Artificial Intelligence (AI) tools and systems come with bold promises to enhance human development and learning, while at the same time they raise significant fears about deskilling the workforce through an increasing dependence on semi-automated systems (Cerratto Pargman et al., Citation2024; Furendal & Jebari, Citation2023). To date, however, there is a notable paucity of scholarly work examining AI tools and systems in the context of teacher education. This Special Issue responds to that recognized gap in the literature, and, more precisely, the insufficient theoretical and empirical grounding of AI within teacher education contexts and the development of the teacher profession. The studies that have begun to emerge underscore not only the scope of this gap but also its urgency, revealing a landscape in which AI is entering educational settings faster than either research or policy can adequately respond. The situation prompts a broad range of questions: How will AI impact teachers’ professional development? On what foundation will knowledge about AI in teacher education be built? What perspectives and prevailing discourses will form theoretical, practical, and ethical knowledge of an AI infused teacher education context? Are there risks involved if teachers’ practical knowledge translates into the adoption of digital teaching resources for teaching about AI or the integration of AI EdTech into teaching.

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.

The increasing surveillance by big tech companies or/and governments has raised concerns about the democratic and participatory structure of the datafied society. Meanwhile, over the course of the past decade, various bottom-up civic tech and digital civic initiatives have emerged to tackle pressing local issues, such as air pollution and disaster response, often via technology-mediated data collection, curation, analysis, design and visualisations, thus promoting democratic participation. In this article, we discuss how these data are understood in diverse contexts beyond the realm of civic tech and digital civics. In doing so, we explore the potential and limits of civic data by exploring the intersections of and differences between civic data and adjacent data-related concepts often used by civic tech communities themselves: counterdata and open data. Through our discursive exploration of these three data concepts, we conclude that understanding is limited when it comes to determining which data are 'civic', and that discussion of questions related to power structures, diversity and inclusion and infrastructuring of civic data has been minimal.

Artificial intelligence (AI) chatbots trained on large language models are an example of generative AI which brings promises and threats to the higher education sector. In this study, we examine the emerging research area of AI chatbots in higher education (HE), focusing specifically on empirical studies conducted since the release of ChatGPT. Our review includes 23 research articles published between December 2022 and December 2023 exploring the use of AI chatbots in HE settings. We take a three-pronged approach to the empirical data. We first examine the state of the emerging field of AI chatbots in HE. Second, we identify the theories of learning used in the empirical studies on AI chatbots in HE. Third, we scrutinise the discourses of AI in HE framing the latest empirical work on AI chatbots. Our findings contribute to a better understanding of the eclectic state of the nascent research area of AI chatbots in HE, the lack of common conceptual groundings about human learning, and the presence of both dystopian and utopian discourses about the future role of AI chatbots in HE.

 Emerging AI technologies are changing teachers’ assessment practices and posing higher education institutions with novel ethical dilemmas. While frameworks and guidelines promise to align technology with moral and human values, the dilemma of how AI may impact existing valuing practices is often overlooked. To examine this gap, we conducted an interview study with university teachers from different disciplines at a university in Sweden. Following a semi-structured study design, we explored university teachers’ anticipations of AI in assessment and examined how emerging AI technologies may reconfigure the fit between values, challenges, and activities situated in everyday assessment contexts. Our findings suggest that anticipated AI, including automation and AI-mediated communication and grading, may amplify and reduce teachers’ possibilities to align activities with professional, pedagogical, and relational values and solve current challenges. In light of the study’s findings, the paper discusses potential ethical issues in the anticipated shifts from human to automated assessment and possible new and reinforced challenges brought by AI for education. 

Conceptualizations of postdigital responsibility are in flux, configured by dialectic debates about what constitutes responsibility in an increasingly technologized world transversed by multiple crises. This entry describes postdigital responsibility through three standpoints: (1) the classical theories of responsibility and the various types of responsibility distinguished in moral philosophy, (2) postphenomenological understandings of responsibility enacted in human-technological mediations, and (3) critical posthumanism and environmental philosophy. These standpoints, that are by no means exhaustive, identify some of the salient traits of postdigital responsibility as mediated, hybrid, distributed between humans and digital technologies, and situated in relationships constituting ecosystems and networks.

While student privacy is frequently a topic of concern in studies about data-powered technologies in higher education, we still know little about handling student privacy in online proctoring systems (OPS). To better understand the challenges associated with student privacy in higher educational practice, we conducted an interview study examining various stakeholders’ understandings of privacy in OPS. We interviewed ten stakeholders–including teachers, students, and administration staff, such as the head of the department, the head of the IT department, and examination administrators–directly involved in the procurement and use of an online examination platform with proctoring features, in one of the largest universities in Scandinavia, to investigate stakeholders’ privacy perceptions, privacy concerns, privacy awareness, and perceived responsibility regarding the privacy practices of OPS. The findings show the participants perceive privacy in OPS as a seclusive and anonymous state of being, a way to control information, and an individual right. The results also point to stakeholders’ concerns regarding collecting sensitive information about the students, the possibility of information misuse, and improper access to students’ personal information. Furthermore, the study’s results identify trade-offs between the stakeholders’ concerns for privacy and the benefits of using OPS for examinations in higher education. This study underscores the need for higher education institutions to involve students and educators in procuring and deploying OPS and develop strategies for cultivating privacy awareness and responsible privacy practices. Finally, implications for developing responsible digital practices in higher education are discussed.