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Workshop on Journalistic Production

Code: 43966
Credits: 9
2026/2027
Degree programme Type Course
Journalism and Digital Content Innovation OB 1

Contact lecturer

Name :
Pedro Vicente Ortin Andres
Email :
pedrovicente.ortin@uab.cat

Teaching staff

Carlos Sanandres Martinez

Group languages

You can consult this information at the end of the document.

Prerequisites

COURSE DESCRIPTION

The Journalism Production Workshop constitutes the practical core of the Master's Degree in Journalism and Innovation in Digital Content.

Unlike courses primarily devoted to theoretical analysis, this workshop operates as a live newsroom and media innovation laboratory, where students integrate the knowledge acquired throughout the Master's programme by designing, producing, publishing and evaluating journalistic content within realistic professional environments.

The course is built upon a fundamental principle:

Journalism is learned by practising journalism.

However, contemporary journalistic practice extends far beyond writing, filming or editing. It requires understanding the complete life cycle of information within a rapidly evolving digital ecosystem shaped by artificial intelligence, algorithmic platforms, audience fragmentation, automation, creator economies and new forms of civic participation.

Accordingly, the workshop offers an immersive learning experience that combines professional practice with critical inquiry.

Throughout the academic year, students will investigate emerging journalistic practices, experiment with innovative storytelling formats, develop editorial strategies and create digital products designed for contemporary media environments.

Rather than reproducing traditional newsroom routines, the course encourages students to imagine and prototype the newsrooms of the future.

The classroom therefore becomes a collaborative production space where ideas evolve through continuous cycles of observation, research, design, experimentation, production, evaluation and iteration.

Artificial intelligence, automation, data journalism, audience analytics, OSINT methodologies, computational verification and editorial product design are integrated throughout the course not merely as technical resources but also as subjects of critical reflection regarding their ethical, social and democratic implications.

Ultimately, the course seeks to educate journalists capable not only of producing high-quality reporting but also of leading innovation processes, designing sustainable editorial products, creating new narrative forms and critically engaging with the transformations currently reshaping journalism worldwide.



COURSE CONTEXT

Journalism is experiencing one of the most profound transformations in its modern history.

The consolidation of digital platforms, the rapid development of generative artificial intelligence, the automation of editorial processes, the fragmentation of audiences, the emergence of creator-driven media and the acceleration of information cycles have fundamentally redefined journalistic practices, media organisations and relationships between news producers and the public.

Within this context, journalism education must move beyond models centred exclusively on the transmission of knowledge and embrace learning environments based on experimentation, collaborative production and applied research.

The Journalism Production Workshop is situated precisely at this intersection between university education, professional practice and media innovation.

Throughout the course, students develop complete journalistic projects, from the identification of information needs and editorial opportunities to the production, publication, distribution and evaluation of digital content.

The workshop functions as a space where research, creativity, technological experimentation and editorial production converge.

Its pedagogical approach draws inspiration from international media innovation laboratories, journalism research centres and university programmes dedicated to editorial product development, combining methodologies such as project-based learning, user-centred design, design thinking and collaborative innovation.

Particular attention is devoted to the ethical dimensions of technological transformation.

The incorporation of artificial intelligence, algorithmic systems and automated editorial workflows requires continuous reflection on transparency, accountability, editorial responsibility, verification, data governance and democratic values.

Consequently, the workshop aims not only to prepare students for today's professional media landscape but also to equip them to shape the future of journalism through critical thinking, responsible innovation and meaningful public engagement.


PREREQUISITES

Students are expected to possess prior knowledge of:

  • Fundamentals of journalism and digital communication.
  • Professional news production workflows.
  • Contemporary journalistic storytelling.
  • Basic multimedia production (text, photography, audio and video).
  • Digital publishing platforms and content management systems.
  • Audiovisual editing and graphic design tools.
  • Basic research methodologies.

The course is nevertheless designed to accommodate students from diverse communication backgrounds, encouraging collaborative learning and the progressive acquisition of technical, editorial and strategic competences.

Students are expected to demonstrate:

  • intellectual curiosity;
  • initiative;
  • collaborative working skills;
  • openness to experimentation;
  • professional commitment;
  • willingness to engage actively in collective production processes.


Objectives

General Objective

To develop advanced competences for designing, producing, publishing and evaluating innovative journalistic projects through laboratory-based methodologies integrating digital storytelling, audiovisual production, artificial intelligence, editorial product design and collaborative professional practice.


