planning and automation design.
Oral project defence	20%	Structured presentation and defence of the project's decisions, tools and results.
Participation and in-class work	10%	Active attendance, workshop contribution, practical exercises, peer feedback and engagement.

Evaluation Criteria:

This course may allow a specific level of AI use. For further information, please consult the professor:

Level 1: AI is not permitted.
Level 2: AI is permitted only for ideation and planning.
Level 3: AI is permitted only for formal review and improvement.
Level 4: AI is permitted for specific tasks, with critical analysis and mandatory citation.
Level 5: AI is permitted throughout the entire process.

The AI Assessment Scale (AIAS) is designed as a flexible framework that helps students and teaching staff understand what use of AI is permitted in each activity and what level of academic responsibility is associated with each level.

Basic Bibliography:

Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95. https://doi.org/10.1007/s11747-019-00695-1?

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42. https://doi.org/10.1007/s11747-019-00696-0?

Dwivedi, Y. K., Kshetri, N., Hughes, L., Ribeiro-Navarrete, S., Boylan, A. K., Firth-McGuckin, S., ... & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642?

Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925

Hootsuite. (2026). Social Trends Report 2026. Hootsuite Media Inc.

McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey Global Institute.

We Are Social & Meltwater. (2026). Digital 2026: Global Overview Report. https://wearesocial.com/

Orlowski, J. (Director). (2020). The Social Dilemma [Documental]. Exposure Labs; Netflix.

Additional Material: