Call for Proposals: VI MeLCi Lab Autumn School 2026

Researchers in the field of Communication and Media Studies are currently facing a structural tension. The Advanced School on AI Research Practices in Media and Communication Studies will take place online from 10–13 November 2026, organised by the CICANT – Centre for Research in Applied Communication, Culture, and New Technologies, in collaboration with MeLCi Lab, AISIC and InTouch Labs at Lusófona University.

Artificial intelligence (AI) — particularly Large Language Models (LLMs) — has become an integral part of the research process, serving as a tool for tasks such as literature reviews, data annotation, audience segmentation and discourse analysis.

At the same time, AI has also become an object of research in its own right: a force reshaping civic cultures, media ecologies and the conditions under which public spheres are formed.

These two roles require distinct skill sets. Using AI as a research method demands technical knowledge, prompt engineering skills and validation protocols. Studying AI as a social phenomenon requires critical frameworks drawn from political theory, media literacy and the ethics of datafication.

Most training programmes address only one of these dimensions. This school seeks to address both while exploring the tensions that exist between them.

The VI MeLCi Lab Autumn School invites PhD candidates, postdoctoral researchers and early-career researchers to apply for a four-day intensive online programme.

The school combines plenary lectures with hands-on workshops organised around two complementary themes. Participants will work with datasets specific to media and communication research, address interpretative challenges characteristic of the field — such as bias in content classification, the instability of AI-generated annotations and the opacity of recommendation systems — while simultaneously developing the technical and critical skills required in today's research landscape.

No prior experience in artificial intelligence or data science is required. Introductory modules will provide all the essential foundational knowledge.

Theme 1: AI in Research Practice: Foundations, Methods and Ethics

• Artificial intelligence tools have been incorporated into research workflows at a pace that has outstripped the development of methodological standards capable of regulating their use. Zero-shot and few-shot prompting techniques now enable researchers without computational backgrounds to perform tasks that previously required supervised classifiers or teams of human coders (Gilardi et al., 2023; Grossmann et al., 2023; Ziems et al., 2024);
• This accessibility presents genuine opportunities but also introduces significant risks: prompt instability, the opaque behaviour of models and the absence of agreed standards for reproducibility mean that convenience may come at the expense of methodological rigour (Barrie et al., 2025);
• This theme equips participants with the methodological foundations, practical skills and ethical guidance required to use AI tools rigorously and responsibly.

1.1 Foundations of Contemporary AI Tools

• Large Language Models have fundamentally transformed what is possible in text-based research. Prompting techniques that require no training data can now achieve annotation accuracy comparable to — and in some cases exceeding — that of human experts;
• However, the same flexibility that makes LLMs accessible also makes them fragile: even minor changes to prompt wording can substantially alter results, undermining replicability;
• This subtheme explores the theoretical architecture of contemporary AI tools, the methodological principles that underpin their responsible use and emerging best practices for transparent and accountable application in Communication research.

1.2 Responsible Literature Reviews Using AI Tools

• AI-powered platforms such as SciSpace and Litmaps have significantly accelerated literature reviews, enabling researchers to map citation networks, identify thematic clusters and locate relevant publications at a speed unattainable through manual searching;
• However, these efficiency gains also introduce new methodological responsibilities. AI-assisted searches may silently exclude relevant literature, favour particular databases or appear comprehensive while in fact offering only partial coverage;
• This subtheme develops strategies for validating AI-generated search results, assessing the limitations of bibliographic coverage and maintaining transparent documentation practices essential for methodological rigour.

1.3 AI-Assisted Data Annotation in Research

• Data annotation is a central stage in most empirical research. Traditionally performed exclusively by human coders, it can now be carried out using AI, offering a viable and often highly effective alternative, particularly for large-scale studies;
• The principal challenge lies in ensuring consistency. Barrie et al. (2025) demonstrate that prompt stability — the extent to which semantically equivalent prompts generate equivalent annotations — remains a major source of variability;
• This subtheme introduces participants to AI-assisted annotation workflows, focusing on practical approaches to evaluating and improving annotation reliability through methodologies such as Prompt Stability Scoring (PSS), while integrating responsible validation practices into research design.

Theme 2: Communication, Audiences and Civic Cultures in the Age of AI

• AI is not only reshaping how researchers work. It is also transforming the media environments they study. Algorithmic recommendation systems determine what audiences see, platform architectures mediate how citizens participate, and the datafication of everyday life raises questions about equity, inclusion and democratic participation that existing frameworks still struggle to address;
• This theme approaches AI not as a methodological tool but as a structural force within media ecologies — one that requires a critical perspective from researchers studying communication, audiences and civic cultures.

2.1 Civic Cultures and Artificial Intelligence

• AI-driven platforms and recommendation algorithms now mediate core dimensions of civic life: how citizens access information, how activist networks emerge and how media literacy is either strengthened or weakened;
• This subtheme examines the opportunities and challenges AI introduces to civic participation, exploring how algorithmic mediation reshapes the conditions under which citizens engage in democratic processes.

2.2 Digital Citizenship and Media Literacy in an AI-Mediated World

• The competencies required for informed participation in AI-mediated environments remain insufficiently defined. Critical media literacy now includes skills that existing frameworks have yet to fully systematise: recognising AI-generated content, understanding how recommendation systems shape information exposure and evaluating the epistemic status of machine-generated content;
• This subtheme examines what digital citizenship demands in an environment characterised by disinformation, deepfakes and opaque algorithmic curation.

2.3 Data Ethics, Equity and Inclusion in AI Research

• AI technologies embed biases originating from their training data, system design choices and the contexts in which they are deployed. The ethical implications of using these tools in knowledge production — who is represented, which categories are imposed and which communities bear the risks of misclassification — remain insufficiently explored;
• This theme moves beyond viewing AI either as a technological panacea or an existential threat. Instead, it focuses on responsible research practices, equitable research design and the specific responsibilities of researchers working with data from, or concerning, underrepresented communities.

Application Information

Application deadline: 15 September 2026
Notification of acceptance: 12 October 2026
Registration deadline: 28 October 2026

Applicants should submit their application in English by 15 September 2026, including:

• 1. An up-to-date curriculum vitae (maximum 3 pages);
• 2. A research statement outlining the applicant's doctoral thesis or current research project, including research questions and methods (maximum 2 pages);
• 3. A motivation letter describing the applicant's current engagement with AI, specific interests or concerns regarding the role of AI in media research and practice, and their preferred theme (maximum 2 pages).

Applications should be submitted as a single ZIP file to melci.lab@ulusofona.pt with the subject line: "Application for the VI MeLCi Lab Autumn School".

The school will be held online and in English.

For further information, please contact: melci.lab@ulusofona.pt