Specific Objectives

By the end of the course, students will be able to:

  • integrate the knowledge acquired throughout the Master's programme into authentic journalistic production environments;
  • design editorial projects adapted to diverse audiences, platforms and media ecosystems;
  • experiment with innovative storytelling formats, audiovisual languages and transmedia narratives;
  • apply agile methodologies to the design and development of journalistic products;
  • incorporate artificial intelligence critically, transparently and responsibly into research, reporting, production and editorial workflows;
  • analyse digital communities, audiences and platforms using analytics and engagement metrics;
  • develop leadership, coordination and collaborative project management skills;
  • design editorial strategies aimed at innovation, sustainability and public value;
  • critically evaluate the social, cultural, technological and ethical implications of journalistic innovation.


Transversal Learning Objectives

The course also seeks to enable students to:

  • strengthen autonomous learning and lifelong professional development;
  • cultivate critical thinking regarding technological and media transformations;
  • integrate gender, diversity and inclusion perspectives into journalistic practice;
  • understand innovation as a continuous process of research, experimentation and reflection;
  • reinforce journalism's democratic role and commitment to the public interest;
  • collaborate effectively within interdisciplinary and multicultural teams while developing communication, negotiation and leadership skills.


Learning outcomes

  1. Analyse and evaluate trends in digital narration in news companies and apply innovative alternatives within a specific product.
  2. Understand and critically interpret the changes introduced by technology in journalism production and management.
  3. Understand the process for solving a research problem, identifying original ideas and integrating previous proposals.
  4. Understand and evaluate the potential of the different formats of digital content and narratives within an environment that is interactive, hypertextual and transmedia.
  5. Appropriately communicate the findings and the fundamental rationale of the research work conducted.
  6. Develop a research project that uses the scientific method in solving a particular problem in the area of journalism and digital communication.
  7. Recognise and decide on the possibilities and formats of digital narration, adapting to specific formats and audiences.
  8. Identify trends in digital content and recognise the characteristics of the new professional environments related to digital news.
  9. Identify the different formats and platforms for distributing and sharing content, adapting the message in an innovative way.
  10. Handle technological tools for managing and producing digital news content, integrating them into new content-distribution platforms
  11. Organise, analyse and evaluate information from audience measurement systems to propose content and creative methods for producing and positioning news.
  12. Present the news products arising from a specific journalism production routine, clearly and attractively.
  13. Present results of the work done to corporate-sector audiences and the target audience, dynamically and clearly.
  14. Make critical analyses of documentation, bibliography and audiovisual information based on case studies put forward.
  15. Conduct applied research on the market for digital content targeting social networks.
  16. Recognise the new professional profiles in journalism and their roles in news companies' innovation models and journalistic products.
  17. Recognise and formulate applied-research problems together with suitable methods for solving them.
  18. Make innovative, creative and responsible decisions when conducting research for the master's dissertation.
  19. Work independently and with self-discipline, under the specific guidance of a tutor, in a competitive workplace.

Contents

Course contents are organised into five interconnected thematic modules developed progressively throughout the academic year.

Rather than isolated units, these modules interact continuously through laboratory activities, collaborative projects and editorial production.


Module 1

Liquid Newsrooms: Organising Journalism in the Twenty-First Century

How do contemporary newsrooms work?

Topics include:

  • The evolution from traditional newsrooms to distributed editorial teams.
  • Emerging professional profiles.
  • Product management in journalism.
  • Creator economy and independent media.
  • Community-centred journalism.
  • Editorial leadership.
  • Media innovation laboratories.
  • Organisational cultures for innovation.

International case studies include:

  • AJ+
  • Rest of World
  • ProPublica
  • The Markup
  • Correctiv
  • Maldita
  • La Pulla
  • Fumaça
  • Disclose
  • Zetland
  • Tortoise Media

Students analyse not only editorial outputs but also organisational models, workflows and innovation cultures.


Module 2

Creative Architectures and Collaborative Editorial Design

How do innovation teams organise creative work?

Students explore collaborative methodologies including:

  • Design Thinking;
  • Agile Journalism;
  • Scrum;
  • Editorial Sprint;
  • Lean Startup;
  • Product Thinking;
  • Human-Centred Design;
  • Collaborative Documentation;
  • Editorial Knowledge Management.

Digital collaboration platforms include:

  • Notion;
  • Trello;
  • Airtable;
  • Miro;
  • Figma;
  • Canva;
  • Slack;
  • GitHub.

Students learn to document decisions, coordinate multidisciplinary teams and develop transparent editorial workflows.


Module 3

Digital Storytelling and Artificial Intelligence

This module examines how emerging technologies are reshaping journalistic practice.

Topics include:

  • Mobile-first storytelling;
  • Vertical video journalism;
  • Short-form documentary;
  • Social storytelling;
  • Podcasts;
  • Newsletters;
  • Interactive narratives;
  • AI-assisted journalism;
  • Prompt engineering;
  • Editorial automation;
  • Synthetic media;
  • Generative visual production;
  • Human-AI collaboration.

