Syllabus: AI in Media & Journalism – Semester Syllabus Outline (Open-Source Focus)
Course Title: Artificial Intelligence in Media and Journalism
Course Duration: 1 Semester (16 Weeks)
Format: 3 hours/week (2 hours lecture + 1 hour lab/practical)
Course Description:
This course introduces students to the application of artificial intelligence in media and journalism using open-source tools. Covering data analysis, automated content generation, fact-checking, and creative storytelling, the course emphasizes ethical responsibility, hands-on learning, and innovation in digital reporting.
Course Objectives:
• Explore core AI concepts and their impact on journalism.
• Develop technical skills with open-source tools for data, text, audio, and video processing.
• Understand ethical and legal challenges in AI-assisted journalism.
• Create interactive and generative journalism projects.
• Design and deliver a capstone project using open-source AI workflows.
Weekly Module Breakdown:
Week 1: Introduction to AI & Ethics in Journalism
Week 2: Data Journalism with Python & Open-Source Visualization Tools
Week 3: AI-Generated Content using Open-Source Language Models (e.g., GPT-J)
Week 4: Natural Language Processing (NLP) for News Analysis
Week 5: Audio/Video Editing & Transcription (Audacity, FFmpeg, Whisper)
Week 6: Investigative Journalism & Document Analysis (Tika, ElasticSearch)
Week 7: Machine Learning for Predictive Journalism (Scikit-learn, PyCaret)
Week 8: Bias & Fairness in Open-Source AI Models (Fairlearn, AIF360)
Week 9: Social Media Analytics with Twint, Gephi
Week 10: Fact-Checking & Verification (Scrapy, InVID)
Week 2: Data Journalism with Python & Open-Source Visualization Tools
Week 3: AI-Generated Content using Open-Source Language Models (e.g., GPT-J)
Week 4: Natural Language Processing (NLP) for News Analysis
Week 5: Audio/Video Editing & Transcription (Audacity, FFmpeg, Whisper)
Week 6: Investigative Journalism & Document Analysis (Tika, ElasticSearch)
Week 7: Machine Learning for Predictive Journalism (Scikit-learn, PyCaret)
Week 8: Bias & Fairness in Open-Source AI Models (Fairlearn, AIF360)
Week 9: Social Media Analytics with Twint, Gephi
Week 10: Fact-Checking & Verification (Scrapy, InVID)
Week 11: Interactive Storytelling (Twine, Rasa, Streamlit)
Week 12: Generative AI for Creative Journalism (Stable Diffusion, Scribus)
Week 13: Legal & Ethical Issues in Open-Source AI for Media
Week 14: Capstone Project Planning (Version Control, Documentation, Git)
Week 15: Capstone Project Development – Mentoring & Troubleshooting
Week 16: Final Capstone Presentation & Peer Review
Week 12: Generative AI for Creative Journalism (Stable Diffusion, Scribus)
Week 13: Legal & Ethical Issues in Open-Source AI for Media
Week 14: Capstone Project Planning (Version Control, Documentation, Git)
Week 15: Capstone Project Development – Mentoring & Troubleshooting
Week 16: Final Capstone Presentation & Peer Review
Assessment Overview:
– Weekly Assignments & Labs – 40%
– Midterm Presentation / Report – 20%
– Weekly Assignments & Labs – 40%
– Midterm Presentation / Report – 20%
– Capstone Project – 30%
– Participation & Peer Feedback – 10%
– Participation & Peer Feedback – 10%
Key Open-Source Tools:
– Data & NLP: Python, Jupyter, Pandas, SpaCy, NLTK, Gensim
– Content Generation: GPT-J, GPT-Neo, Fairseq, OpenNMT
– Visualization: Datawrapper, Apache Superset, Gephi
– Audio/Video: Audacity, FFmpeg, Shotcut, Whisper ASR
– Verification & Research: Scrapy, InVID, Apache Tika
– Creative Tools: Twine, Streamlit, Rasa, Stable Diffusion, Scribus
– Project Management: Git, GitHub, Kanboard
– Content Generation: GPT-J, GPT-Neo, Fairseq, OpenNMT
– Visualization: Datawrapper, Apache Superset, Gephi
– Audio/Video: Audacity, FFmpeg, Shotcut, Whisper ASR
– Verification & Research: Scrapy, InVID, Apache Tika
– Creative Tools: Twine, Streamlit, Rasa, Stable Diffusion, Scribus
– Project Management: Git, GitHub, Kanboard
AI in Media & Journalism – Lesson Plans (Open Source)
Week 1: Introduction to AI & Ethics in Journalism
- Overview of AI, journalism evolution, and ethical implications.
