OVERVIEW

The course explores the intersection between Artificial Intelligence (AI) and the humanities, addressing symbolic, subsymbolic, and generative paradigms. It aims to provide Digital Humanities students with both theoretical and practical skills to understand and apply AI techniques for the enhancement, accessibility, and analysis of cultural heritage. A significant part of the course is dedicated to hands-on labs, where students use Python and generative AI tools (such as language models) to develop simple applications in the humanities domain. The Cyber Humanities framework guides the entire path, fostering critical and ethical reflection on AI use in relation to digital citizenship, social impact, and cultural transformation. The course prepares students to design innovative, responsible, and sustainable solutions for the digital cultural landscape.

AIMS AND CONTENT

LEARNING OUTCOMES

Obiettivo del corso è quello di analizzare soluzioni che l’Intelligenza Artificiale e altre tecnologie innovative hanno prodotto per la tutela, fruizione e valorizzazione del patrimonio culturale. Si vuole inoltre di stimolare nuove soluzioni sia metodologiche che tecnologiche per consentire di catalizzare possibili interazioni e aggregazioni tra i vari soggetti impegnati a sviluppare nuove applicazioni nel settore dei beni culturali. Riprodurre siti culturali e renderli fruibili in modo diverso, anche attraverso il web, significa aprire nuove possibilità di sviluppo per la crescita civile ed economica dei territori. Nasce quindi la necessità di definire ruoli e connessioni, dove solo la ricerca e le nuove tecnologie possono suggerire percorsi e soluzioni competitive che integrino turismo e cultura da un lato, con impresa e mercato da un altro.

AIMS AND LEARNING OUTCOMES

Knowledge and understanding: Students will gain a solid understanding of the theoretical foundations of artificial intelligence, including symbolic paradigms (knowledge representation, intelligent agents, search strategies), subsymbolic paradigms (machine learning, computer vision), and recent developments in generative AI. Particular attention will be devoted to how these technologies reshape the production and interpretation of knowledge in the humanities. The course is framed within the perspective of Cyber Humanities, which integrates computational competencies with humanistic sensitivity, encouraging critical reflection on algorithmic mediation, digital citizenship, bias, and the sustainability of innovation.

Applying knowledge: Students will be able to:

• apply AI tools and techniques (e.g., NLTK, LLMs, symbolic models) to textual and cultural data for tasks such as analysis, classification, extraction, and transformation;
• use Python and dedicated libraries to build basic applications for the accessibility and enhancement of cultural heritage;
• experiment with generative models by crafting purposeful and reflective prompts, including for the co-creation of cultural and educational content;
• translate humanistic problems into computational workflows, while respecting methodological, ethical, and communicative constraints.

Making judgenents: Students will develop a reflective and critical approach, enabling them to:

• assess the opportunities and risks of AI use in cultural contexts;
• justify technological and methodological choices based on epistemological frameworks and project goals;
• discuss the social, normative, and ethical implications of digital solutions, with reference to real-world applications (e.g., using generative AI for heritage narration, historical memory, or inclusive communication);
• formulate autonomous and well-founded judgments on the relevance and sustainability of computational tools, in line with Cyber Humanities principles.

Learning slills: The course supports the development of metacognitive awareness and autonomous learning, strengthening the ability to:

• engage in self-directed exploration of AI libraries, frameworks, and tools;
• transfer acquired competencies to diverse and interdisciplinary contexts;
• collaborate effectively in heterogeneous teams, contributing analytical and creative insights;
• reflect on the learning process by documenting work, analyzing prompt effectiveness, and critically evaluating human-AI interaction.

Learning Outcomes: By the end of the course, students will be able to:

• understand and describe the main paradigms of AI, distinguishing symbolic, subsymbolic, and generative approaches;
• use AI methodologies and tools—especially in NLP and generative AI—to build simple applications in the Digital Humanities domain;
• design and document computational workflows aimed at cultural analysis and dissemination;
• critically analyze the role of AI in knowledge construction, communication processes, and cultural transformation;
• collaborate in interdisciplinary projects and communicate their choices clearly, with awareness of ethical and social dimensions;
• contribute to the design of innovative, responsible, and sustainable solutions aligned with the values of Cyber Humanities.

PREREQUISITES

Basic knowledge of programming in Python.

TEACHING METHODS

The course is organized according to two distinct categories of activities:

• Theoretical Lecture (Lecture - Lecture): a teaching activity in which the student is predominantly "passive," i.e., attends a theoretical or practical-application lecture in the classroom, or through the tools provided by the teaching portal.
• Practical Lesson (Hands-on experience - Study session): component of "assisted teaching" in which the student is predominantly "active," i.e., carries out guided activities in the laboratory in person. 

Class attendance, the materials used, and the exercises are all essential elements for proper preparation in this discipline. Therefore, it is recommended to attend the lectures and exercises, to carefully read and scrupulously follow the directions provided in the materials made available online on the teaching portal. 

The laboratory will be held by the teacher in charge of the course, assisted by laboratory tutors. The laboratory activities will be carried out at the Computer Laboratory of the Savona Campus, and students will be divided into groups according to the capacity of the laboratory itself. Students will be required to book laboratory activities through the course portal. Only those who have made a reservation will be able to access the laboratory activities. The organization and dates of the laboratory activities will be communicated directly by the teacher at the beginning of the lessons and will be available on the course portal.

The teaching organization scheme results in 6 CFUs for a total of 150 hours of study-work.

SYLLABUS/CONTENT

RECOMMENDED READING/BIBLIOGRAPHY

• Stuart J. Russell, Peter Norvig, Artificial Intelligence. A modern approach, 4th. Ed. MyLab - Pearson, 2021.
• Materials used during the classroom lessons and during the laboratory activities made available as the course progresses on the AulaWeb portal in the section ‘Materials used in class’, together with links to resources and texts available online.

TEACHERS AND EXAM BOARD

Exam Board

GIOVANNI ADORNI (President)

GIANNI VIARDO VERCELLI

ILARIA TORRE (President Substitute)

LESSONS

LESSONS START

https://easyacademy.unige.it/portalestudenti/

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

In order to take the exam, the student must register online through the Student Portal at:   https: //servizionline.unige.it/studenti/.

The exam consists of an individual interview on the course program and on the discussion of the project carried out.

ASSESSMENT METHODS

In order to pass the exam, the student must:

- to produce a Disciplinary Thematic Glossary: ​​for each lesson the student is asked to create (and gradually enrich and refine) a glossary of disciplinary keywords;
- to develop a thematic project assigned by the teacher during the course.

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).