Special attention is devoted to:

  • transparency;
  • accountability;
  • editorial responsibility;
  • algorithmic bias;
  • copyright;
  • verification;
  • human oversight.

Artificial intelligence is approached not as a substitute for journalism but as an editorial technology requiring critical understanding.


Module 4

Active Media Laboratories

The workshop is structured around specialised laboratories.

These include:

  • Audiovisual Lab;
  • Artificial Intelligence Lab;
  • Data Journalism Lab;
  • OSINT Lab;
  • Social Media Lab;
  • Podcast Lab;
  • Newsletter Lab;
  • Visual Storytelling Lab;
  • Audience Strategy Lab;
  • Editorial Product Lab.

Each laboratory follows the same learning cycle:

Inspiration → Demonstration → Experimentation → Production → Critical Review → Iteration

Students progressively integrate techniques acquired across laboratories into increasingly complex editorial projects.


Module 5

Critical Production: From Editorial Idea to Public Impact

The final module integrates everything learned throughout the course.

Students explore questions such as:

  • How can journalism remain relevant in saturated information environments?
  • How can editorial products create meaningful relationships with communities?
  • How can journalistic innovation contribute to democratic life?

Projects incorporate:

  • editorial strategy;
  • audience development;
  • verification;
  • visual communication;
  • sound design;
  • AI integration;
  • impact evaluation;
  • sustainability planning.

The final project must demonstrate the integration of:

  • research;
  • editorial design;
  • journalistic production;
  • innovation methodology;
  • technological competence;
  • ethical reflection;
  • collaborative practice.


Learning activities and methodology

Title Hours ECTS Learning outcomes
Search, selection and reading of bibliography and other resources. Personal study. Planning and individual work / teamwork. 113 4.52 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
Lectures 12 0.48 1, 2, 3, 4, 6, 7, 8, 11, 14, 16, 18
Tutoring and workshops 45 1.8 1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
Laboratory activities 55 2.2 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19

The Journalism Production Workshop adopts a teaching methodology based on active learning, applied research, and collaborative production. The course is developed as a Media Innovation Lab, where the classroom is transformed into an open newsroom, a space for experimentation, and a permanent laboratory for developing journalistic projects.

Teaching combines processes of critical analysis, technological experimentation, and editorial production with the goal of having students learn through action, reflection, and iteration.

Learning is built through real projects, open challenges, and problem-solving processes linked to contemporary professional situations.

Sessions combine different types of activities:

inspiration and contextualization sessions;

critical discussion seminars;

hands-on labs;

production workshops;

case studies;

individual and group tutorials;

editorial reviews;

public presentations;

peer feedback sessions.

The course incorporates methodologies from innovation and product design:

Project-Based Learning (PBL).

Challenge-Based Learning.

Learning by Doing.

Design Thinking.

Agile Project Management.

Editorial Sprint.


Knowledge production is based on a process of observation, experimentation, production, evaluation, and improvement.

Faculty primarily assume the role of mentor, editor, and facilitator of the processes.

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
A) Attendance and participation in classes, debates and presentations 30% 0 0 1, 2, 4, 7, 8, 9, 10, 14, 16, 17, 18
B) Practical activities 50% 0 0 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
C) Submission and presentation of assignments 20% 0 0 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19

The subject follows a model of continuous evaluation, consistent with the practical and experimental nature of the workshop.

The evaluation aims to assess both final results and work processes, research capacity, active participation, individual evolution and contribution to collaborative work.

Attendance and active participation in laboratory activities constitute an essential requirement for the correct development of the subject.


Evaluation activities

A. Participation, involvement and laboratory work

30 %

It will be valued:

active participation;

professional attitude;

initiative;

ability to work in a team;

involvement in laboratories;

contribution to collaborative processes;

critical capacity;

quality of feedback offered to colleagues.


B. Practical projects

50 %

It includes the set of pieces, exercises, laboratories and prototypes developed during the course.

They will be especially valued:

journalistic quality;

innovation;

rigor;

originality;

methodological strength;

narrative quality;

adequate use of digital technologies;

Critical integration of artificial intelligence.


C. Public presentation of the projects

20 %

Each team will publicly present their final project.

It will be valued:

capacity for synthesis;

argumentative quality;

oral presentation;

methodological justification;

viability of the project;

ability to respond during the debate.


EVALUATION CRITERIA

To pass the subject it will be necessary:

having participated in the set of compulsory activities;

submit all assessment activities;

obtain a minimum grade of 5 out of 10 in each of the evaluable blocks;

demonstrate effective participation in collaborative work.

In the projects developed as a team, teachers will be able to establish specific mechanisms for individual monitoring.

