Logo

New Matters and Trends in the Audiovisual Sector

Code: 105015
Credits: 6
2026/2027
Degree programme Type Course
Audiovisual Communication OP 3

Contact lecturer

Name :
Maria Teresa Soto Sanfiel
Email :
mariateresa.soto@uab.cat

Group languages

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

Prerequisites

To successfully complete the course Current Issues and Trends in the Audiovisual Industry, students are expected to possess the following knowledge and competencies:


  • Basic knowledge of the audiovisual industry: Students should have a sound understanding of the fundamental aspects of the audiovisual sector, including production, distribution, and consumption processes, as well as the legal and regulatory framework governing the industry. This prior knowledge provides the necessary foundation for engaging with the course materials, participating in class discussions, and critically analysing the technological, industrial, and cultural transformations shaping the audiovisual landscape.

  • Basic competencies in audiovisual production: Students are expected to have previous experience in the development of audiovisual content or projects. This background is essential for understanding the creative and industrial processes examined throughout the course and for contextualising the case studies and emerging trends discussed.

  • English language proficiency: As the course is taught entirely in English, students must possess sufficient proficiency in both spoken and written English. This includes the ability to follow lectures, actively participate in class discussions, engage with academic literature, and complete all coursework and assessments in English.


Objectives

The main objective of the course Current Issues and Trends in the Audiovisual Industry is for students to develop a comprehensive understanding of the key transformations currently reshaping the audiovisual sector. By the end of the course, students will be able to:


  • Understand the transforming audiovisual landscape: Develop a comprehensive and up-to-date understanding of the major forces reshaping the audiovisual industry, including technological innovation, evolving business models, changing content production practices, shifting audience consumption patterns, and the growing role of artificial intelligence in creative and industrial processes.


  • Analyse the dynamics of the audiovisual industry: Critically examine the relationships between production, representation, distribution, consumption, regulation, and technological innovation within the contemporary audiovisual ecosystem, identifying the key challenges and opportunities arising from digital transformation, artificial intelligence, and increasing societal demands for diversity and inclusion.


  • Develop AI literacy for the audiovisual industries: Acquire the knowledge and critical skills needed to understand the foundations, applications, and limitations of artificial intelligence in the audiovisual sector, including AI-assisted content creation and editing, workflow automation, recommendation systems, audience analytics, and their implications for media production, distribution, and consumption.


  • Evaluate emerging trends: Assess the impact of emerging technologies—particularly artificial intelligence—on audiovisual content, creative processes, production workflows, business models, professional practices, and audience experiences.


  • Analyse representation, diversity, and inclusion: Examine how audiovisual media construct representations of identity and diversity from an intersectional perspective, identifying the mechanisms through which media may reproduce or challenge stereotypes, prejudice, discrimination, and social inequalities. Students will also explore how responsible media representation can contribute to inclusion, prejudice reduction, social cohesion, and the public good.


  • Contextualise global and local realities: Understand how global trends shape audiovisual products and industries while recognising how they are adapted, negotiated, and transformed within specific regional contexts—particularly in Europe and Spain—taking into account local markets, cultural differences, regulatory frameworks, and audience practices.


  • Develop critical and analytical skills: Strengthen the ability to critically evaluate technological, industrial, and cultural developments, identify biases in artificial intelligence systems, algorithms, and media representations, and anticipate future transformations affecting the audiovisual sector.


  • Foster an ethical and socially responsible perspective: Critically discuss the ethical, legal, professional, and societal implications of artificial intelligence and other emerging technologies, as well as the ethical responsibilities associated with audiovisual representation. Particular attention will be given to issues such as synthetic media and deepfakes, copyright and image rights, transparency, algorithmic bias, privacy, labour transformation, and the role of audiovisual media in promoting diversity, inclusion, social justice, and the public good.


Learning outcomes

  • CM06 (To design audiovisual products in accordance with emerging audiovisual industry trends.) To design audiovisual products in accordance with emerging audiovisual industry trends.
  • CM08 (To interpret changes to the professional profiles that currently exist in the audiovisual system, avoiding the reproduction of gender inequalities.) To interpret changes to the professional profiles that currently exist in the audiovisual system, avoiding the reproduction of gender inequalities.
  • CM09 (To use information to assess audiovisual project feasibility.) To use information to assess audiovisual project feasibility.
  • SM13 (To conduct research with a view to obtaining information that may be useful in analysing the audiovisual industry and/or designing feasible audiovisual projects.) To conduct research with a view to obtaining information that may be useful in analysing the audiovisual industry and/or designing feasible audiovisual projects.

