COMPUTER SCIENCE FOR CULTURAL HERITAGE

This course provides a foundational understanding of computer science principles and digital technologies applied to cultural heritage. Students will acquire conceptual and practical tools for managing, preserving, inventorying, digitizing, and valorizing artistic and cultural assets, with a focus on open-source solutions and the use of Artificial Intelligence.

By the end of this course, students will be able to:
- Outline the basics of hardware, software, networks, and the web;
- Identify digital formats for images, audio, and video;
- Illustrate the role of databases in managing cultural heritage;
- Describe the principles of Data Science and Artificial Intelligence;
- Apply digital tools to manage and promote cultural heritage;
- Create data processing and analysis workflows using Orange Data Mining;
- Utilize generative AI tools to support analysis, content creation, and communication.

No particular prerequisite is required.

Module 1 – Fundamentals of Computer Science (14 hours)

Detailed Objectives
- To provide the essential concepts of data representation and processing.
- To introduce the fundamentals of hardware, software, networks, and the web.
- To understand the role of databases in the management of cultural heritage.
- To learn about digital formats for images, audio, and video.
- To introduce the principles of Data Science and Artificial Intelligence.
- To develop a critical awareness of the social impact of information technologies.

Content
- Data and information: binary representation, encoding.
- Algorithms and hardware/software architectures.
- Networks and the World Wide Web.
- Relational databases and applications in the cultural sector.
- Digital images, audio, and video.
- Introduction to Data Science and data culture.
- Principles of AI and Generative AI.
- Ethical and social aspects of computer science.

Activities
- Guided discussion on real-world case studies of cultural heritage digitization.
- Critical analysis of digital applications in museums and archives.
- Talks by experts from the cultural heritage field.

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Module 2 – Data Management and AI Application Lab (34 hours)

Detailed Objectives
- To apply digital tools to the management and enhancement of cultural heritage.
- To experiment with data processing and analysis workflows using Orange Data Mining.
- To use generative AI tools to support analysis, creation, and communication activities.
- To develop a collaborative project to create a bot agent.

Content and Activities
1. **Data Management:** acquisition, storage, processing, visualization, and reporting.
- Creation of a simple museum database using free online tools.
2. **Workflows with Orange Data Mining:**
- (b.1) Image classification (paintings) with storage in a vector store → creating an image search engine for a museum.
- (b.2) Literary text analysis → creating a textual search engine for a library.
3. **Generative AI:**
- Using prompts in ChatGPT, Mistral, DeepSeek, and Gemini.
- Text production, data analysis, and image generation.
4. **Bot Agent for Cultural Heritage:**
- Creation of a small virtual assistant to support visitors at museums, archaeological sites, or libraries.

Digital lecture notes and multimedia tutorials will be provided by the professor.
G. B. Ronsivalle, La nuova intelligenza digitale. Come trasformare i dati in decisioni per progettare il futuro. Maggioli, 2022

A. Individual Written Test: Open-ended questions covering the theoretical concepts of Module 1.
B. In-course Individual Assignments: Hands-on lab work and production of deliverables.
C. Group Project: Development of a theme-based bot agent (each group will be assigned a unique context and target).

The overall assessment will be based on both individual components and the group project.

written and oral

A. Individual Written Test (Qualifying Exam): This is a pass/fail test with a passing score of 60%. It is a prerequisite for the final grade.
B. Individual Practical Assignments: A series of in-course deliverables worth a total of up to 5 points.
C. Group Project: Development of a theme-based bot agent, graded on a scale of 0-30 points.

Final Grade Calculation: To receive a final grade, a student must first pass the written test (A). The final score is then calculated as the sum of the points from the practical assignments (B) and the group project (C).

Module 1: Interactive classroom lectures featuring examples and group discussions.
Module 2: Hands-on lab sessions with guided exercises using free software and AI tools.
Collaborative learning: Students will work in groups to develop a final project, distributing roles and sharing expertise.

This subject deals with topics related to the macro-area "Cities, infrastructure and social capital" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development