The individual grade may differ from the overall grade of the group when justified by the effective contribution of each student.


RECOVERY

Students will have the right to the recovery process in accordance with current academic regulations of the Autonomous University of Barcelona.

Students who have been evaluated in activities that represent at least two thirds of the final grade will be eligible for recovery.

The activities linked to the continuous participation in the laboratories and the monitoring of the work process will not be recoverable, given its formative and face-to-face nature.


USE OF GENERATIVE ARTIFICIAL INTELLIGENCE

Generative artificial intelligence is part of both the contents and the methodologies of the subject.

Students will be able to use artificial intelligence tools in documentation, research, ideation, analysis, production, editing and project development processes.

Its use must respect the principles of:

transparency;

authoring;

verification;

professional responsibility;

academic integrity.

When using artificial intelligence tools, the student must indicate:

the tools used;

their purpose;

the relevant prompts or instructions when relevant;

the degree of human intervention;

the incorporated modifications;

a critical reflection on the advantages, limitations and possible biases detected.

In no case may AI replace editorial responsibility, information verification or critical thinking.

The undeclared use of AI systems when it is relevant for the development of an activity may be considered an academic irregularity, in accordance with the regulations of the Autonomous University of Barcelona.


ACADEMIC INTEGRITY AND PLAGIARISM

Academic integrity is a fundamental principle of the subject.

Any action that involves:

plagiarism;

copy;

FORGERY;

data manipulation;

impersonation of identity;

unauthorized use of materials;

concealment of the use of artificial intelligence;

any other practice aimed at obtaining an improper advantage in the evaluation processes.

In accordance with current regulations of the Autonomous University of Barcelona, these actions may lead to a grade of 0 in the corresponding activity or, where appropriate, the suspension of the subject, without prejudice to the disciplinary measures that may arise from it.


GENDER PERSPECTIVE, DIVERSITY AND INCLUSION

The gender perspective constitutes a transversal axis of the subject.

The contents, case studies, activities and projects will incorporate a critical look at the structural inequalities present in the media systems and in the production of knowledge.

It will be encouraged:

the diversity of sources;

the plural representation of groups;

the use of an inclusive language;

the incorporation of various authorships into the bibliography;

critical analysis of the biases present in algorithms and artificial intelligence systems.

The subject will promote a journalistic practice committed to human rights, equality, inclusion and social justice.


ACCESSIBILITY, SUSTAINABILITY AND DIGITAL RESPONSIBILITY

The projects developed throughout the course will incorporate criteria of accessibility, sustainability and technological responsibility.

It will be encouraged:

universal design;

the accessibility of content;

the protection of personal data;

digital security;

responsible use of technologies;

reflection on the environmental impact associated with digital production and artificial intelligence systems.

Bibliography

Core Bibliography

Anderson, C. W., Bell, E., & Shirky, C. (2012). Post-industrial journalism: Adapting to the present. Tow Center for Digital Journalism, Columbia Journalism School.

Beckett, C. (2019). New powers, new responsibilities: A global survey of journalism and artificial intelligence. Polis, London School of Economics and Political Science.

Bell, E., Owen, T., Brown, P. D., Hauka, C., & Rashidian, N. (2017). The platform press: How Silicon Valley reengineered journalism. Tow Center for Digital Journalism, Columbia Journalism School.

Boczkowski, P. J., & Mitchelstein, E. (2021). The digital environment: How we live, learn, work, and play now. MIT Press.

Bro, P. (2018). Models of journalism. Routledge.

Broussard, M. (2023). More than a glitch: Confronting race, gender, and ability bias in tech. MIT Press.

Deuze, M., & Witschge, T. (2020). Beyond journalism. Polity Press.

Diakopoulos, N. (2019). Automating the news: How algorithms are rewriting the media. Harvard University Press.

Harney, S., & Moten, F. (2013). The undercommons: Fugitive planning & Black study. Minor Compositions.

Jarvis, J. (2023). The Gutenberg parenthesis: The age of print and its lessons for the age of the internet. Bloomsbury Publishing.

Nielsen, R. K., & Ganter, S. A. (2022). The power of platforms: Shaping media and society. Oxford University Press.

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.

Ortín, P. (2025). Periodismo Dadá. Jot Down Books.

Ortín, P., & Esono Ebalé, N. (2022). Diez mil elefantes. Reservoir Books.

Petre, C. (2021). All the news that’s fit to click: How metrics are transforming the work of journalists. Princeton University Press.

Rincón, O. (2006). Narrativas mediáticas: O cómo se cuenta la sociedad del entretenimiento. Gedisa.

Ripley, A. (2021). High conflict: Why we get trapped and how we get out. Simon & Schuster.