Contents

This course is designed to encourage students to critically reflect on the disruptive issues and emerging trends that are reshaping today's audiovisual landscape. Particular emphasis is placed on Artificial Intelligence (AI), from its fundamental principles to its advanced applications in content creation, production, distribution, consumption, and recommendation systems, with special attention devoted to the critical phenomenon of deepfakes.


The course also examines stereotypical representation and the industrial, cultural, and institutional factors that shape it, as well as the potential of audiovisual media to reduce prejudice and promote social inclusion through edutainment. In addition, it explores the legal and ethical dimensions of AI in Europe and Spain, the evolution of audiovisual formats and content (e.g., web series, digital audio, documentaries, and true crime), and the strategies of globalisation and localisation that underpin contemporary audiovisual production and distribution.

Learning activities and methodology

Title Hours ECTS Learning outcomes
Seminars 15 0.6
Lessons 33 1.32
Tutoring 9 0.36
Projects 81 3.24

The course combines lectures, case-based seminars, and practical activities designed to facilitate the application of the concepts and approaches covered throughout the course.


A detailed schedule outlining the topics for each session will be presented during the first class and made available on the Virtual Campus. The Virtual Campus will also provide the instrumental materials required for the course, together with information on assignments, practical activities, and assessment. The presentation slides used during class form part of the in-class learning experience and will not be uploaded to the Virtual Campus. The corresponding content is expected to be acquired through class attendance and engagement with the required readings and bibliography assigned to each topic.


The course will be taught entirely in English. However, selected readings, teaching materials, or audiovisual examples may be presented in Catalan and/or Spanish where appropriate.

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
Seminars 20% 6 0.24 CM06, CM08, CM09, SM13
Exam 30% 3 0.12 CM06, CM08, CM09, SM13
Practical exercises 50% 3 0.12 CM06, CM08, CM09, SM13

Continuous assessment

Assessment in this course is based on continuous evaluation. The final grade will be calculated as follows:


- Examination: 30%

- Seminars: 20%

- Practical assignments: 50%


The examination assesses each student's individual acquisition of the fundamental theoretical knowledge covered in the course. Seminars and practical assignments assess students' ability to apply acquired knowledge, develop critical analysis, solve problems relevant to the contemporary audiovisual industry, and participate actively in course activities.


Spelling and written expression will be penalised by 0.5 marks for each error in all assessment activities.


All assessment activities must be original. The use of generative artificial intelligence tools is permitted only as support for learning and the preparation of coursework. Under no circumstances may such tools replace students' own analysis, argumentation, critical reflection, or intellectual contribution.


Plagiarism, the undeclared or unauthorised use of artificial intelligence tools, or any other academic misconduct that may significantly affect the assessment of an activity will result in a mark of 0 for that activity, without prejudice to any disciplinary measures established by University regulations. Where multiple irregularities are identified across different assessment activities within the same course, the final course grade will be 0.


Reassessment for continous assessment

To be eligible for the resit assessment, students must meet all of the following requirements:


- Have been assessed in activities representing at least two-thirds (66.7%) of the final course grade.

- Have obtained a weighted average mark of at least 3.5 out of 10 across all assessment activities.

- Have obtained a minimum mark of 3.5 out of 10 in the examination.


For example, a student who obtains 3.8 in the examination, 5 in the seminars, and 4 in the practical assignments will be eligible for the resit because all three requirements have been met. Conversely, a student with a weighted average mark of 4.5 but only 2.8 in the examination will not be eligible because the minimum examination mark has not been achieved.


Seminars form part of the continuous assessment process and, due to their nature, are not eligible for resit. The resit assessment may apply only to the examination and/or the practical assignments, as determined by the lecturer.


Single assessment

The single assessment system consists of the following components:


- Theoretical examination: 30%

- Case-study or current communication challenges examination: 20%

- Critical review of the scientific literature: 25%

- Final audiovisual production project and oral defence: 25%


Description of the assessment activities for the single assessment pathway

The theoretical examination is an individual assessment designed to evaluate students' acquisition of the fundamental theoretical knowledge covered in the course. Its format differs from that used in the continuous assessment pathway.


