[Commlist] Call for Proposals: VI MeLCi Lab Autumn School 2026

Call for Proposals

VI MeLCi Lab Autumn School 2026

Advanced School on AI Research Practice in Media and Communication

10–13 November 2026 | Online

Organised by CICANT:  MeLCi Lab, AISIC, and InTouch Labs | Lusófona
University, Portugal


Website:
https://melcilab.cicant.ulusofona.pt/training/vi-melci-lab-autumn-school-2026-advanced-school-on-ai-research-practice-in-media-and-communication/

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Researchers in communication and media studies now face a structural
tension. Artificial intelligence - particularly large language models - has
entered the research pipeline as a tool for applications such as literature
search, data annotation, audience segmentation, and discourse analysis. At
the same time, AI has become an object of inquiry: a force reshaping civic
cultures, media ecologies, and the conditions under which publics form.
These two roles demand different competencies. Using AI as a method
requires technical skill, prompt design, and validation protocols. Studying
AI as a societal force requires critical frameworks drawn from political
theory, media literacy, and the ethics of datafication. Most training
programmes address one side or the other. This school addresses both and
the friction between them.

The VI MeLCi Lab Autumn School invites applications from PhD students,
postdoctoral researchers, and early-career scholars for a four-day
intensive online programme. The school combines keynote lectures with
hands-on workshops, structured around two complementary themes.
Participants will work with media-specific datasets, confront the
interpretative challenges particular to communication research, like bias
in content classification, the instability of AI-generated annotations, and
the opacity of recommendation systems, and develop both the technical and
critical capacities the current research landscape requires.

No prior experience with AI or data science is assumed. Introductory
modules provide the necessary foundations.

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

AI tools have entered research workflows faster than the methodological
standards needed to govern their use. Zero- and few-shot prompting now
enables researchers with no computational training 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). The
accessibility is genuine. So are the risks: prompt instability, opaque
model behaviour, and the absence of agreed reproducibility standards mean
that convenience can outpace accountability (Barrie et al., 2025). This
theme equips participants with the methodological foundations, practical
skills, and ethical orientation to use AI tools rigorously.

1.1 Foundations of Current AI Tools

Large language models have transformed what is computationally tractable in
text-based research. Prompting techniques that require no training data
have achieved annotation accuracy comparable to - and in some cases
exceeding - expert human coders. But the same flexibility that makes LLMs
accessible also makes them fragile: minor prompt adjustments can shift
outputs in ways that compromise replicability. This sub-track addresses the
theoretical architecture of contemporary AI tools, the methodological
principles governing their responsible use, and the best practices emerging
for transparent, accountable deployment in communication research.

1.2 Accountable Literature Search Using AI Tools

AI-powered platforms such as SciSpace and Litmaps have accelerated
literature discovery, enabling researchers to map citation networks,
identify thematic clusters, and surface relevant work at a pace that manual
search cannot match. The efficiency gain, however, introduces a new
accountability burden. AI-assisted searches can silently exclude relevant
literature, privilege certain databases, or present coverage as
comprehensive when it is partial. This sub-track develops strategies for
validating AI-generated search results, assessing coverage boundaries, and
maintaining the transparent documentation practices that methodological
rigour demands.

1.3 AI-Assisted Data Annotation in Research Pipelines

Data annotation anchors most empirical research pipelines. Where this task
once relied exclusively on human coders, AI-based annotation now offers a
viable and often highly effective alternative - particularly at scale. The
central challenge is consistency. Barrie et al. (2025) demonstrate that
prompt stability, i.e., the degree to which semantically equivalent prompts
produce equivalent annotations, remains a significant source of
variability. This sub-track introduces participants to AI-driven annotation
workflows, focusing on practical approaches to assessing and improving
annotation reliability through frameworks such as Prompt Stability Scoring
(PSS) and integrating responsible validation practices into research design.


