HLVK03862U Litteraturvidenskab: Forskningsemne: Forventningsæstetik

Årgang 2024/2025
Engelsk titel

Research Topic: Anticipative Aesthetics




In this course, we will explore the implications of algorithmic technology and culture on the concept of aesthetics and its anticipative manifestations in the generation of cultural objects and narratives. We will delve into the research topic of “anticipative aesthetics,” tracing its philosophical roots to technocultural entanglements and practical implications.


Anticipation, a concept that refers to thinking and living towards the future, is a defining characteristic of our current era (Adams, Murphy, and Clarke 2009). The influence of data-driven algorithmic technologies on the cultural dynamics of anticipation has profound implications for aesthetic production. From the anticipatory logics embedded in the algorithms that drive aesthetic filters on Instagram to the predictive text suggestions in our everyday digital communications, we have recently observed how Large Language Models (LLMs) automate and amplify the generation of aesthetic materials such as text, images, and videos. This automation is based on algorithmic processes that incorporate anticipative logics and functionalities into aesthetic materials, objects, and infrastructures.


With the recent algorithmic conditions of aesthetic production and experience in mind, we can pursue a dual interpretation of aesthetics: as a theory of perception, but also as a theory rooted in rationality (Jacques Rancière; Yuk Hui; Keren Gorodeisky and Eric Marcus). Drawing from aesthetics as the philosophical study of the essence of art, we begin with philosophical explorations into time, perception, and experience (Henri Bergson; Martin Heidegger; Hannah Arendt; Elizabeth Grosz; Sandra Lee Bartky) before transitioning into technophilosophical perspectives on the impact of digital technology on the production of today’s (digital) aesthetic objects.


Through these readings—and by engaging with emerging research fields such as Critical AI, Critical Future Studies, Algorithmic Aesthetics, and Algorithmic Culture—we will develop a shared vocabulary around various concepts of anticipation while distinguishing their differences: prediction, foreseeing, expectation, contemplation, forecasting, imagination, and prospection. We aim to identify these variations within cultural practices involving algorithmic technology and learn about the algorithmic functionality behind aesthetics.


The course is structured through a combination of theoretical inquiry, practical exercises, and meta-reflexive exercises. A consistent objective is to develop effective learning strategies when working with Large Language Models. This includes critical considerations about the role of algorithms as co-creators in content generation, reflecting on the choices made during the use of algorithms in aesthetic production, and metacognitive reflections on the process of knowledge generation for each student’s individually written final paper—a key component of the assessment.

Filosofisk teori (Henri Bergson, Hannah Arendt, Elizabeth Grosz).

Teknologifilosofisk teori (Bernard Stiegler, Yuk Hui).

Æstetisk teori (Jacques Rancière, Lev Manovich and Emanuele Arielli)

Feministisk teori (Karen Barad, Sandra Lee Bartky)

Teori om AI og algoritmisk kulturel betydning (Wendy Chun, Kate Crawford, Tiziana Terranova)

The teaching will consist of a mix of lectures, guest lectures, group work, collective case studies, student presentations, a workshop on the functionality of algorithmic technology, and a few excursions.

The course is based on learning principles from aesthetic learning. Informed by John Dewey’s (1938) theory about the role of experience in relation to learning, aesthetic learning is about learning through the sensations and emotions that are activated in experiences with aesthetic material (Lindström 2012). In this case, our experiences with generating aesthetic material and narratives through practice with algorithmic technology, for example through Large Learning Models.
Hvis du skal skrive BA-projekt, kan du bruge dette kursus som inspiration. Du skal tilmelde dig BA-projekt forløbet samt eksamen, og samtidig kontakte Liselotte på studie_litvid@hum.ku.dk for at blive tilmeldt inspirationskurset.
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