The case-study examination consists of the individual resolution of case studies or current communication challenges related to the course contents. Its purpose is to assess students' ability to apply theoretical knowledge to situations relevant to the contemporary audiovisual industry. Cases will be provided by the lecturer. Students may use the course bibliography and, where appropriate, additional references drawn from scientific journal articles.


The critical review of the scientific literature consists of selecting three recent scientific articles published preferably in journals indexed in Web of Science or Scopus and, whenever possible, ranked in the Q1 or Q2 quartiles. The articles must address a current topic related to the course contents and learning outcomes, and the topic must be approved in advance by the lecturer.


Based on these articles, students must prepare a critical review of the scientific literature comparing the main contributions of the selected studies, identifying similarities and differences, analysing their methodological and theoretical strengths and limitations, and developing an original critical reflection on their contribution to knowledge and their relevance to the audiovisual industry. This review will provide the conceptual foundation for the audiovisual production project.


The final audiovisual production project and its oral defence consist of developing an audiovisual work that incorporates innovative techniques, technologies, or formats related to the course contents. The project must be grounded in the previously completed critical literature review and will be presented and defended orally. Detailed information regarding the project's requirements, format, and development process will be provided through the Virtual Campus.


Assessment will take into account both the quality of the project and the student's ability to justify the conceptual, methodological, and creative decisions made throughout its development and to respond appropriately to questions raised during the oral defence. To this end, the lecturer may ask questions concerning the literature review, the project's development process, and the decisions adopted in order to assess students' understanding of the course contents, their critical thinking skills, and the authorship of the submitted work.


Presentation of the final audiovisual production project and oral defense

The presentation and oral defence of the audiovisual project form part of the assessment and are compulsory.


Presentations will take place on the official dates established by the lecturer for the course assessment activities, coinciding with the presentation sessions scheduled for students following the continuous assessment pathway. Students following the single assessment pathway are required to attend all presentation sessions, as these constitute an academic activity involving learning, peer exchange, and collective assessment.


Assessement schedule

Students following the single assessment pathway must complete all assessment activities, including written examinations, submission of assignments, and oral presentations, on the official dates established by the lecturer for the course.


Failure to attend any assessment activity on the scheduled date will result in the loss of the ordinary assessment opportunity. Students will only be eligible for the official resit assessment if they satisfy the requirements established by University regulations.


Spelling and written expression will be penalised by 0.5 marks for each error.


All assessment activities must be original. The use of generative artificial intelligence tools is permitted only as support for learning and the preparation of coursework. Under no circumstances may such tools replace students' own analysis, argumentation, critical reflection, or intellectual contribution.


Plagiarism, the undeclared or unauthorised use of artificial intelligence tools, or any other academic misconduct that may significantly affect the assessment of an activity will result in a mark of 0 for that activity, without prejudice to any disciplinary measures established by University regulations. Where multiple irregularities are identified across different assessment activities within the same course, the final course grade will be 0.


Reassement for single assessment

To be eligible for the resit assessment, students must meet all of the following requirements:


- Have been assessed in activities representing at least two-thirds (66.7%) of the final course grade.

- Have obtained a weighted average mark of at least 3.5 out of 10 across all assessment activities.

- Have obtained a minimum mark of 3.5 out of 10 in the theoretical examination.


The theoretical examination is the only assessment activity eligible for resit. The resit will consist of a new written examination assessing the theoretical knowledge covered in the course.


The case-study examination, the critical review of the scientific literature, and the final audiovisual production project are not eligible for resit. The marks obtained in these activities will therefore be retained and incorporated into the final course grade according to their respective weighting.


Spelling and written expression will be penalised by 0.5 marks for each error.


If a student commits any irregularity that may significantly affect the assessment of an evaluation activity, that activity will be awarded a mark of 0, irrespective of any disciplinary proceedings that may be initiated. Where multiple irregularities are identified across different assessment activities within the same course, the final course grade will be 0.


Use of Artificial Intelligence

This course encourages the responsible use of Artificial Intelligence (AI) tools to support learning and the development of the audiovisual project. In particular, AI may be used for audiovisual content creation, the preparation of the final presentation, and the identification of documentary and academic sources.


