Digital lab for the social sciences

Digitalni laboratorij za družboslovce

Course credits:

6.00 ECTS / 60 (30 hours of lectures, 30 hours of exercises, 0 hours of seminars, 0 hours other forms of work)

Course holder:

izr. prof. dr. Vesna Dolničar

Type:

Elective expert

Language:

English

Semester:

First semester

Study degree

1. level

Course execution:

• Undergraduate Programme of Communication studies - Marketing Communication and Public Relations
• Undergraduate Programme of Cultural studies - Studies of Culture and Creativity
• Undergraduate Programme of Defence Studies
• Undergraduate Programme of International Relations
• Undergraduate Programme of Journalism
• Undergraduate Programme of Media and Communication Studies
• Undergraduate Programme of Political Science - Public Policies and Sdministration
• Undergraduate Programme of Political Science - Studies of Politics and the State
• Undergraduate Programme of Sociology
• Undergraduate Programme of Sociology - Human Resources Management

Prerequisits:

Students are allowed to participate in the course provided that they have completed the enrolment procedure.

Objectives and competences

The aim of the course is to familiarize students with the use of digital technologies and generative AI in academic and research work, and to equip them with the knowledge and skills required for the effective, critical, and responsible use of digital tools in academic contexts. Students will acquire the following knowledge and competences during the course: (1) Understanding of the basic principles of generative AI, large language models, and their possibilities and limitations in the study process; (2) Skills in the use of digital and AI tools for literature search, reading and understanding scientific texts, editing academic writing, and organizing academic work; (3) Competences in project management, the management of time, attention, and productivity, and the use of digital tools for individual and collaborative work in virtual environments; (4) Basic understanding of web technologies and markup languages, as well as the ability to use generative AI in the creation of simple web solutions; (5) Knowledge and skills for searching, evaluating, organizing, and using online information resources, bibliographic databases, and publicly available social science data repositories; (6) Understanding of open-source and free software principles and the ability to critically assess the quality and credibility of digital content; (7) Basic competences in the use of spreadsheets and online tools for the organization, analysis, and visualization of social science data.

Content (Syllabus outline)

The course addresses the following topics: • The use of generative artificial intelligence (AI) in the study process, including the basics of large language models, the design of effective prompts, and the use of AI tools for literature search, reading and understanding scientific texts, and editing academic writing. • Project management in the study process, including project definition, goal setting, task breakdown, timeline planning, coordination of work, and the use of digital tools for project collaboration. • Management of time, attention, and productivity in digital learning environments, with emphasis on self-regulated learning, work planning, distraction management, the organization of work habits, and sustainable productivity. • Collaboration, communication, and collaborative writing in virtual environments, including models of collaborative writing, team dynamics, meeting organization, feedback practices, and the use of digital and AI tools for collaboration and documentation of joint work. • Fundamentals of web technologies and markup languages, with emphasis on HTML and CSS, the structure of web documents, the separation of content and presentation, and the creation of simple static web pages. • The use of generative AI creation of web pages and apps (“vibe coding”), with emphasis on the critical assessment of generated code, understanding the limitations of generative models, and the responsible use of AI tools in the development of simple web solutions. • Familiarity with and use of publicly available social science data repositories and online data repositories, as well as the basic principles of open-source software and open digital ecosystems in the context of academic and research work. • The use of spreadsheets and online tools for the basic analysis and visualization of social science data, including data organization, the use of filters, functions, pivot tables, basic bivariate analyses, and the interpretation of data visualizations. • The functioning of web search engines, basic and advanced information retrieval strategies in web search engines, and the evaluation of quality, reliability, relevance, and credibility of web resources and AI generated content. • Knowledge and use of bibliographic and full-text databases, online repositories, and strategies for searching academic resources with AI chatbots.

Intended learning outcomes:

Students define and relate the concepts of digital technologies, generative AI, web technologies, and information resources. They interpret data and information from online sources and critically assess their quality and credibility. They use digital and AI tools for literature searching, organizing academic work, collaboration, creating web content, and analyzing and visualizing data. They critically evaluate the opportunities and limitations of these tools in academic and research contexts.

Learning and teaching methods:

Lectures, practical assignments, individual and group assignments, e-learning, use of digital and AI tools

Assessment

practical assignments: 50 % written assessment: 50 %

Obligatory literature

Additional literature