Universitat Internacional de Catalunya
AI in Journalism
Other languages of instruction: English, Spanish,
Teaching staff
Introduction
The subject Introduction to Artificial Intelligence offers a practical and multidisciplinary exploration of this field, aimed at students without prior technical knowledge. With a focus on deep understanding of AI and the ability to communicate with experts, the subject addresses the history, opportunities and challenges of AI, as well as its applications in digital transformation and data science. The transversal nature of this subject makes it suitable for undergraduate students from any area of knowledge. The course is designed to provide both the most innovative tools and a general understanding of the basic principles of AI, its specific applications in different fields and the ethical challenges it entails.
At the end of the academic program, students will be able to anticipate the coming changes that AI technologies will introduce in their environment, as well as understand how to react appropriately, capitalizing on said technologies to their benefit. Likewise, the subject will address the regulatory and ethical challenges surrounding AI technologies, empowering participants to evaluate their relevance in the context of their respective fields.
Pre-course requirements
No specific prior knowledge is required to take the subject.
Objectives
Provide the basis for comprehensive training on artificial intelligence and its role in digital transformation.
Promote the development of skills to work in data science and computational thinking, including programming concepts.
Instruct students to explore search engines, Machine Learning and Large Language Models (LLM).
Provide training to develop skills in content processing, information verification, and ethics in the use of AI.
Provide training that allows students to analyze the ethical and social challenges associated with technology.
Have skills that offer a significant advantage in the labor market.
Learning outcomes of the subject
At the end of the course, students will be able to:
Knowledge
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Discriminate reliable sources of information about AI.
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Be aware of the social challenge posed by AI.
Skills
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Handle information and automate processes to achieve greater efficiency and productivity in data usage and tasks.
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Identify and evaluate the ethics and legality in the use of Artificial Intelligence and other technologies.
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Use Generative AI tools.
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Determine the best information verification tools for each situation.
Competence
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Apply AI knowledge in specific professional environments.
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Research and evaluate new AI technologies.
Syllabus
Understanding AI: Definitions and paradigms
Historical evolution, opportunities, and recent developments
Development of Generative AI
Digital Transformation, Data Science, and Computational Thinking
Statistics and Programming for Understanding AI
Leading Institutions and Companies in AI Development
2. INFORMATION GENERATIONSearch Engines:
Google and Bing utilities
Strategies for Information Search and Analysis
Machine Learning and Language Models:
- Machine Learning (ML) and Large Language Models (LLMs)
- Text Generators: GPT-4 and Bard, and their applications
- Multimodal AIs: Google Gemini
Fundamentals of Transformers and Neural Networks
- Biases and operational limitations in AI
- Effective Prompt Creation and Applications
AI Applications in Daily Life:
- AI Personal Assistants: Specialization, popularity, and monetization
- Reliable information sources: Search strategies and databases
- Social Media Ecosystem and its interaction with AI
Conversational Chatbots and Oral Information Collection:
- Speech-to-Text programs
Text and Visual Content Processing:
- Tools for text processing, translation, and subtitling
- Automated creation of graphics, images, and videos with prompts
Visual Art:
- Autonomous preparation of slide presentations
Interactivity and Audio Synthesis:
- Oral language: Text-to-Speech and its applications
- Conversational assistants, voice synthesis, and cloning
- Audio management and music creation with AI
Virtual Reality and Immersivity:
- Creation and use of avatars
- Bots for social media interaction
- Introduction to the metaverse and its applications
Information and Content Verification and Fact-Checking:
- Verification toolkits and their features
- Strategies for traceability and reverse digital information searches
- Methodologies for source validation and combating misinformation
Legal and Regulatory Framework for AI:
- The European framework on AI: privacy, intimacy, data protection
- Digital rights and authorship: Legal and ethical considerations
- Regulations on responsibility attribution in autonomous systems
Ethical and Social Challenges of New Technologies:
- The three levels of ethics in the technological society
- Ethical and social keys to a technological society
- Analysis of specific challenges and dilemmas
Responsible and Sustainable Use of AI:
- Recommendations for the ethical introduction and management of AI applications in professional and personal settings
- Sustainability and ethical development: How AI can contribute to or harm a sustainable future
AI Literacy:
- Education and training for an ethical and critical understanding of technology
Artificial Intelligence and Society:
- Social and cultural impact of AI: Effects on equity, inclusion, and social cohesion
- Contemporary ethical challenges: Algorithmic discrimination, biases, and social justice
- Debate on the future of AI: Long-term impact scenarios on society and humanity
- Characteristics of the students' field of knowledge
- Practical applications of AI in specific professional environments
- Professional ethics and deontology related to AI
- Analysis of AI trends and future outlook
- Resources for updating knowledge in AI and emerging technologies
Teaching and learning activities
In person
1. Theoretical Classes:
- Expository sessions to introduce the theoretical foundations of artificial intelligence, digital transformation, and associated ethics.