Rogers, S. (Ed.). (2019). The data journalism handbook: Towards a critical data practice. Amsterdam University Press.

Usher, N. (2021). News for the rich, white, and blue: How place and power distort American journalism. Columbia University Press.

Zelizer, B., Boczkowski, P. J., & Anderson, C. W. (2022). The journalism manifesto. Polity Press.


20.2 Complementary Bibliography

Ananny, M. (2018). Networked press freedom: Creating infrastructures for a public right to hear. MIT Press.

Boczkowski, P. J. (2010). News at work: Imitation in an age of information abundance. University of Chicago Press.

Bruns, A. (2018). Gatewatching and news curation: Journalism, social media, and the public sphere. Peter Lang.

Bucher, T. (2018). If... then: Algorithmic power and politics. Oxford University Press.

Carlson, M. (2015). The robotic reporter: Automated journalism and the redefinition of labor, compositional forms, and journalistic authority. Digital Journalism, 3(3), 416–431. https://doi.org/10.1080/21670811.2014.976412

Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.

Crusafon Baques, C., González-Saavedra, C., & Murciano Martínez, M. (2020). Las redes sociales y las aplicaciones móviles en las estrategias de transformación digital de los medios de servicio público europeos. Comunicació: Revista de Recerca i Anàlisi, 37(2), 33–54. https://doi.org/10.2436/20.3008.01.195

Díaz-Noci, J. (2021). The life of news and the wealth of media companies in the digital world: Reader revenues and professional practices on a post-COVID world. DigiDoc Research Group, Universitat Pompeu Fabra.

Fletcher, R., & Nielsen, R. K. (2017). Paying for online news: A comparative analysis of six countries. Digital Journalism, 5(9), 1173–1191. https://doi.org/10.1080/21670811.2016.1246373

García-Avilés, J. A., Carvajal-Prieto, M., Arias, F., & De Lara-González, A. (2018). How journalists innovate in the newsroom: Proposing a model of the diffusion of innovations in media outlets. The Journal of Media Innovations, 5(1), 1–16. https://doi.org/10.5617/jomi.v5i1.3968

Gillespie, T. (2018). Custodians of the internet: Platforms, content moderation, and the hidden decisions that shape social media. Yale University Press.

Gray, J., Bounegru, L., & Chambers, L. (Eds.). (2012). The data journalism handbook. O’Reilly Media.

Howard, A. B. (2014). The art and science of data-driven journalism. Tow Center for Digital Journalism, Columbia Journalism School.

Kuiken, J., Schuth, A., Spitters, M., & Marx, M. (2017). Effective headlines of newspaper articles in a digital environment. Digital Journalism, 5(10), 1300–1314. https://doi.org/10.1080/21670811.2017.1279978

Kruikemeier, S., & Lecheler, S. (2018). News consumer perceptions of new journalistic sourcing techniques. Journalism Studies, 19(5), 632–649. https://doi.org/10.1080/1461670X.2016.1192956

Lewis, S. C., Guzman, A. L., & Schmidt, T. R. (2019). Automation, journalism, and human–machine communication: Rethinking roles and relationships of humans and machines in news. Digital Journalism, 7(4), 409–427. https://doi.org/10.1080/21670811.2019.1577147

Napoli, P. M. (2019). Social media and the public interest: Media regulation in the disinformation age. Columbia University Press.

Nelson, J. L. (2021). Imagined audiences: How journalists perceive and pursue the public. Oxford University Press.

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.

Ortín, P., & Pereiro, V. (2006). Mbini: Cazadores de imágenes en la Guinea colonial. Llibreria Altaïr.

Papacharissi, Z. (2015). Affective publics: Sentiment, technology, and politics. Oxford University Press.

Pariser, E. (2011). The filter bubble: What the internet is hiding from you. Penguin Press.

Peres-Neto, L. (2022). Journalist-Twitterers as political influencers in Brazil: Narratives and disputes towards a new intermediary model. Media and Communication, 10(3), 28–38. https://doi.org/10.17645/mac.v10i3.5363

Perreault, G., & Stanfield, K. (2019). Mobile journalism as lifestyle journalism? Field theory in the integration of mobile in the newsroom and mobile journalist role conception. Journalism Practice, 13(3), 331–348. https://doi.org/10.1080/17512786.2018.1424021

Rincón, O. (Ed.). (2008). Los tele-presidentes: Cerca del pueblo, lejos de la democracia. Friedrich-Ebert-Stiftung.

Rincón, O. (Ed.). (2010). ¿Por qué nos odian tanto? Estado y medios de comunicación en América Latina. Friedrich-Ebert-Stiftung.

