58勛圖

CO4042 The Computational Muse: Literature and AI

Academic year

2026 to 2027 Semester 1

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 10

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Planned timetable

To be confirmed

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr C A Godbarge

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Clement Godbarge

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

How do algorithms change the way we read, write, and create? Can we 'vibe-code' our way into literary creativity? Is there even a formula to literary invention? In this module, we trace the rich history of algorithmic approaches to literature from ancient Greece to contemporary AI. We explore how classical memory systems, combinatorial techniques, and constrained writing methods like those of the Oulipo group have set the stage for today's computer-assisted creativity. Through hands-on exercises, we critically examine intersections between human creativity and systematic processes. Students will evaluate the impact of natural language processing (NLP) and large language models (LLMs) on both writing and reading practices, reflecting on the concepts of authorship. By the end, you will have gained unique insights about literary invention and imitation, and whether machines can truly create, placing contemporary digital practices into a historical context.

Assessment pattern

Coursework - 100%

Re-assessment

Coursework - 100%

Learning and teaching methods and delivery

Weekly contact

1 seminar per week (during 10 weeks) + 1 practical class in a computer room per week (during 2 weeks)

Scheduled learning hours

15

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

132

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Trace and contextualize the historical development of algorithmic approaches to literature from ancient Greece to contemporary AI systems.
  • Analyze and apply key techniques including Art of Memory, combinatorial methods, Oulipo constraints, and computational approaches to literary texts.
  • Demonstrate practical understanding of how NLP algorithms and LLMs function in literary analysis and production through hands-on exercises.
  • Critically evaluate changing conceptions of authorship, originality, and creativity across different historical periods and technological contexts.
  • Assess the aesthetic, ethical, and cultural implications of automated and semi-automated approaches to literary production.
  • Articulate a theoretically informed perspective on the relationship between human creativity and computational systems in literature and academic writing.