- Detailed analysis of practical cases illustrating AI applications in various sectors and their societal impact.
- Practical exercises to promote the development of skills in data science and computational thinking.
- Programming labs to acquire basic programming knowledge and explore search engines.
- Work with generative AI tools and practical applications in problem-solving.
- Detailed analysis of ethical cases related to the use of artificial intelligence.
- Class debates and discussions to encourage reflection on the ethical and social aspects of AI.
- Development of projects applying the knowledge acquired in simulated professional scenarios.
- Integration of information verification tools and analysis of reliable sources into project implementation.
- Invitations to experts in artificial intelligence and digital ethics to deliver conferences and seminars.
- Active participation of students in discussions and Q&A sessions to foster communication with experts in the field.
- Periodic evaluation of progress through assignments, projects, class participation, and exams assessing the application of learned concepts to specific cases.
- Constant feedback to improve performance and understanding of the concepts.
- Simulations of professional environments for students to apply AI knowledge and make ethical and legal decisions in realistic scenarios.
- Use of online platforms to access additional resources, case studies, and supplementary materials.
- Encouragement of self-directed learning and research to deepen understanding of specific topics.
Evaluation systems and criteria
In person
SUMMARY ARTICLE (20%) (to be completed at home)
The weekly article will have an approximate length of 800 words. It will include:
a) The main ideas about the proposed topic.
b) Points of view and recent controversies.
c) Published articles that help focus on the topic.
d) Links to useful resources to include in the article.
It will include a title and a photo generated by AI. The text will use the Calibri font, size 12 (justified), and size 16 in bold for the title (centered). The article must be uploaded to the corresponding folder for the topic on Drive no later than midnight on the Sunday following the class. Late submissions will not be graded. Articles that are not submitted will receive a score of zero and will be averaged with that score. This task accounts for 20% of the final course grade.
PRESENTATION (10%)
Each week, an “assigned group” will present their article in class. The presentation will be supported by a maximum of 10 slides that summarize the topic in a clear and visual way. This presentation is worth 10% of the final grade.
SYNTHESIS OF ARTICLES (30%)
A single document must be submitted that synthesizes the content of everything covered in the course and serves as a basis for preparing for the final test. The length should be approximately 3,000 words. This document is worth 30% of the final grade.
ASSESSMENTS (40%)
Two assessments will be conducted during the course, combining multiple-choice tests and short-answer questions. One will take place halfway through the syllabus, and the other will be at the end of the course, the latter being considered the final exam. The content of these evaluations will be drawn from class explanations and the articles developed throughout the course.
The tests will include:
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Multiple-choice questions with three possible answers.
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Short development questions.
Errors in the multiple-choice questions will deduct 0.25 points. -
Midterm test: Worth 20% of the final grade.
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Final test: Worth 20% of the final grade.
SECOND AND SUBSEQUENT EXAMS. A multiple-choice exam will be administered, and a document summarizing the content of the course will be submitted. The document must be approximately 5,000 words long.
Bibliography and resources
Torres, J. (2023). La intel· ligència artificial explicada als humans. Plataforma.
Degli-Esposti, S. (2023). La ética de la inteligencia artificial. Los Libros de La Catarata.
Carretero, A. V. (2023). El último periodista. La inteligencia artificial toma el relevo. Marcombo.