Simelio, N., Ginesta, X., San Eugenio Vela, J., & Corcoy, M. (2019). Journalism, transparency and citizen participation: A methodological tool to evaluate information published on municipal websites. Information, Communication & Society, 22(3), 369–385. https://doi.org/10.1080/1369118X.2017.1386706

Tejedor, S. (2022). Artificial intelligence and newsgames in journalism: Proposals and ideas from the case study of three projects. Visual Review, 12(3), 1–8. https://doi.org/10.37467/revvisual.v9.3749

Tejedor, S., Cervi, L., Pulido, C. M., & Pérez Tornero, J. M. (2021). Análisis de la integración de sistemas inteligentes de alertas y automatización de contenidos en cuatro cibermedios. Estudios sobre el Mensaje Periodístico, 27(3), 973–983. https://doi.org/10.5209/esmp.77003

Usher, N. (2014). Making news at The New York Times. University of Michigan Press.

Wahl-Jorgensen, K. (2019). Emotions, media and politics. Polity Press.

Wardle, C., & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe.

20.3 Strategic Reports

Beckett, C., & Yaseen, M. (2023). Generating change: A global survey of what news organisations are doing with artificial intelligence. JournalismAI, Polis, London School of Economics and Political Science.

Egan, J., Robertson, C. T., Ross Arguedas, A., Newman, N., Nielsen, R. K., Mukherjee, M., & Fletcher, R. (2026). Reuters Institute digital news report 2026. Reuters Institute for the Study of Journalism, University of Oxford.

Maslej, N., Fattorini, L., Perrault, R., Gil, Y., Parli, V., Kariuki, N., Capstick, E., Reuel, A., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Niebles, J. C., Shoham, Y., Wald, R., Walsh, T., Hamrah, A., Santarlasci, L., Betts Lotufo, J., Rome, A., Shi, A., & Oak, S. (2025). The AI Index 2025 annual report. AI Index Steering Committee, Stanford Institute for Human-Centered Artificial Intelligence, Stanford University.

Mozilla Foundation. (2024). AI & news futures: Risks and opportunities for public interest journalism. Mozilla Foundation.

Newman, N. (2024). Journalism, media, and technology trends and predictions 2024. Reuters Institute for the Study of Journalism, University of Oxford.

Newman, N. (2025). Journalism, media, and technology trends and predictions 2025. Reuters Institute for the Study of Journalism, University of Oxford.

Newman, N. (2026). Journalism, media, and technology trends and predictions 2026. Reuters Institute for the Study of Journalism, University of Oxford.

Newman, N., Fletcher, R., Robertson, C. T., Ross Arguedas, A., & Nielsen, R. K. (2024). Reuters Institute digital news report 2024. Reuters Institute for the Study of Journalism, University of Oxford.

Newman, N., Ross Arguedas, A., Robertson, C. T., Nielsen, R. K., & Fletcher, R. (2025). Reuters Institute digital news report 2025. Reuters Institute for the Study of Journalism, University of Oxford.

Sajadieh, S., Fattorini, L., Perrault, R., & AI Index Steering Committee. (2026). The AI Index 2026 annual report. Stanford Institute for Human-Centered Artificial Intelligence, Stanford University.

UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.

World Economic Forum. (2025). Global risks report 2025. World Economic Forum.

21. DIGITAL RESOURCES AND OBSERVATORIES

AlgorithmWatch. (s. f.). AlgorithmWatch. https://algorithmwatch.org

Bellingcat. (s. f.). Bellingcat. https://www.bellingcat.com

Data Journalism. (s. f.). DataJournalism.com. https://datajournalism.com

Datawrapper. (s. f.). Datawrapper Academy. https://academy.datawrapper.de

European Data Journalism Network. (s. f.). European Data Journalism Network. https://www.europeandatajournalism.eu

First Draft. (s. f.). First Draft archive. https://firstdraftnews.org

Global Investigative Journalism Network. (s. f.). GIJN. https://gijn.org

JournalismAI. (s. f.). JournalismAI. https://www.journalismai.info

Maldita.es. (s. f.). Maldita.es. https://maldita.es

Nieman Foundation for Journalism. (s. f.). Nieman Lab. https://www.niemanlab.org

Partnership on AI. (s. f.). Partnership on AI. https://partnershiponai.org

Poynter Institute. (s. f.). Poynter. https://www.poynter.org

Reuters Institute for the Study of Journalism. (s. f.). Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk

Stanford Institute for Human-Centered Artificial Intelligence. (s. f.). AI Index. https://hai.stanford.edu/ai-index

The Markup. (s. f.). The Markup. https://themarkup.org

Verification Handbook. (s. f.). Verification handbook. https://verificationhandbook.com


