It’s based on the article ‘Systems thinking for journalists’ which we recommend lecturers and trainers read before adapting this lesson plan.

In some aspects systems thinking is similar to some of the research methods outlined in two other articles on Media Helping Media, ‘Story development techniques’ and ‘How to develop news angles’.

Both those pieces focus on the need to think through various aspects (angles) of a news story in order to provide the most comprehensive coverage. Systems thinking goes a step further; it’s about linking those angles to try to find the root cause of the issue being covered.

Lesson Plan: Systems thinking in journalism

Lesson objectives:

By the end of this lesson, students will be able to:

• Define systems thinking and explain its relevance in journalism.
• Identify the interconnected parts of a news story and analyse how they influence each other.
• Apply systems thinking to develop more comprehensive and contextualised news reports.
• Recognise biases and limitations in traditional event-based reporting.
• Use tools and techniques to create systemic analyses of current events.

Lesson outline:

Introduction to systems thinking

• • Definition of systems thinking: A holistic approach that examines how different parts of a system influence each other rather than looking at them in isolation.
  • Relevance to journalism: Traditional news reporting often focuses on isolated events without exploring the broader context and underlying factors.
  • Discussion:
    • What are some limitations of event-driven journalism?
    • How can a systems approach improve storytelling?
  • Case Study: A brief look at a news event reported in a fragmented way versus a systemic approach (e.g., climate change, economic crises, or social movements).

Mapping news stories as systems

• • Activity: Students choose a recent news story and break it down into its key elements, such as:
    • Root causes
    • Stakeholders (who is affected?)
    • Key influences (policies, cultural factors, economic conditions)
    • Short- and long-term outcomes
  • Group work: Using system mapping techniques, students create visual representations of how these elements connect.

System mapping techniques: 

• • Tools: In the simplest form all you need is plenty of paper and pens or a whiteboard with markers. However there are several free and premium systems thinking tools that lecturers might want to experiment with before introducing them to the students. A few examples include Insight Maker, Kumu, Miro, Loopy, and SageModeler.
  • Causal loop diagrams (CLDs): Students draw arrows connecting factors to show causal relationships, identifying feedback loops that reinforce or counteract trends. This can be done on flip-charts or a white board. We suggest lectures and trainers make sure they are familiar using CLDs before adapting this lesson.
  • Network analysis: Mapping relationships between different characters in the news story, for example individuals, policymakers, corporations, interest groups, to try to  understand where influence and interdependence lies. A flipchart or whiteboard works fine for this purpose
  • Mind mapping: A simpler approach where students brainstorm connections between ideas and categorise them hierarchically. Again, flipchart with plenty of paper and pens is ideal.
  • Discussion: What are the hidden relationships and ripple effects in the story? What elements are often overlooked?

Applying systems thinking in journalism

• • Example analysis: Reviewing a well-researched investigative report that applies systems thinking, such as the reporting on the 2008 financial crisis, COVID-19 pandemic, or climate change policies.
  • Writing exercise: Students rewrite a short news article by incorporating systemic elements, adding context, and highlighting connections between different factors.
  • Peer review: Share and discuss how applying systems thinking changed the focus and depth of the story.
  • Guidance on framing questions: Encouraging students to ask broader questions, such as:
    • What are the underlying causes of this event?
    • Who benefits or suffers from this issue?
    • What are the possible long-term consequences?

Example of systems thinking in journalism

• • Case study: Climate change and wildfires
    • Traditional approach: A news story about a wildfire focuses on the immediate damage, number of homes burned, and the firefighting response.
    • Systems thinking approach: A systemic analysis would explore:
      • How climate change contributes to increasing wildfire frequency.
      • The role of urban expansion and deforestation in wildfire vulnerability.
      • Government policies on land management and climate resilience.
      • Economic and social impacts on affected communities.
      • Future risks and possible preventive measures.
    • Discussion: How does this approach provide a deeper understanding for the audience? How might it influence public discourse and policy decisions?

Conclusion and reflection

• • Recap: Key takeaways from the lesson.
  • Discussion: How does systems thinking enhance journalistic integrity and improve public understanding?
  • Assignment: Choose a current news article and analyze it using a systems thinking approach. Identify connections that are missing and suggest ways to improve the reporting.

Assessment & evaluation:

• Active participation in discussions and activities.
• Quality and depth of system maps and rewritten articles.
• Reflection on how systems thinking impacts journalism.

Materials needed:

• Whiteboard or digital collaboration tool.
• Flipchart with paper and pens.
• Coloured stickers.
• Sample news articles for analysis.
• Paper, pens, or laptops for writing exercises.
• Optional online system mapping tools (see above).

