• Inactive for Year: 2025/26

• Module Leader(s): Dr Tom Schofield
• Lecturer: Professor Areti Galani, Mr Chris Stokel-Walker, Miss Rachel Maclean, Dr Paulina Kuranchie, Dr Nick Rush-Cooper

• Owning School: Arts & Cultures
• Teaching Location: Newcastle City Campus

Semesters

Your programme is made up of credits, the total differs on programme to programme.

Aims

AI, Culture and Society introduce s students to critical approaches to the study and use of Artificial Intelligences (AIs), including the underlying technologies of machine learning and big data. It provides students a foundation in understanding how AIs are developed and used, a comprehensive knowledge of key debates over their current and future uses, and hands-on experience interacting with AIs in creative and research settings.

Outline Of Syllabus

Topics covered in the lectures and workshops address leading edge concepts and skills related to the role of AIs in contemporary social and cultural contexts. These may include:
•       Origins, histories and possible futures of AI and machine learning
•       Types of AIs and their uses (e.g. generative and discriminatory)
•       Cultural Tropes of AI
•       Problems with diversity and normativity in Generative AIs and Big Data
•       Performativity and AI: feminist, queer, anti-racist approaches
•       Approaches to AI training
•       AI in state and corporate surveillance
•       Adversarial AIs and counter-hegemonic resistance
•       AI and sustainability

Teaching Methods

Teaching Activities

Teaching Rationale And Relationship

Asynchronous materials, alongside tailored readings, serve as an introduction to each session topic, providing a foundation for both overarching and specific topics (K1, K2, K3, K4). These are contextualised at present-in-person lectures, where academic and practical aspects of the topics are developed.

In-person workshops are used to further explore specific examples from the preparatory and lecture materials, with an emphasis on hands-on experience with tools (K1, K4, S1, S2) and reflecting on their social and ethical implications (K2, K4). Small group sessions also allow for exploring data and models in different cultural contexts and reflecting on their impact on future uses (K2, K3, K4).

The opening session-blocks focus on providing an organisational and conceptual framework for Artificial Intelligence / Machine learning in the context of media and society (K1, K2), which subsequent sessions develop case-studies around major debates in the field (K2, K3, K4) that fit into this, and unpack historical, present-day and future-facing impacts of digital technologies across various contexts (K2, K3, K4).

Drop-in surgeries support this work through opportunities for feedback and feed-forward from module staff, allowing students to articulate and reflect on their ideas and learning individually and in groups (S1, S3, S4), further develop technical skills related to the learning materials (S4), and to plan and allocate their time and resources effectively in completion of the summative work.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment

Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Assessment Rationale And Relationship

Formative Assessment
The problem-solving exercise enables students to engage with real world data and AI tools in relation to concepts and debates set out in lectures, preparatory materials and seminars (S1, S2, S3), while developing good practices in collaboration (S4). It facilitates this by requiring students to work in a small team to produce an intentional result with an AI/ML and reflect on their process using technologies explored in workshops and online materials.

The collaborative format is geared towards fostering participation from students from a range of geographical (and social) backgrounds, allowing them to develop their ideas – and receive feedback and feed-forward – in preparation for the summative assessment (S3, S4).

Summative Assessment | Design & Creative Project (80%) (K1, K4, S1, S2, S4).
The creative project builds on the technical, conceptual and collaborative skills from the formative assessment while allowing students more flexibility in the focus of their research into AI and related technologies. Task specific assessment are as follows:
•       Evidence of research and incorporation of relevant academic and technical sources related to ethical and cultural impacts of AI.
•       Demonstration of intentional and proficient use of AI/ML in creative exploration of research data.
•       Evidence of successful collaboration on original research into the ethical uses of AI/ML.


Summative Assessment | Reflective Log (20%): (K1, K2, K4, S3, S4)
The individual written report allows for a degree of flexibility in presentational style to support different cultural and academic backgrounds through developing capacity in critical self-reflection (K4, S3). Task specific assessment criteria are as follows:
•       Evidence of individual experience, expanded knowledge and expertise gained during the collaborative project.
•       Critical analysis of contribution, drawing from a range of primary and secondary research and perspectives.
•       Evidence of successful collaboration on original research into the ethical uses of AI/ML.
•       Coherence, tone and presentation including appropriate use of language for post-graduate study and correct Harvard referencing (K1, K2, K4, S3, S4).

Reading Lists

• SAC8010's Reading List

Timetable

• Timetable Website: www.ncl.ac.uk/timetable/
• SAC8010's Timetable