OVERVIEW

This module explores generative AI tools applied to design, with a particular focus on text-to-text, text-to-image, and text-to-video models. Students will learn how to integrate these tools into the design process, understanding their potential, limitations, and ethical and operational implications.

AIMS AND CONTENT

LEARNING OUTCOMES

The teaching, focused on the integration of generative artificial intelligence in the field of design, aims to reveal the capabilities and applications of generative AI. Through hands-on laboratory exercises, students will learn how this technology is able to improve the efficiency of design processes, assist in creative decisions and be used effectively at various stages of the design cycle.

AIMS AND LEARNING OUTCOMES

  Become familiar with the main generative AI tools (text-to-text, text-to-image, text-to-video).

  Learn to effectively and responsibly integrate generative tools into the design process.

  Reflect critically on the creative, technical, and ethical use of generative AI in design.

  Experiment with AI tools to generate visual and textual content supporting design ideation.

PREREQUISITES

Ability to analyse and solve complex design-related problems.
Basic knowledge of programming concepts and ability to use design software
Familiarity with digital tools and online collaboration platforms.
Basic knowledge of calculus and statistics.
Ability to apply quantitative methods for analysing and solving design problems.
Ability to critically evaluate the ethical and practical implications of emerging technologies in design.

TEACHING METHODS

The teaching activity will be provided in person through lectures and exercises.

SYLLABUS/CONTENT

• Introduction to generative models: definition and working principles

• Overview of text-to-text (e.g., ChatGPT), text-to-image (e.g., DALL·E, Midjourney), and text-to-video (e.g., Runway, Pika) tools

• Use cases of generative AI tools in design workflows

• Prompt experimentation and generation of visual/textual content

• Critical reflection on risks, bias, and ethical aspects of generative AI

• Case study discussions and design best practices

RECOMMENDED READING/BIBLIOGRAPHY

To follow the module, the material provided by the teacher will be sufficient.

TEACHERS AND EXAM BOARD

Exam Board

FILIPPO BERTANI (President)

MARIA MOROZZO DELLA ROCCA E DI BIANZE'

ANNALISA BARLA (President Substitute)

ANDREA VIAN (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/corsi/9008/studenti-orario

EXAMS

EXAM DESCRIPTION

The exam consists in the development of a group project that demonstrates understanding of the course content and the ability to apply the acquired tools in a design context.

ASSESSMENT METHODS

The evaluation is based on the assessment of:

• Knowledge of key concepts
• Critical analysis skills

FURTHER INFORMATION

Students with disabilities or learning disorders are allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the Delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities. Students are invited to contact the teacher of this course and copy the Delegate (https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita.html).