The use of AI must be fully transparent. Students are required to state clearly which AI tools they have used, the specific tasks for which they were employed, and how they contributed to the final outcome of the project. In addition, they must include a brief critical reflection on the advantages, limitations, and impact of these tools on their creative and production process.


When AI is used to search for information or academic sources, students remain responsible for verifying the accuracy and reliability of the information obtained, as AI systems may generate errors, inaccurate information, or non-existent references.

AI should be understood as a tool that supports creativity and learning, not as a substitute for students’ own analysis, decision-making, critical thinking, or creativity. Assessment will place particular value on students’ intellectual contribution, the originality of their decisions, and the quality of their critical reflection.


Where there are indications of unauthorized AI use or any other form of academic misconduct, the instructor may request drafts, preparatory materials, or any other evidence demonstrating the authorship and development of the work. Students may also be required to explain or justify the decisions made during the project, either orally or in writing. Instructors may use AI detection systems or any other verification procedures they consider appropriate. If irregularities are identified following this review, the assignment may receive a grade of zero, without prejudice to any additional disciplinary measures established by University regulations.


Academic Misconduct

Any irregularity committed during an assessment activity (including academic fraud, plagiarism, or improper use of AI, unless such use is expressly authorized in this course guide) that may significantly affect the assessment outcome will result in a grade of zero for that assessment. Where the course guide establishes that obtaining a minimum mark in that assessment is a compulsory requirement for passing the course, or where multiple irregularities occur in assessment activities within the same course, the final course grade will be zero. In addition, disciplinary proceedings may be initiated against the student.


Bibliography

During the course, additional resources will be added to this bibliography:


