X

Teaching Portfolio

Laurence Dierickx

2024-12-01

BA & Master’s programme (lectures and workshops)

1. Digital investigative methods and tools

This programme consists of a two-hour lecture on verification, fact-checking and digital investigation, followed by a one-day workshop on investigative techniques and open source intelligence (OSINT). The lecture provides an in-depth understanding of the principles of fact-checking and verification, as well as the essential methods of digital investigation. It covers the characteristics, limitations and strengths of search engines, as well as advanced techniques for effective information retrieval. The workshop builds on the lecture and provides a hands-on approach to OSINT techniques. Participants will take part in a practical exercise. Duration: 2 hours + 1 day.

2. Introduction to data journalism

This comprehensive course introduces journalism students to the principles and tools of data journalism. Combining theory and hands-on practice, it equips students with the skills to identify, analyse and present data-driven stories effectively. It is divided into three parts:

A curriculum has been developed to support this teaching

3. Data journalism with R

This comprehensive hands-on training course is specifically designed for journalism students to develop data analysis and visualisation skills using R. The course introduces essential concepts and tools, and provides step-by-step guidance through practical applications. Students will gain expertise in data processing, analysis and visualisation using libraries such as Tidyverse, Highcharter and Leaflet. Course Modules and Content:

Duration: The course is structured to be completed over 8-10 weeks, with approximately 3-4 hours per week of lectures and exercises, making it suitable for semester-based schedules or intensive workshops. It was designed for the data journalism specialisation at the Master 2 level.

4. Advanced data analysis for data journalism

This comprehensive 3-session training programme (2/3 hours per session) is designed to equip data journalists with advanced data analysis techniques, enhanced by the use of large language models (LLMs).

5. Investigative journalism

This training program provides a comprehensive introduction to the key principles and methods of investigative journalism, with an emphasis on the use of Large Language Models (LLMs) to enhance the investigative process. The course covers basic approaches to investigative reporting, hypothesis building and the critical aspects of systematic verification.

6. AI & journalism: Challenges and opportunities in professional practices

This course explores the transformative impact of Artificial Intelligence (AI) on journalism, examining how AI is reshaping the production, distribution and consumption of news, while addressing the ethical and professional challenges it poses. It is therefore divided into six key parts:

  1. Defining AI: This section introduces the basic concepts of AI, including its historical development, definitions and distinctions between narrow AI, general AI and generative AI. It explains how AI systems work and how they are integrated into technological processes.
  2. AI in news production: This section explores the role of AI in breaking news detection, investigative reporting, advanced archive searching or diversifying content production. It shows how AI can automate tasks and speed up workflows, while raising questions about creativity, human agency and editorial control.
  3. AI in news distribution: This section looks at algorithms that drive content recommendations and personalisation. It explores the impact of AI on user engagement, diversity of information and the risks of filter bubbles, echo chambers and political polarisation.
  4. The case for fact-checking: This section focuses on how AI is being used to verify information. It examines tools and technologies designed for automated fact-checking, their effectiveness in combating disinformation, and the inherent limitations of these systems in real-time or complex contexts.
  5. The case for generative AI: This section explores the rise of generative AI tools, such as large language models, and their application in journalism. It highlights the benefits and risks of using LLMs, while addressing concerns about accuracy, bias and ethical accountability.
  6. AI as an object of journalistic inquiry: Finally, the course examines AI as an object of inquiry. It teaches methods for understanding and critiquing the data and algorithms behind AI systems, and for uncovering bias and discriminatory practices embedded within these technologies.

Ethical and societal challenges are central throughout the course, with discussions on algorithmic bias, the environmental impact of AI, and its implications for journalistic identity and practices. It also examines the complementary role of human-machine collaboration in maintaining ethical standards such as transparency, accountability, and fairness. This comprehensive overview of AI in journalism underscores both the innovative possibilities and the challenges of integrating these technologies into professional practices while emphasizing the importance of preserving journalistic integrity in an AI-driven era. Duration: 4 hours.

Guest lectures

1. AI in Journalism: Practical, ethical and democratic challenges

This guest lecture explores the transformative impact of artificial intelligence on journalism, examining its implications from practical applications to ethical and democratic challenges. Starting with fundamental questions – *What is journalism? What is AI?* – the session delves into the integration of AI into news production and distribution. Key case studies include the role of AI in fact-checking and the emergence of generative AI as a tool for content creation. Participants will critically reflect on the evolving concept of truth in the age of AI, addressing questions such as: What constitutes a fact when algorithms influence narratives? This session will provide insights into both the opportunities and risks of AI, offering a nuanced perspective on how technology is reshaping the media landscape and its accountability to democratic values. Duration: 3-4 hours (for students not studying journalism).