Tools: Discussion forums, Jupyter Notebooks
Assignment: Short essay on AI’s impact.
Week 2: Data Journalism with Python & Visualization
- Data collection, cleaning, analysis; using Pandas and Datawrapper.
Tools: Jupyter, Pandas, Datawrapper
Assignment: Visualize a dataset relevant to current news.
Week 3: Open-Source Language Models
- Intro to GPT-J/Neo, text generation, prompt design.
Tools: GPT-J via HuggingFace, Google Colab
Assignment: Generate a news brief using GPT-J.
Week 4: NLP for News Analysis
- NLP techniques: tokenization, sentiment, topic modeling.
Tools: SpaCy, Gensim
Assignment: Analyze sentiment of headlines from major outlets.
Week 5: Audio/Video Transcription & Editing
- Basics of audio transcription, editing interviews.
Tools: Whisper, Audacity, Shotcut
Assignment: Transcribe a short clip and create a podcast snippet.
Week 6: Investigative Journalism & Document Parsing
- Search and parse structured/unstructured documents.
Tools: Apache Tika, ElasticSearch
Assignment: Extract metadata and insights from PDFs.
Week 7: Machine Learning for Predictive Journalism
- Build basic prediction models with Scikit-learn.
Tools: Scikit-learn, PyCaret
Assignment: Predict article popularity or trend.
Week 8: Bias & Fairness in AI
- Measure and mitigate bias in models.
Tools: AIF360, Fairlearn
Assignment: Bias audit on dataset or generated content.
Week 9: Social Media Mining & Network Analysis
- Extract and visualize social media data.
Tools: Twint, Gephi
Assignment: Network map of trending topic or hashtags.
Week 10: Verification & Fact-checking Tools
- Detect misinformation, verify media.
Tools: Scrapy, InVID
Assignment: Fact-check a viral video/post.
Week 11: Interactive & Conversational Journalism
- Build interactive stories/chatbots.
Tools: Twine, Rasa
Assignment: Create a branching story with Twine.
Week 12: Generative AI for Creative Journalism
- AI image/text generation for immersive storytelling.
Tools: Stable Diffusion, Scribus
Assignment: Design a visual magazine cover using AI content.
Week 13: Ethics, Law & Policy in Open-Source AI
- Media law, open licensing, and ethical frameworks.
Tools: Creative Commons, case studies
Assignment: Case analysis of AI misuse in journalism.
Week 14: Capstone Planning: Collaboration & Git
- Project design, Git/GitHub basics, documentation.
Tools: Git, GitHub, Kanboard
Assignment: Capstone proposal with timeline on GitHub.
Week 15: Capstone Project Development
- Students collaborate in teams to finalize their capstone projects using open-source tools. Focus on real-world
media challenges.
Activities: Group work, code/documentation check-ins, mentor sessions.
Tools: Git, GitHub, Kanboard, Markdown editors.
Deliverables: Mid-capstone project progress and public repo with README.
Week 16: Final Evaluation & Presentations
- Students present their completed capstone projects to peers and faculty. Emphasis on creativity, ethical AI
use, and storytelling impact.
Activities: Presentations, Q&A, peer review.
Tools: OBS Studio (for recording), BigBlueButton/Jitsi (for live demo), Creative Commons licenses for
publishing.
Deliverables: Final project showcase video, open license release, feedback summary
