Finding floors

: cartographies of pedagogical encounters with a posthuman teacherbot

Abstract

Digital automation is continuing to shape teaching practices in higher education. This paper problematises the way that automated teaching is understood by arguing that the question of automated teaching is not simply concerned with the transmission of knowledge between humans and technology; rather, it can be understood as a posthuman question, concerned with automated teaching as an assemblage of relational encounters. Much of the research to date has conceptualised the automated teacher as a neutral object for human gain that transmits knowledge in an efficient and scalable manner. This failure to acknowledge algorithmic agency renders the automated teacher as empirically ‘missing’. In contrast I developed a chatbot, Flors the teacherbot, to co-teach with me on an Interactive Narrative module at Ulster University. Flors imbues posthuman critical theory (Braidotti, 2019), which conceptualises Flors as an equal entity with the capacity to shape knowledge processes. Cartographies draw upon the qualitative data to map the pedagogical encounters with this second-year group of 21 undergraduate students as they collectively author a narrative within Flors. It is at a whimsical juncture, at which Flors remixes the story, that Flors’ agential capacity as a co-author is encountered. Through such intra-relational teaching moments, student expressions of adequate understandings around Flors as an active subject emerge. The findings suggest that when Flors the automated teacher is understood as an active posthuman subject it is possible to understand automated teaching as something other than a one-directional form of control – rather, as a relational encounter with freedom. Finally, I propose a framework for automated teaching comprising five defining pedagogical features: (1) a call to action, (2) relational co-teaching, (3) educational values, (4) political values, and (5) ethical values. This research has theoretical significance for pedagogical innovation in automated teaching.

Date of Award	Jun 2022
Original language	English
Supervisor	Adrian Hickey (Supervisor), Helen Jackson (Supervisor) & Aodheen McCartan (Supervisor)