2. Ethical issues of platforms, algorithms and artificial intelligence

This guest lecture explores the ethical challenges posed by digital platforms, algorithms and artificial intelligence (AI). He addresses the dual phenomena of a continuous flow of information and disinformation, and highlights their impact on democracy, public opinion and societal polarisation. It will also look at the role of algorithms and AI in shaping content, decision-making and the spread of misinformation on social media platforms. Key topics include the responsibilities of platform operators, the ethical implications of AI bias, data privacy challenges, and regulatory efforts such as the European Union’s General Data Protection Regulation (GDPR) and the Digital Services Act (DSA). The presentation emphasises the need for transparency, accountability and human oversight in AI applications, particularly in the field of journalism.

3. AI: An enemy of journalism?

This guest lecture examines the growing influence of artificial intelligence (AI) on the journalism industry, offering a critical analysis of its potential benefits and challenges. As AI technologies become more integrated into news production – from automated fact-checking to content generation – this session will explore their impact on journalistic practices. It will also consider the risks, such as misinformation, algorithmic bias and the potential loss of journalistic integrity. It also considers the environmental costs of AI and how the media can balance technological innovation with ethical responsibility in an AI-driven world. Duration: 2 hours.

4. Mitigating the risks of using LLMs: A balanced approach to taking the lead

Generative AI, particularly large language models (LLMs), carries risks due to bias, unreliable data and inference limitations that can lead to misinterpretation. However, AI can benefit businesses by speeding up workflows, analysing large datasets and automating tasks. In business and finance, AI can quickly process large amounts of data, identify trends, and provide insights for decision-making. A balanced approach is needed to mitigate risks and maximise the potential of AI. This presentation will focus on AI literacy, human oversight, and ethical governance strategies to improve AI performance and reduce bias. Duration: 1 hour (including Q-A)

Masterclasses

1. Data journalism: From ethics to practice

This course bridges ethical considerations and practical skills in using data for powerful storytelling. Participants will explore the core ethical standards of data journalism, based on EU ethical codes, and understand how ethics guide each stage of the journalistic process. The course emphasises data literacy, equipping participants to collect, process, analyse and effectively communicate data insights. Divided into three parts – data collection and processing, data analysis and storytelling through visualisation – it provides hands-on experience in creating accurate, engaging and contextually rich narratives. An interactive quiz with real-life use cases ensures that participants apply ethical principles in practice, reinforcing the integral link between ethics and data journalism. Duration: 6 hours.

2. Translating research into compelling visuals

This course focuses on the art and science of translating research into compelling and accurate visual representations. Through a blend of historical insight and practical methods, participants will explore how humans have visualised information for centuries – from ancient memory tools and medieval illuminated texts to modern statistical charts. The curriculum emphasises understanding data types, selecting appropriate visual formats, and creating clear, impactful graphics. Key topics include:

Duration: 3 hours.

3. Conducting systematic literature reviews using machine learning with R

This module explores the use of unsupervised machine learning techniques, such as topic modelling and clustering, to support human analysis in systematic literature reviews. The course includes a case study based on the presentation * »Automated Fact-Checking to Support Professional Practices: Systematic Literature Review and Meta-Analysis »* (IJOC, 2023). This example illustrates how machine learning can improve the review process. It also focuses on the balancing machine-driven insights with critical human judgement in the context of academic and professional research. Duration: 3 hours.

Interuniversity Certificate in Media Literacy

1. Social media, disinformation and fact-checking

This course explores the critical issues linked to social networks, disinformation and fact-checking. It is structured around four main themes: the different types of information disorder and their impact on society, the factors that encourage the rapid spread of false information, the role of artificial intelligence as an instrument for amplifying these phenomena, and strategies for dealing with them. The fourth section looks at instruments for combating information disorder, including European legislation (DSA, Code of Practice on Disinformation) and fact-checking as a specialised branch of journalism. Duration: 2,5 hours.

2. Fact-checking tools and techniques

This module focuses on information verification tools and techniques, with an emphasis on images, texts, videos and the uses of artificial intelligence. Structured around the 5W rule (Who, What, When, Why, How), it offers a rigorous methodology for assessing the reliability of information. The course also includes teaching materials specially designed to promote media literacy and help raise awareness of fact-checking practices, thus contributing to the development of a critical mindset in the face of information disorder. Duration: 2,5 hours.

CIVIS – Online fake news and disinformation: recognize and verify

1. The fact-checking networks

This guest lecture provides a comprehensive overview of the development and role of fact-checking networks in combating misinformation and disinformation. It examines the global growth of fact-checking through the International Fact-Checking Network (IFCN), the European Fact-Checking Standards Network (EFSCN) and the European Digital Media Observatory (EDMO). The presentation highlights key principles such as transparency, impartiality and fairness that are essential to credible fact-checking practices. It will also address the complexities faced by fact-checkers, such as navigating nuances in claims, the challenges posed by propaganda, and the need for a deep understanding of geopolitical contexts. Duration: 2 hours.

2. Content verification & evidence collection

This hands-on 6-hour workshop explores the complex challenges of verifying multimedia content in an era dominated by artificial intelligence (AI) and deepfake technologies. Participants will gain hands-on experience using the latest tools and techniques to authenticate images, videos and other forms of digital media. The course covers the following key areas: