OVERVIEW

The course explores the intersection of Artificial Intelligence, Digital Humanities, and Cyber Humanities, addressing the main AI paradigms (symbolic, subsymbolic, and generative) and their applications to the analysis, enhancement, and accessibility of cultural heritage. The course provides students with theoretical and practical skills to understand intelligent systems, design AI-based solutions, and critically evaluate their opportunities, limitations, and implications. A significant part of the course is devoted to hands-on activities in which students use Python, problem-solving techniques, Prompt-Based Learning, and generative AI tools to develop applications in the humanities domain. The course is framed within the perspectives of Cyber Humanities and Cyber Humanism, promoting a human-centered and responsible approach to AI, with particular attention to AI literacy, ethics, algorithmic citizenship and the cultural transformations introduced by digital and algorithmic ecosystems.

AIMS AND CONTENT

LEARNING OUTCOMES

The course introduces core principles of Artificial Intelligence applied to the Digital Humanities, with a focus on NLP, knowledge representation, and computer vision using Python. Emphasis is placed on ethical reflection, problem solving, and Natural Language Programming within a Cyber Humanities framework.

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 and planning), subsymbolic paradigms (machine learning, neural networks, and computer vision), and recent developments in generative AI. Particular attention will be devoted to how these technologies reshape the production, mediation, and interpretation of knowledge in the humanities.

The course is framed within the perspectives of Cyber Humanities and Cyber Humanism, integrating computational competencies with humanistic sensitivity and fostering critical reflection on algorithmic mediation, digital citizenship, AI literacy, bias, and the sustainability of technological innovation.

Applying knowledge and understanding

Students will be able to:

• apply AI tools and techniques (e.g., NLTK, LLMs, symbolic models) to textual, visual, and cultural data for analysis, classification, knowledge extraction, and transformation;

• use Python and dedicated libraries to develop simple applications for the accessibility, analysis, and enhancement of cultural heritage;

• employ generative AI systems through Prompt-Based Learning and Natural Language Programming practices, documenting and critically evaluating human–AI interactions;

• translate humanistic problems into computational workflows while respecting methodological, ethical, and communicative constraints;

• integrate AI tools into research and design activities while maintaining critical oversight and responsibility for decisions.

Making judgements

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

• assess the opportunities and risks associated with AI use in cultural contexts;

• justify technological and methodological choices based on epistemological frameworks and project goals;

• discuss the social, legal, and ethical implications of AI-based solutions, with reference to real-world applications;

• formulate autonomous and well-founded judgements on the relevance, reliability, and sustainability of computational tools;

• identify limitations, biases, and potential unintended consequences of intelligent systems.

Learning skills

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 through project documentation, prompt analysis, and critical evaluation of human–AI interactions.

Learning outcomes

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

• understand and describe the main paradigms of Artificial Intelligence, distinguishing symbolic, subsymbolic, and generative approaches;

• use AI methodologies and tools to develop simple applications in the Digital Humanities domain;

• design, document, and validate computational workflows aimed at the analysis, interpretation, and enhancement of cultural heritage;

• use generative AI critically and responsibly within problem-solving and knowledge-construction processes;

• analyse the role of AI in contemporary knowledge production, communication, and cultural transformation;

• collaborate effectively in interdisciplinary projects and communicate their choices clearly and responsibly;

• contribute to the design of innovative, sustainable, and human-centered solutions aligned with the principles of Cyber Humanities.

PREREQUISITES

Basic knowledge of Python programming is required. Any additional concepts, libraries, and tools needed for the course activities will be introduced during lectures and laboratory sessions.

TEACHING METHODS

The course combines theoretical and practical activities within a problem-solving and active-learning framework.

Theoretical lectures introduce the fundamental concepts of Artificial Intelligence, Digital Humanities, and Cyber Humanities, providing the methodological and conceptual foundations needed to understand the main AI paradigms and their applications.

Practical and laboratory activities allow students to apply acquired knowledge through guided exercises, the development of simple Python applications, the use of libraries for cultural data analysis, and experimentation with generative AI tools. Particular attention is devoted to Prompt-Based Learning, Natural Language Programming, and the design of computational workflows for Digital Humanities.

The course also includes discussions, case-study analysis, and the progressive development of an individual or group project aimed at integrating the knowledge and skills acquired throughout the course.

Attendance is strongly recommended. All teaching materials, including exercises, datasets, examples, and project guidelines, will be made available through the course portal.

The course corresponds to 6 ECTS credits, equivalent to a total student workload of 150 hours, including lectures, laboratory activities, individual study, and project development.

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

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).