Reference resource: Systems thinking for journalists

This lesson equips journalism students with the ability to see the bigger picture behind news stories, fostering deeper and more insightful reporting. By applying systems thinking, journalists can provide richer narratives that help audiences understand complex issues more comprehensively.

The post Lesson: Systems Thinking first appeared on Media Helping Media.

Created from training resources on Media Helping Media, it provides a robust foundation in investigative journalism, equipping students with the skills, knowledge, and ethical considerations necessary to conduct in-depth, impactful reporting.

The module, which can be tailored to meet local needs, covers essential topics such as sourcing, document analysis, dossier building, interviewing techniques, ethical considerations, and the evolving role of AI.

The module incorporates practical exercises and real-world examples to equip students with the foundational knowledge needed for a career in journalism.

It is released under the terms of the Creative Commons BY-NC-SA 4.0 licence so that educational institutions can make use of the material free-of-charge.

Course philosophy: This course emphasises the critical role of investigative journalism in a democratic society. It aims to equip students with the skills, ethics, and resilience to pursue impactful stories that expose wrongdoing and promote transparency.

Course structure: The course is divided into six thematic modules, each building upon the previous one. Each module will incorporate:

• Theoretical foundations: Core concepts and principles.
• Practical skills: Hands-on exercises and simulations.
• Case studies: Analysis of real-world investigative reports.
• Ethical dilemmas: Discussion and debate on ethical challenges.
• Guest speakers: Experienced investigative journalists sharing their insights.

Detailed module breakdown:

Module 1: Foundations of investigative journalism (Weeks 1-2)

• • 1.1 Defining investigative journalism:
    • Distinguishing it from other forms of journalism.
    • The role of investigative journalism in holding power accountable.
    • Historical context and evolution of investigative reporting.
      • Resource: Tips for investigative journalism
  • 1.2 The investigative mindset:
    • Cultivating curiosity, skepticism, and persistence.
    • Developing critical thinking and analytical skills.
    • Managing stress and maintaining emotional resilience.
      • Resource: The mindset for investigative journalism
  • 1.3 Ethical frameworks:
    • Principles of accuracy, fairness, and transparency.
      • Resource: Why editorial ethics are important
      • Resource: Lesson – Editorial ethics
    • Navigating conflicts of interest and maintaining impartiality.
      • Resource: Accuracy in journalism
      • Resource: Fairness in journalism
      • Resource: Integrity in journalism
    • Protecting sources and minimising harm.
      • Resource: Lesson – Investigative journalism
      • Resource: Lesson – Investigative journalism
    • Practical exercise: Analyse a controversial investigative report and discuss its ethical implications.

Module 2: Sourcing and interviewing strategies (Weeks 3-4)

• • 2.1 Source development:
    • Identifying and cultivating diverse sources.
    • Building trust and maintaining confidentiality.
    • Using open-source intelligence (OSINT).
      • Resource: Source motivation
      • Resource: Open-source intelligence
  • 2.2 Interviewing techniques:
    • Preparing for and conducting in-depth interviews.
    • Active listening and probing for information.
    • Handling difficult or reluctant interviewees.
      • Resource: Ways a suspect can help a journalist
      • Resource: Lesson – Interviewee motivation
    • Practical exercise: Role-playing different interview scenarios.

Module 3: Document analysis and data management (Weeks 5-6)

• • 3.1 Accessing and analysing official documents:
    • Freedom of information laws and access requests.
    • Analysing financial records, legal documents, and government reports.
    • Identifying patterns and inconsistencies.
      • Resource: How to investigate official documents
      • Resource: Lesson – Investigating official documents
  • 3.2 Building and managing investigative dossiers:
    • Organising and classifying information.
    • Using databases and spreadsheets for data management.
    • Maintaining accurate and secure records.
      • Resource: Compiling an investigative dossier
      • Resource: Lesson – The investigative dossier
    • Practical exercise: Analyse a set of documents and create a structured dossier.

Module 4: Investigating corruption and sensitive issues (Weeks 7-8)

• • 4.1 Techniques for corruption investigations:
    • Following the money trail and identifying illicit activities.
    • Investigating public procurement and government contracts.
    • Dealing with whistleblowers and anonymous sources.
      • Resource: Investigating corruption
  • 4.2 Handling sensitive information:
    • Protecting vulnerable sources and victims.
    • Reporting on sensitive topics with sensitivity and accuracy.
      • Resource: Respecting privacy
      • Resource: Lesson – privacy and journalism
      • Resource: Offence and journalism
      • Resource: Lesson – Offence and journalism
    • Case study: Analysis of a major corruption scandal.

