Teaching Portfolio

My teaching covers a wide range of topics at the intersection of journalism, data analysis, digital investigation and artificial intelligence. I combine theoretical foundations, methodological rigour and extensive hands-on training. A particular focus of my work concerns disinformation, verification, fact-checking and the ethical challenges introduced by new technologies. Below is an overview of the main courses, guest lectures and masterclasses I deliver. Laurence Dierickx

BA & Master’s Programme (Lectures and Workshops)

Format: 2-hour lecture + 1-day workshop
This module introduces the principles of verification, fact-checking and digital investigation. The lecture covers search engine logic, information retrieval strategies, and the strengths and limitations of online investigative methods. The workshop builds on these foundations through practical OSINT exercises, enabling students to apply investigative techniques through real-world scenarios.

2. Introduction to Data Journalism

A comprehensive course introducing journalism students to data-driven storytelling.

Part 1 – Theoretical Foundations (4 hours)
History of data journalism, ethical and legal considerations, the data journalism workflow. Students critically analyse a published data story to identify data sources, methods and narrative techniques.

Part 2 – Data Quality and Data Analysis (1-day workshop)
Introduction to data quality concepts; practical cleaning with OpenRefine; descriptive statistics and pivot tables using Excel. Includes hands-on exercises.

Part 3 – Data Storytelling (1-day workshop)
Best practices in visualisation, narrative structures, and ethical design. Students build a clear and engaging story using Flourish.
A detailed syllabus supports the course.

A fully practical introduction to data analysis with R, tailored for journalism. Topics include:

• Base R essentials

• File handling (CSV, JSON, PDF)

• Web scraping with rvest (including Twitter and Google Scholar)

• Data manipulation with the Tidyverse

• Case study: COVID-19 analysis in Belgium

• Data visualisation with Highcharter (including advanced customisation)

• Interactive tables using DT

• Mapping with Highcharter and Leaflet

• Introduction to text mining and sentiment analysis

• Final project: data storytelling using students’ own datasets

A training programme for developing advanced analytical skills:

1. Data wrangling & regression analysis : Cleaning, preparing and modelling data using linear and logistic regression.

2. Correlation & advanced analytical methods : Understanding relationships between variables; introduction to clustering, classification and network analysis.

3. Text mining & NLP : Frequency analysis, n-grams, word clouds and practical applications for newsroom research.

A multi-session programme combining traditional investigative techniques with the use of LLMs:

1. What is investigative journalism? Inductive and deductive approaches; developing investigative angles.

2. Using LLMs to build hypotheses. How LLMs support research, insight generation and hypothesis formulation.

3. Systematic verification and evidence collection. Triangulation methods; verification of text and multimedia; tools for ensuring accuracy.

4. Cartography.  Mapping techniques, historical context, data collection and ethical considerations.

5. Data analysis for investigation. Data wrangling, cleaning, exploratory analysis, and use of basic statistics to detect patterns.

6. Text mining. NLP methods for analysing large volumes of text and extracting meaning.

7. Investigating social media. Verification techniques on social platforms and analysis of online behaviour.

Part 1 – Understanding AI and its role in journalism (4 hours)

Covers: Core AI concepts and their historical development; AI in news production and distribution; automated fact-checking technologies; possibilities and limits of generative AI; AI  AI as an object of enquiry.

Part 2 – Generative AI in Practice (2 hours)

Guest Lectures

Introduces the role of AI in newsrooms, from automation to fact-checking. Examines democratic implications, notions of truth, editorial responsibility and risks linked to generative technologies.

Covers information and disinformation flows, algorithmic bias, data privacy, the responsibility of platforms, and regulatory frameworks such as GDPR and the DSA.

A practical session focusing on AI literacy, oversight, ethical governance and methodologies for reducing bias.

Explores how myths, fiction and cultural narratives have shaped public understanding of AI from antiquity to modern media.

Masterclasses

Explores ethical standards (including EU codes) and hands-on techniques for collecting, processing, analysing and visualising data. Includes an interactive ethics quiz based on real case studies.

Combines historical perspective and practical methodology for clear, impactful and accurate visual communication. Topics include semiotics, cognitive clarity, data preparation and narrative design.

Introduces topic modelling, clustering and computational support for systematic reviews, illustrated with a published case study.

Interuniversity Certificate in Media Literacy

Covers information disorders, drivers of virality, the role of AI, and European responses such as the DSA and the Code of Practice on Disinformation.

Verification methods for images, text and video based on the 5W approach. Includes teaching materials for media literacy.

CIVIS – Online Fake News and Disinformation

Overview of global fact-checking organisations (IFCN, EFCSN, EDMO), their principles, and the challenges of dealing with propaganda, claims evaluation and geopolitical contexts.

Practical training in verifying multimedia content, including:

• Challenges of AI-generated manipulation

• Reverse image/video search

• Metadata analysis

• Geolocation tools

• Identifying AI-generated images

• Understanding factuality and limitations of LLMs