[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/ 
<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) <mailto:(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) 
<mailto:(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 
<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 
<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 <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 
<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 
<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 <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 
<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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