Degree | Type | Year |
---|---|---|
Digital Humanities and Heritage | OB | 1 |
You can view this information at the end of this document.
No prior knowledge of computer science or programming is required, except for familiarity with computer equipment at an advanced user level. The required level of mathematics is that of compulsory secondary education.
Some familiarity with humanities and/or cultural topics is recommended.
English proficiency sufficient to read texts is required
This course presents the historical and theoretical framework for work in Digital Humanities and Heritage, placing emphasis on aspects such as the preservation of historical memory and the relevance of culture in addressing social challenges. In terms of content, students are introduced to the formalization of narrative discourse in the Humanities, and the distinction between basic concepts such as Data, Information, and Knowledge is analyzed.
The course serves as an introduction and foundation for the digital tools commonly used in the digitization and editing of texts, in the digitization of sound and music, as well as in the digitization of historical and artistic heritage. It also initiates discussion on the historical and theoretical aspects of the discipline.
Students are introduced to the fundamental aspects of digital information processing and the quantitative and qualitative analysis of cultural data.
A rigorous deontological discussion is initiated on Sustainable Digital Technologies, with the aim of reflecting on the ethical dimension of Digital Humanities. The current challenges posed by the use of Artificial Intelligence in cultural and humanistic fields are also discussed.
Introduction to the Idea of Digital Humanities
Digital Textualities
Digital Sound and Music
Digital Art
Digital Archaeology
Digital Architecture
Digital Philosophy
Artificial Intelligence and the Humanities
Ethics and Sustainability
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Attendance at lectures led by the professor | 36 | 1.44 | CA05, KA03, KA05, KA06, SA08, SA37, CA05 |
Type: Supervised | |||
Classroom practical work | 34 | 1.36 | CA05, KA04, KA05, SA06, SA07, SA37, CA05 |
Type: Autonomous | |||
Personal Study. Bibliographical consultation | 60 | 2.4 | CA04, CA05, KA03, KA04, KA06, SA06, SA07, SA08, SA37, CA04 |
Attendance at theoretical classes led by the professor.
Attendance at seminar sessions and practical work with computers and specific software led by the professor.
Classes are held in a specialized computer lab.
Comprehensive reading of texts.
The student must dedicate independent effort to consulting specialized bibliography. Part of the documentation is in English.
Class debates, moderated by the teaching staff, on the most significant topics.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Presentation of a critical essay using Generative Artificial intelligence | 40% | 10 | 0.4 | CA04, CA05, KA03, KA04, KA05, KA06, SA06, SA07, SA08, SA37 |
Presentation of a written essay debating some relevant subject | 30% | 5 | 0.2 | CA04, CA05, KA03, KA04, KA05, KA06, SA06, SA07, SA08, SA37 |
Presentation of written essais commenting some text | 30% | 5 | 0.2 | CA04, CA05, KA03, KA04, KA05, KA06, SA06, SA07, SA08, SA37 |
The evaluation methodology for this master’s course is based on the active and reflective participation of students. Their analytical skills will be assessed through written comments on articles and bibliographic references proposed by the teaching staff. In addition, students will prepare critical summaries of class debates, where they must express and argue their own position in relation to opposing opinions, demonstrating their ability for dialogue and critical thinking. Another key component of the assessment will be a critical assignment involving the use of Generative Artificial Intelligence tools, applied to one of the topics discussed during the course. This assignment must include a reflection on the limitations and potential of these technologies within the field of Digital Humanities. The specific details regarding format, criteria, and deadlines will be explained and discussed in class by the professor.
Single assessment is allowed, upon request, following the procedure approved by the Dean of the Faculty of Arts.
At the time each assessment activity is carried out, the instructor will inform students (via Moodle) of the procedure and the date for reviewing grades.
Recovery procedure: only the final assignment (second case study) is eligible for reassessment. This decision will be made on a case-by-case basis following a personal interview between the student and the professor.
The submission date for reassessment will also be determined on a case-by-casebasis and by mutual agreement between the professor and the student.
The student will receive a grade of "Not assessable" if they fail to submit any of the obligatory essays.
If a student commits any irregularity that may significantly alter the grade of an assessment activity, that activity will be graded with a 0, regardless of any disciplinary proceedings that may be initiated. If multiple irregularities occur in the assessment activities of the same course, the final grade will be 0.
This course recommends the use of Artificial Intelligence (AI) technologies as an integral part of the development of assignments, provided that the final result reflects a significant contribution from the student in terms of analysis and personal reflection. The student must: (i) identify which parts were generated using AI; (ii) specify the tools used; and (iii) include a critical reflection on how these tools influenced the process and the final outcome of the activity.
A lack of transparency in the use of AI in this graded activity will be considered academic dishonesty and will result in a grade of 0 with no possibility of recovery, or more serious sanctions in severe cases.
Detailed references will be presented thorugh UAB Virtual Campus-MOOC.
Main general references:
There is not a single software for this first introductory course
A list of Generative Artificial Intelligence apps that can be used during the course
ChatGPT, https://chat.openai.com/
LeChat https://mistral.ai/
PerplexityAI, https://www.perplexity.ai
Claude, https://claude.ai/
DeepSeek, https://deepseek.com/
Gemini, https://gemini.google.com/
Cohere, https://cohere.com
Sherpa.ai, https://sherpa.ai/
BotXO, https://botxo.ai/
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(SEMm) Seminars (master) | 1 | Spanish | first semester | afternoon |