Module 5: Risk management and legal considerations (Week 9)

• • 5.1 Identifying and mitigating risks:
    • Assessing physical, digital, and legal risks.
    • Developing safety protocols and contingency plans.
    • Maintaining digital security and data protection.
      • Resource: Avoiding the pitfalls of investigative journalism
      • Resource: Scenario – Trespass and journalism
    • 5.2 Legal frameworks and defamation:
      • Understanding defamation laws and libel.
      • Using legal resources and seeking legal advice.
        • Resource: Scenario – Withholding information
        • Resource: Court reporting for beginners
      • Guest speaker: A media lawyer discussing legal pitfalls.

Module 6: AI and the future of investigative journalism (Week 10)

• • 6.1 AI tools for investigative reporting:
    • Using AI for data analysis, visualisation, and OSINT.
    • Exploring AI-powered fact-checking and verification tools.
      • Resource: AI and investigative journalism
      • Resource: AI in day-to-day journalism
      • Resource: Lesson – AI-powered investigations
    • Guest speaker: An expert in the use of AI for newsgathering
  • 6.2 Ethical considerations in AI-driven investigations:
    • Bias in algorithms and data.
    • Transparency and accountability in AI applications.
      • Resource: Dealing with algorithmic bias
      • Resource: Lesson – Algorithmic bias
      • Resource: Unconscious bias
      • Resource: Lesson – dealing with unconscious bias
    • Debate: The ethical implications of using AI in investigative journalism.
      • Resource: Media Helping Media’s ethics section

Assessment:

• Class participation and discussions:
  • Assessing class participation involves setting clear expectations, recognising diverse contributions beyond just verbal comments, and ensuring fairness through rubrics, observation, and self/peer assessments.
  • This method encourages engagement, promotes critical thinking, develops communication skills, and provides valuable feedback, allowing instructors to gauge student understanding and involvement in a structured and objective manner.
• Practical exercises and case studies:
  • Practical exercises and case studies serve as dynamic assessment tools by directly applying theoretical knowledge to real-world scenarios.
  • Through exercises like mock interviews, document analysis, or data visualisation, students demonstrate their ability to execute investigative techniques.
  • Case studies, involving the analysis of actual investigative reports, challenge students to identify methodologies, ethical considerations, and potential pitfalls.
  • These methods allow instructors to evaluate not only a student’s grasp of concepts but also their practical skills, problem-solving abilities, and decision-making processes within the context of investigative journalism, providing a more comprehensive understanding of their competence.
• Investigative proposal (midterm): Students will develop a detailed proposal for an investigative project, including a research plan, sourcing strategy, and ethical considerations.
• Final investigative project: This will be a more in-depth investigative project, with the students producing a multi media report.
• Peer review: Students will critically evaluate each other’s work, providing constructive feedback.
• Portfolio development: Students will compile a portfolio of their investigative work, showcasing their skills and abilities.

Technology integration:

• Use of data analysis software (e.g., Excel, Google Sheets, Tableau).
• Introduction to OSINT tools and techniques.
• Training on secure communication and data storage.

Teaching methods:

• Lectures and presentations.
• Case studies and real-world examples.
• Guest speakers from investigative journalism.
• Practical exercises and workshops.
• Group discussions and debates.

Materials:

• Course readings and handouts.
• Access to online resources and databases.
• Software for data analysis and visualisation.

This module is designed to be adaptable and can be tailored to the specific needs and resources of the institution.

Summary

This comprehensive investigative journalism module aims to equip students with essential skills, knowledge, and ethical understanding through a structured six-part course, covering foundations, sourcing, document analysis, corruption investigations, risk management, and AI integration.

Utilising practical exercises, case studies, and guest speakers, students will learn to navigate the complexities of investigative reporting, build investigative proposals, conduct in-depth projects, and develop portfolios, all while emphasising ethical considerations, legal awareness, and the use of relevant technology, with assessments focused on participation, practical application, and project development.

The post Module: Investigative Journalism first appeared on Media Helping Media.

It is based on the article ‘AI and investigative journalism‘ which we recommend trainers read before adapting this lesson plan for your own purposes.

Learning objective

Students will evaluate the effectiveness of AI tools in enhancing investigative journalism by analysing case studies and identifying patterns, trends, and anomalies in datasets. They will also assess the ethical implications of AI usage in journalism, ensuring transparency and human oversight.

• Student-facing objective: By the end of this lesson the student will be able to evaluate how AI tools can improve investigative journalism and understand the ethical considerations involved.
• Standards: Students will learn ways in which they can use artificial intelligence (AI) as a tool when producing a piece of investigative journalism.