  • Alanazi, S., Asif, S., Caird-daley, A., & Moulitsas, I. (2025). Unmasking deepfakes: A multidisciplinary examination of social impacts and regulatory responses. Human-Intelligent Systems Integration, 1(1-23).
  • Ching, D., Twomey, J., Aylett, M. P., Quayle, M., Linehan, C., & Murphy, G. (2025). Can deepfakes manipulate us? Assessing the evidence via a critical scoping review. PLoS One, 20(5), e0320124.
  • Fu, G. J., Soto-Sanfiel, M. T., & Saha, S. (2026). Resurrecting historical figures: The persuasive power of deepfakes versus text-based first-person narratives. Mass Communication and Society, 1–18. https://doi.org/10.1080/15205436.2026.2637155
  • Fu, G. J., Soto-Sanfiel, M. T., & Sánchez-Soriano, J.-J. (2026). The fuzzy mechanism of processing stereotyped gay characters in media. Poetics, 115–116, 102090. https://doi.org/10.1016/j.poetic.2026.102090
  • Gambín, Á. F., Yazidi, A., Vasilakos, A., et al. (2024). Deepfakes: Current and future trends. Artificial Intelligence Review, 57(64). https://doi.org/10.1007/s10462-023-10679-x
  • Montoya-Bermúdez, D. F., & Soto-Sanfiel, M. T. (2023). The production of web series: Amateurs vs. professionals. International Journal on Media Management, 25(3–4), 166–183. https://doi.org/10.1080/14241277.2024.2386676
  • Ramírez-Correa, P., Grandón, E. E., & Mariano, A. M. (2026). Mapping the landscape of generative artificial intelligence literacy: A systematic review toward social, ethical, and sustainable AI adoption. Sustainability, 18(3), 1429. https://doi.org/10.3390/su18031429
  • Rodríguez-de-Dios, I., Blanco-Fernández, V., & Soto-Sanfiel, M. T. (2026). Audiovisual fiction to reduce prejudices against non-binary people. Media Psychology, 1–27. https://doi.org/10.1080/15213269.2026.2614632
  • Rodríguez-de-Dios, I., & Soto-Sanfiel, M. T. (2024). Reducing transphobia with the narratives of transgender YouTubers. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 18(5), Article 2. https://doi.org/10.5817/CP2024-5-2
  • Sánchez-Soriano, J. J. (2023). La representación LGTB+ en las series españolas de ficción. El espejo en que nos miramos. Comunicación Social.
  • Sánchez-Soriano, J. J., & Jiménez, L. G. (2020). La construcción mediática del colectivo LGTB+ en el cine blockbuster de Hollywood. El uso del pinkwashing y el queerbaiting. Revista Latina deComunicación Social, (77), 95-116.
  • Soto-Sanfiel, M. T., Angulo-Brunet, A., & Saha, S. (2025). Deepfakes as narratives: Psychological processes explaining their reception. Computers in Human Behavior, 165, 108518. https://doi.org/10.1016/j.chb.2024.108518
  • Soto-Sanfiel, M.T., Angulo-Brunet, A., & Saha, S. (2025). Motivations for (not) sharing deepfakes on social networks. The Communication Review, 1-26. https://doi.org/10.1080/10714421.2025.2499348
  • Soto-Sanfiel, M. T., Angulo-Brunet, A., Sánchez-Soriano, J. J., et al. (2025). Responses of low-prejudice viewers to lesbian stereotypes in fiction. Archives of Sexual Behavior, 54, 4239–4257. https://doi.org/10.1007/s10508-025-03323-y
  • Soto-Sanfiel, M. T., & Fu, G. J. (2026). Deepfaking the past: Memory and perceived truth of resurrected historical figures. Computers in Human Behavior, 182, 109008. https://doi.org/10.1016/j.chb.2026.109008
  • Soto-Sanfiel, M. T., Hussain, S. A., & Saha, S. (2026). Nostalgic appeal in prosocial deepfakes of deceased and living artists: Effects on emotional engagement and credibility. Behaviour & Information Technology, 1–23. https://doi.org/10.1080/0144929X.2026.2641599
  • Soto-Sanfiel, M.T. & Montoya-Bermúdez, D.F. (2024). Explaining aversion to true crime documentaries: Why do audiences refuse to watch them? Studies in Documentary Film, 18(2), 130-146. https://doi.org/10.1080/17503280.2023.2295040
  • Soto-Sanfiel, M. T., & Montoya-Bermúdez, D. F. (2023). Consumption of true crimes and perceived vulnerability: Does the cultural context matter? International Communication Gazette, 85(7), 560-579. https://doi.org/10.1177/17480485221131474
  • Soto-Sanfiel, M.T. & Villegas-Simón, I. (2024). Scriptwriters’ conceptions of audience attitudes toward LGBTQ+ characters Mass Communication & Society, 27(5), 1252-1276. https://doi.org/10.1080/15205436.2023.2292123
  • Soto-Sanfiel, M.T., Angulo-Brunet, A. & Lutz, C. (2024). The scale of Artificial Intelligence Literacy for all (SAIL4ALL): A tool for assessing knowledge on artificial intelligence in all adult populations and settings. ArXiv.org. https://doi.org/10.31235/osf.io/bvyku
  • Soto-Sanfiel, M. T., & Wu, Q. (2026). How audiences make sense of deepfake resurrections: A multilevel analysis of realism, ethics, and cultural meaning. Computers in Human Behavior, 174, 108822. https://doi.org/10.1016/j.chb.2025.108822
  • Villegas-Simón, I., & Soto-Sanfiel, M.T. (2024, online). The concept of normalization in the production of LGBTIQ+ media imaginaries: the scriptwriters’ conceptions. Journal of Communication, 74(3), 237-248. https://doi.org/10.1093/joc/jqae014
  • Villegas-Simon, I., & Soto-Sanfiel, M. T. (2021). Adaptation of scripted television formats: Factors and mechanisms of cultural identity in a global world. International Journal of Communication, 15, 17. https://ijoc.org/index.php/ijoc/article/view/16569
  • Villegas-Simon, I., & Soto-Sanfiel, M. T. (2021). Similarities in adaptations of scripted television formats: The global and the local in transnational television culture. Poetics, 86, 101524. https://doi.org/10.1016/j.poetic.2020.101524
  • West, R., & Beck, C. S. (Eds.). (2025). Communication, entertainment, and messages of social justice. Routledge. https://doi.org/10.4324/9781003493952
  • Zhang, B., Cui, H., Nguyen, V., & Whitty, M. (2025). Audio deepfake detection: What has been achieved and what lies ahead. Sensors (Basel, Switzerland), 25(7), 1989. https://pmc.ncbi.nlm.nih.gov/articles/PMC11991371/

Software

In this course, students are free to use the software that best suits their needs and technical abilities. If working with specific software is proposed, it will always be free, open-source options.

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
(TE) Theory 4 English first semester morning-mixed
(SEM) Seminars 41 English first semester morning-mixed