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

AI does not only reshape how researchers work. It reshapes the media
environments researchers study. Algorithmic recommendation determines what
the public sees, platform architectures mediate how citizens engage, and
the datafication of everyday life raises questions about equity, inclusion,
and democratic participation that existing frameworks struggle to answer.
This theme addresses AI not as a methodological resource but as a
structural force within media ecologies - one that demands critical
engagement from researchers who study 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 encounter information, how activist
networks form, and how media literacy is exercised or undermined (Sarafis
et al., 2025). This sub-track examines the opportunities and challenges AI
introduces for civic engagement, exploring how algorithmic mediation
reconfigures the conditions under which publics participate 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 poorly defined. Critical media literacy now extends to
skills that existing frameworks have not yet systematised: recognising
AI-generated content, understanding how recommendation systems shape
information exposure, and assessing the epistemic status of
machine-produced outputs (Chiu et al., 2024). This sub-track examines what
digital citizenship demands in an environment shaped by misinformation,
deepfakes, and opaque algorithmic curation.

2.3 Data Ethics, Equity, and Inclusivity in AI Research

AI technologies carry biases embedded in their training data, design
choices, and deployment contexts. The ethical implications of using these
tools for knowledge production: who is represented, whose categories are
imposed, and whose communities bear the risks of misclassification, remain
insufficiently examined (Ferrara, 2024; Ntoutsi et al., 2020). This theme
moves beyond the binary framing of AI as either a technological panacea or
an existential threat. It addresses responsible research practice,
equitable research design, and the specific obligations researchers hold
when working with data from or about underrepresented communities.


Application Details

Deadline for submission: 15 September 2026

Notification of acceptance: 12 October 2026

Registration deadline: 28 October 2026

Interested participants should submit their application (in English) by 15
September 2026, including:

1. An updated curriculum vitae (max. 3 pages)

2. A research statement describing their doctoral dissertation or current
research project, including research questions and methods (max. 2 pages)

3. A motivation letter describing their current engagement with AI,
specific concerns or interests regarding AI's role in media research and
practice, and their preferred theme (max. 2 pages)

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

The school will be conducted online and in English.

For enquiries, please contact: (melci.lab /at/ ulusofona.pt)

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References

Barrie, C., Palaiologou, E., & Törnberg, P. (2024). Prompt stability
scoring for text annotation with large language models. arXiv preprint
arXiv:2407.02039. https://doi.org/10.48550/arXiv.2407.02039

Chiu, T. K., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are
artificial intelligence literacy and competency? A comprehensive framework
to support them. Computers and Education Open, 6, 100171.
https://doi.org/10.1016/j.caeo.2024.100171

Ferrara, E. (2024). Fairness and bias in artificial intelligence: A brief
survey of sources, impacts, and mitigation strategies. Sci, 6(1), 3.
https://doi.org/10.3390/sci6010003

Gilardi, F., Alizadeh, M., & Kubli, M. (2023). ChatGPT outperforms crowd
workers for text-annotation tasks. Proceedings of the National Academy of
Sciences, 120(30), e2305016120. https://doi.org/10.1073/pnas.2305016120

Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P.
E., & Cunningham, W. A. (2023). AI and the transformation of social science
research. Science, 380(6650), 1108–1109.
https://doi.org/10.1126/science.adi1778

Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal,
M., ... & Staab, S. (2020). Bias in data-driven artificial intelligence
systems — An introductory survey. Wiley Interdisciplinary Reviews: Data
Mining and Knowledge Discovery, 10(3). https://doi.org/10.1002/widm.1356

Sarafis, D., Karamitsios, K., & Kravari, K. (2025). AI and civic
engagement: A brief exploration of applications and opportunities. 2025
International Conference on Advancement in Data Science, E-learning and
Information System (ICADEIS), 1–6.
https://doi.org/10.1109/icadeis65852.2025.10933183

Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024).
Can large language models transform computational social science?
Computational Linguistics, 50(1), 237–291.

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