Learning activities

Warm-up

• Display a short video clip or image related to investigative journalism, such as a news report or a scene from a documentary.
• Ask students: “What do you notice? What do you wonder?”
• Give them a few minutes to think and jot down their observations and questions.
• Then, have them share with a partner.
• Select a few students to share their thoughts with the class, recording key points.
• Guide the discussion towards the role of technology and data in journalism, setting the stage for exploring AI’s impact.

Direct instruction

• Introduction to AI in investigative journalism:
  • Begin with a brief overview of how AI is transforming investigative journalism.
  • Highlight key areas such as data analysis, pattern recognition, and efficiency enhancement.
  • Use examples, like AI uncovering financial fraud through transaction analysis, to illustrate these points.
• Data analysis and pattern recognition:
  • Explain how AI algorithms sift through large datasets to identify patterns and anomalies.
  • Provide a scenario where students analyse a simplified dataset of financial transactions to spot potential fraud.
  • Guide them through the process, emphasising AI’s role in identifying connections that are difficult for humans to detect manually.
• Ethical considerations and human oversight:
  • Discuss the importance of ethical considerations in using AI for journalism.
  • Highlight the need for transparency and human oversight.
  • Present a case study where AI was used in reporting, and ask students to evaluate the ethical implications.
  • Encourage them to consider how journalists can maintain ethical standards while leveraging AI tools.

Guided practice

Think, Pair, Share: Guide students through a collaborative analysis of AI’s role in investigative journalism.

• Think: Present a dataset related to a recent investigative journalism case. Ask students to individually identify patterns, trends, or anomalies using AI tools.
• Pair: Have students pair up to discuss their findings. Encourage them to compare insights and refine their understanding of AI’s capabilities.
• Share: Facilitate a class discussion where pairs share their observations. Highlight diverse perspectives and insights.
• Connect: Relate findings to real-world applications of AI in journalism. Discuss how AI enhances efficiency and accuracy.
• Reflect: Prompt students to consider ethical implications and the importance of human oversight in AI-assisted journalism.

Independent practice

• Dataset analysis:
  • Provide students with a dataset related to a current investigative journalism topic.
  • Instruct them to use AI tools to identify patterns, trends, or anomalies.
  • Encourage them to document their findings and reflect on AI’s role in enhancing their analysis.
• Case study evaluation:
  • Assign a case study where AI was used in investigative journalism.
  • Have students evaluate the effectiveness of AI tools in the investigation, focusing on data analysis, pattern recognition, and ethical considerations.
• Reflection:
  • Ask students to write a brief reflection on how AI can transform investigative journalism.
  • Encourage them to consider both the benefits and ethical challenges.

Assignment

Ask students these questions:

1. How can AI assist journalists in identifying patterns and anomalies in large datasets?
2. What are some ethical considerations journalists must keep in mind when using AI tools?
3. What’s one question you still have from today’s lesson?

Suggested answers:

• Suggested answer to Question 1: AI can analyse vast amounts of data quickly, identifying patterns and anomalies that might be missed by humans.
• Suggested answer to Question 2: Journalists must ensure transparency, maintain human oversight, and consider the ethical implications of AI use.

Teacher resources

Differentiation guide

• Advanced learners:
  • Encourage deeper exploration of AI’s role in investigative journalism by assigning additional case studies.
  • Challenge them to critique AI’s limitations and propose solutions.
  • Facilitate discussions on the future of AI in journalism and its potential impact on media ethics.
• Striving learners:
  • Simplify datasets and provide step-by-step guidance in using AI tools.
  • Use visual aids to illustrate AI processes.
  • Pair them with peers for collaborative learning.
  • Offer additional support in understanding ethical considerations, using real-world examples to clarify concepts.
• Recommended reading:
  • This lesson plan is based on the article ‘AI and investigative journalism‘ which we recommend trainers read before adapting this lesson plan for your own purposes.

Notable definitions

• Artificial intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems, to perform tasks such as data analysis, pattern recognition, and decision-making in journalism.
• Pattern recognition: The ability of AI systems to identify regularities and trends within large datasets, which can help journalists uncover hidden stories or anomalies.
• Deepfake: A synthetic media in which a person in an existing image or video is replaced with someone else’s likeness, often used to spread misinformation, which AI tools can help detect.

Required materials

• Computers: For accessing AI tools and datasets.
• Internet access: To retrieve datasets and case studies.
• AI software/tools: For data analysis and pattern recognition.
• Projector: To display video clips or images.
• Printed datasets: Simplified versions for manual analysis.
• Notebooks: For students to jot down observations and reflections.

Lesson summary

• Warm-up
• Direct instruction
• Guided practice
• Independent practice
• Assignment

The free teaching tools at the Khan Academy were used in the production of this lesson plan.

Related article

AI and investigative journalism

The post Lesson: AI-Powered Investigations first appeared on Media Helping Media.