Automation in assessment is a fast-emerging AI research field that raises ethical issues for education. So far, dominant approaches to ethics have led to the development of numerous ethical guidelines to fix issues that the deployment of AI systems might introduce. This chapter critically examines the ethical considerations of AI automation in education by focusing on assessment and Automated Grading Systems (AGS). To this end, a relational approach to ethics is discussed that focuses on AGS’ specificities regarding data, algorithms, and assessment and the context where these systems are used, including situations and purposes, actors and relations, and time and place.

Automated grading systems (AGSs) have gained attention for their potential to streamline assessment in higher education. However, their integration into university assessment practice poses challenges, particularly for teachers in computer science seeking to balance their workload while ensuring an adequate and fair assessment of students’ programming skills and knowledge. The present study focuses on individuals with expertise in developing, using, and researching AGSs in higher education, whom we refer to as “AGS experts”. Through semi-structured interviews, we examine how the AGSs they engage with impact their work and assessment practices in computer science education. Drawing on the concept of breakages, we argue that while AGS experts invest time and effort in developing these systems, enticed by the promises of more efficient workload management and improved assessment practices, the actual use may introduce tensions leading to breakages disrupting assessment practices. Our findings illustrate the complexities and the potential impact the deployment of AGS brings to assessment practices within a public university setting and discuss the implications for future research. 

AI chatbots have recently fuelled debate regarding education practices in higher education institutions worldwide. Focusing on Generative AI and ChatGPT in particular, our study examines how AI chatbots impact university teachers' assessment practices, exploring teachers' perceptions about how ChatGPT performs in response to home examination prompts in undergraduate contexts. University teachers (n = 24) from four different departments in humanities and social sciences participated in Turing Test-inspired experiments, where they blindly assessed student and ChatGPT-written responses to home examination questions. Additionally, we conducted semi-structured interviews in focus groups with the same teachers examining their reflections about the quality of the texts they assessed. Regarding chatbot-generated texts, we found a passing rate range across the cohort (37.5 - 85.7%) and a chatbot-written suspicion range (14-23%). Regarding the student-written texts, we identified patterns of downgrading, suggesting that teachers were more critical when grading student-written texts. Drawing on post-phenomenology and mediation theory, we discuss AI chatbots as a potentially disruptive technology in higher education practices.

The emergence of generative artificial intelligence (GAI) has given rise to diverse narratives about its transformative potential in higher education. Despite widespread speculation about how GAI might change teaching and learning, there is a significant gap in understanding how GAI artefacts are perceived in educational practices, particularly from the perspective of university teachers. This study investigates how GAI mediates teachers’ practices and reconfigures professional roles. Drawing on post-phenomenology and technological mediation theory, we focus on university teachers’ experiences and perceptions of GAI in higher education. Twenty-four university teachers participated in workshops involving assessment exercises with GAI-generated outputs, followed by focus group interviews discussing the challenges and opportunities posed by GAI. Findings reveal that GAI prompts teachers to reassess established practices, particularly in relation to assessment, while confronting ethical concerns regarding fairness, trust, and quality. Teachers described their initial engagement with GAI as transformative yet challenging, as they navigated uncertainties about their roles while prioritising students’ learning and development. By capturing teachers’ voices during this pivotal period, the study contributes to the growing body of research on AI's role in higher education and provides a nuanced understanding of its impact on teaching and learning.