22. REFERENCE NEWS ORGANISATIONS AND MEDIA LABS

AJ+. (s. f.). AJ+. https://www.ajplus.net

Brut. (s. f.). Brut. https://www.brut.media

CUNY Newmark Graduate School of Journalism. (s. f.). Craig Newmark Graduate School of Journalism at CUNY. https://www.journalism.cuny.edu

Disclose. (s. f.). Disclose. https://disclose.ngo

El Hilo. (s. f.). El Hilo. https://elhilo.audio

El Orden Mundial. (s. f.). El Orden Mundial. https://elordenmundial.com

Fumaça. (s. f.). Fumaça. https://fumaca.pt

La Pulla. (s. f.). La Pulla. https://www.youtube.com/@LaPulla

Media Innovation Studio. (s. f.). Media Innovation Studio. https://www.mediainnovationstudio.org

ProPublica. (s. f.). ProPublica. https://www.propublica.org

Rest of World. (s. f.). Rest of World. https://restofworld.org

The Bureau of Investigative Journalism. (s. f.). The Bureau of Investigative Journalism. https://www.thebureauinvestigates.com

Tow Center for Digital Journalism. (s. f.). Tow Center for Digital Journalism. https://towcenter.columbia.edu

23. RECOMMENDED SOFTWARE

Students will work with professional tools commonly used across contemporary newsrooms and innovation laboratories.

Artificial Intelligence

  • ChatGPT
  • Claude
  • Gemini
  • Perplexity
  • NotebookLM
  • Elicit
  • Consensus
  • Scite


Audiovisual Production

  • DaVinci Resolve
  • Adobe Premiere Pro
  • Adobe After Effects
  • CapCut
  • Descript
  • OBS Studio
  • Runway
  • ElevenLabs
  • Adobe Audition
  • Audacity


Design and Visual Communication

  • Figma
  • Canva
  • Adobe Express
  • Flourish
  • Datawrapper
  • RAWGraphs
  • Tableau Public


Data and Verification

  • OpenRefine
  • Google Dataset Search
  • Google Trends
  • Wayback Machine
  • TinEye
  • InVID
  • Google Fact Check Explorer
  • Jupyter Notebook


Collaboration and Project Management

  • Notion
  • Trello
  • Airtable
  • Slack
  • Miro
  • Microsoft Teams
  • GitHub
  • Google Workspace
  • Substack
  • WordPress


Software

The course runs throughout the entire academic year and is organised as a continuous Media Innovation Laboratory.

Its pedagogical structure follows a progressive model that combines professional practice, applied research and editorial product development.

Each semester has its own learning objectives while contributing to the same overall project.

The first semester focuses on journalistic production, audiovisual storytelling and experimentation with digital narratives.

The second semester evolves into a collaborative innovation laboratory dedicated to designing, developing and validating original journalistic products.

Throughout the year, the classroom operates as a live newsroom, where students assume different professional roles and participate in authentic editorial workflows including research, planning, production, editing, publication, evaluation and strategic decision-making.


FIRST SEMESTER

Audiovisual Innovation and Digital Storytelling

Central Question

How do today's most innovative news organisations and content creators produce journalism for digital audiences?

The first semester focuses on strengthening students' abilities to produce high-quality journalistic content while experimenting with the languages, aesthetics and formats that define contemporary digital communication.

Students combine individual production with collaborative newsroom dynamics, simulating the daily routines of multidisciplinary editorial teams.

The semester aims to build a professional digital portfolio that demonstrates each student's capacity to create rigorous, innovative and platform-specific journalistic work.

Learning Objectives

During the first semester students will learn to:

  • understand contemporary digital storytelling practices;
  • produce audiovisual journalism for social media platforms;
  • experiment with transmedia narratives;
  • integrate artificial intelligence into editorial workflows;
  • understand the logic and culture of digital platforms;
  • analyse audiences, communities and engagement metrics;
  • develop an individual editorial voice and professional identity.


Practical Laboratories

The semester is organised around a series of specialised production laboratories.

Audiovisual Storytelling Lab

Students will produce:

  • vertical video journalism;
  • short documentaries;
  • mobile reporting;
  • social video;
  • reels;
  • Shorts;
  • visual explainers.


Social Media Lab

Content production for:

  • Instagram
  • TikTok
  • YouTube
  • LinkedIn
  • Newsletters
  • Telegram
  • WhatsApp Channels

Students will analyse platform logics, distribution strategies and audience engagement.


Artificial Intelligence Lab

Practical applications include:

  • AI-assisted research;
  • prompt engineering;
  • content generation;
  • voice synthesis;
  • audiovisual production;
  • editorial automation;
  • AI-supported creative workflows.

Special emphasis is placed on transparency, editorial responsibility and critical evaluation.


