Communication and Generative Ai
A.Y. 2026/2027
Learning objectives
Within the area of computer science, students will acquire knowledge and understanding of:
- what artificial intelligence is and how it works — language models and automatic generation of images, audio and video;
- basic principles of generative models and how to interact with them (prompting);
- AI applications in digital communication;
- criticalities: reliability, bias and transparency of AI-generated contents.
- what artificial intelligence is and how it works — language models and automatic generation of images, audio and video;
- basic principles of generative models and how to interact with them (prompting);
- AI applications in digital communication;
- criticalities: reliability, bias and transparency of AI-generated contents.
Expected learning outcomes
- Using generative AI for digital communication projects.
- Writing effective prompts to get relevant and coherent results from generative models.
- Critically analysing AI-generated contents, evaluating their communicative quality, context adherence and potential bias.
- Incorporating generative AI in the writing and editing process, and in the production of multimedia contents.
- Writing effective prompts to get relevant and coherent results from generative models.
- Critically analysing AI-generated contents, evaluating their communicative quality, context adherence and potential bias.
- Incorporating generative AI in the writing and editing process, and in the production of multimedia contents.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Online lectures on teams
Course syllabus
The course is structured into mini-modules
Module 0 - Foundations of Computer Science: how computer works, how internet works, databases, high overview of computer programming (not in exam)
Module 1 - Foundations of Artificial Intelligence and Basic Terminology
Definition of Artificial Intelligence, Machine Learning, Deep Learning, generative models, information retrieval. Basic machine-learning concepts: types of learning, data, tasks, models, and loss functions.
Module 2 - Generative Models: Development and Functioning
Architecture and training of models (Transformers, LLMs): embeddings (numerical representations), positional encoding, neural networks, and the attention mechanism. Handling word position, semantics, and context. Training phases, inference, fine-tuning. Reinforcement Learning from Human Feedback (RLHF), Retrieval-Augmented Generation (RAG), Deep Search, and hybrid models. Agentic AI.
Module 3 - Prompt Engineering
Management of user queries by an online LLM. Temperature and response randomization. System, role, and contextual prompting. Zero-shot, one-shot, and few-shot prompting. The RISEN methodology (Role, Instructions, Steps, End goal, Narrowing) for creating an initial prompt. Advanced prompting: Chain of Thought (CoT), Tree of Thought (ToT). Best practices in prompting and contextualization in communication.
Module 4 - Evaluation of Generative AI Models
Metrics, benchmarks, leaderboards. Intrinsic and extrinsic evaluation. Accuracy and confusion matrix. BLEU score, BertScore. Reference datasets and open leaderboards. Case studies
Module 5 - Critical and Ethical Issues
Bias in data, models, and prompts: sources, impacts, and mitigation. Quantification and assessment of bias in LLMs. Reliability and transparency: explainability, watermarking, traceability.
Module 6 - Multimodal Generative AI
Overview of representations and models for image, audio, and video generation. Limits of multimodal generative AI.
Throughout the course, students will also engage in:
Hands-on prompt-engineering exercises, with a focus on use cases in digital communication and language mediation.
Critical analysis of AI-generated content.
Activities that integrate generative-AI tools into multimedia content-creation workflows.
Module 0 - Foundations of Computer Science: how computer works, how internet works, databases, high overview of computer programming (not in exam)
Module 1 - Foundations of Artificial Intelligence and Basic Terminology
Definition of Artificial Intelligence, Machine Learning, Deep Learning, generative models, information retrieval. Basic machine-learning concepts: types of learning, data, tasks, models, and loss functions.
Module 2 - Generative Models: Development and Functioning
Architecture and training of models (Transformers, LLMs): embeddings (numerical representations), positional encoding, neural networks, and the attention mechanism. Handling word position, semantics, and context. Training phases, inference, fine-tuning. Reinforcement Learning from Human Feedback (RLHF), Retrieval-Augmented Generation (RAG), Deep Search, and hybrid models. Agentic AI.
Module 3 - Prompt Engineering
Management of user queries by an online LLM. Temperature and response randomization. System, role, and contextual prompting. Zero-shot, one-shot, and few-shot prompting. The RISEN methodology (Role, Instructions, Steps, End goal, Narrowing) for creating an initial prompt. Advanced prompting: Chain of Thought (CoT), Tree of Thought (ToT). Best practices in prompting and contextualization in communication.
Module 4 - Evaluation of Generative AI Models
Metrics, benchmarks, leaderboards. Intrinsic and extrinsic evaluation. Accuracy and confusion matrix. BLEU score, BertScore. Reference datasets and open leaderboards. Case studies
Module 5 - Critical and Ethical Issues
Bias in data, models, and prompts: sources, impacts, and mitigation. Quantification and assessment of bias in LLMs. Reliability and transparency: explainability, watermarking, traceability.
Module 6 - Multimodal Generative AI
Overview of representations and models for image, audio, and video generation. Limits of multimodal generative AI.
Throughout the course, students will also engage in:
Hands-on prompt-engineering exercises, with a focus on use cases in digital communication and language mediation.
Critical analysis of AI-generated content.
Activities that integrate generative-AI tools into multimedia content-creation workflows.
Prerequisites for admission
No
Teaching methods
Lectures, group discussions, and computer-based exercises
Teaching Resources
Slides, exercises and lecture notes.
Prompt engineering book by Lee Boonstra
Prompt engineering book by Lee Boonstra
Assessment methods and Criteria
Written test
Professor(s)