APG5107 · Digital language: Online communication and artificial intelligence

Overview

Digital language is where human communication and computer systems meet. In this unit you will learn about who interacts online, what data they produce, and how that data shapes the intelligent technologies now working and writing alongside us. You will gain a thorough understanding of the structures, affordances, and practices that define digital language, including themes such as identity, audience dynamics, digital literacy and multimodality, and you will build the skills to analyse digital communication through practical examples in every assessment task. You will see how the richness of online communication can be turned into the data that Artificial Intelligence (AI) systems learn from, and how the models built from that data pick up the patterns, conventions and social signals you have spent the semester analysing. By the end, you will be able to account not only for what these systems produce, but for where it comes from. No coding experience is needed. You will leave the unit able to analyse digital communication with precision, to work critically with language data and AI tools, and to apply both across domains ranging from education, media and translation studies to research and technology development.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Within semester assessment: 100%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Explain key terms and concepts used to engage with digital language;
2. Discuss how the interplay between structures, affordances and practices shape communicative events involving digital language;
3. Apply insights from this unit to personal and professional contexts;
4. Analyse digital discourse data, properly synthesising and contextualising the socio-cultural factors that shape digital language;
5. Engage in informed debates about digital language targeted to different audiences – professional and lay.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.

Student experience, not official rules. Nothing here changes what the Handbook says.