Audience and Analytics Lab

Students will work with:

  • audience analytics;
  • SEO principles;
  • engagement metrics;
  • audience development;
  • community building;
  • editorial distribution strategies.


First Semester Final Project

Each student will develop an individual Digital Journalism Portfolio consisting of a curated selection of multimedia projects produced throughout the semester. The portfolio will demonstrate technical competence, editorial judgement, creativity and critical reflection. It will also serve as the foundation for the collaborative innovation project developed during the second semester.


SECOND SEMESTER

Media Innovation Lab: Designing the Future of Journalism

Central Question

How can we imagine, design, prototype and validate the journalistic products that do not yet exist?

During the second semester, the course evolves from a production workshop into a fully operational Media Innovation Lab. Students move beyond producing individual journalistic pieces to collaboratively designing complete editorial products that respond to real information needs, emerging technologies and evolving audience behaviours.

Working in multidisciplinary teams, students operate as independent editorial laboratories, assuming professional roles such as editor, producer, product manager, audience strategist, data journalist, visual storyteller, AI specialist or innovation lead.

The objective is not simply to create content, but to investigate social challenges, identify editorial opportunities, prototype innovative solutions and evaluate their potential impact.

The laboratory reproduces the working dynamics of leading international media innovation centres, combining journalism with product thinking, design methodologies, audience research and rapid experimentation.

Rather than asking "How do we tell stories?", the laboratory asks:

"What kind of journalism does society need next?"


Learning Objectives of the Second Semester

By the end of this phase, students will be able to:

  • identify opportunities for editorial innovation;
  • conduct user-centred research and audience discovery;
  • define clear editorial value propositions;
  • design sustainable journalistic products;
  • prototype and iterate innovative media experiences;
  • evaluate projects using qualitative and quantitative evidence;
  • collaborate effectively within multidisciplinary teams;
  • publicly present professional editorial proposals.


The Innovation Process

The laboratory follows five iterative phases inspired by international innovation methodologies.


Phase 1 — Observation and Research

Every project begins with understanding reality before attempting to transform it.

Students investigate contemporary information ecosystems through:

  • audience observation;
  • ethnographic research;
  • platform analysis;
  • media ecosystem mapping;
  • trend analysis;
  • interviews;
  • benchmarking;
  • documentary research;
  • data analysis;
  • OSINT investigations.

The objective is to identify meaningful journalistic problems rather than immediately proposing solutions.

Students learn to recognise information gaps, underserved communities and emerging editorial opportunities.

Phase 2 — Ideation

Once research has been completed, teams generate and evaluate possible editorial responses.

The course introduces innovation methodologies commonly used in media organisations and creative industries, including:

  • Design Thinking;
  • Double Diamond Framework;
  • Editorial Sprint;
  • Design Sprint;
  • Lean Startup;
  • Product Thinking;
  • Editorial Canvas;
  • User Journey Mapping;
  • Jobs-to-be-Done.

Students define:

  • target audiences;
  • editorial mission;
  • value proposition;
  • content strategy;
  • distribution channels;
  • community strategy;
  • technological requirements;
  • sustainability model.


Phase 3 — Prototyping

Ideas become tangible editorial products.

Each team develops a Minimum Viable Product (MVP) capable of being tested with real users.

Possible prototypes include:

  • newsletters;
  • podcasts;
  • investigative platforms;
  • audiovisual formats;
  • documentary projects;
  • AI-assisted editorial tools;
  • mobile journalism products;
  • social media ecosystems;
  • verification platforms;
  • interactive storytelling experiences;
  • data journalism projects;
  • community-based journalism initiatives.

Each prototype should integrate:

  • editorial architecture;
  • visual identity;
  • narrative design;
  • publishing workflow;
  • audience strategy;
  • technological implementation.


Phase 4 — Testing and Validation

Innovation requires evidence.

Projects are evaluated through iterative testing processes involving:

  • user interviews;
  • usability testing;
  • editorial reviews;
  • peer feedback;
  • audience analytics;
  • engagement metrics;
  • observational research.

Students learn to revise hypotheses, improve prototypes and document every iteration.

Failure is treated as valuable information rather than as a negative outcome.


Phase 5 — Public Presentation

The course concludes with a professional presentation of each project.

Teams present:

  • the identified problem;
  • research methodology;
  • editorial concept;
  • prototype;
  • innovation process;
  • ethical considerations;
  • audience strategy;
  • future development roadmap.

Presentations follow the format of professional editorial pitches commonly used in innovation labs, accelerators and media incubators.

Whenever possible, projects may be presented to invited journalists, editors, media entrepreneurs and researchers.

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TEm) Theory (master) 60 Spanish annual morning-mixed