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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. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this commentary is to comment on Fischer's et al. (2022) Design/methodology/approach: This commentary responds to Fischer's et al. (2022) call on envisioning alternate conceptualizations of learning for the digital era. In doing so, the author argues for reconsidering learning in its socio-material condition, situated and made of a web of social and technological relations. In this context, the author takes a relational lens on learning to interrogate taken-for-granted views of (1) personalizing data increasingly used for student learning, (2) emerging educational infrastructures for higher education and (3) the student–teacher relationship mediated by data and algorithms. Findings: In this commentary, the author suggested unpacking assumptions about learning that get reflected in the design and discourses about socio-technical arrangements and transformations in education. Taking the example of personalized learning, the author has illustrated a relational mode of thinking that leads the author to argue that, renewed definitions of learning must be discussed multidimensionally and, most importantly, situated in the material world that learning is already part of. Research limitations/implications: Following Fischer et al. (2022, this issue), the author agrees that the focus should be on finding "new ways of organizing learning by exploring opportunities for radically new conceptualizations and practices." In order to do that it is of utmost importance to problematize the social and material conditions that actively configure learning today and infrastructure tomorrow's learning. Hopefully, these observations will entice others to discuss further the educational transformations at stake in the age of datafication and algorithmic decision-making. Originality/value: The author argues for reconsidering learning in its socio-material condition, which is situated and made of a web of social and technological relations. In this context, the author argues that any attempt to reconceptualize learning from a transformational perspective in the 21st century, as mentioned by Fischer et al. (2022), needs to interrogate views and assumptions about the socio-technical relationships researchers, practitioners and educators are contributing to via their practices and discourses. [ABSTRACT FROM AUTHOR]

Abstract: 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. [ABSTRACT FROM AUTHOR]

This paper engages with the meanings and practices of digital sovereignty that four social collectives are building and enacting in Latin America. Through a qualitative methodological approach based on semi-structured interviews with founding members of the selected collectives, we sought to know how they conceive and carry out activities of digital sovereignty on a daily basis. Among the research findings, the identification of five articulating axes in the work of these organizations stands out, namely: 1) conception / proposal of particular digital sovereignty; 2.) ways to achieve/achieve digital sovereignty; 3.) criticism and resignification in the ways of making digital sovereignty; 4.) agents involved and 5.) relationship with other groups and/ or organizations interested in digital sovereignty. [ABSTRACT FROM AUTHOR]

Understanding students' privacy concerns is an essential first step toward effective privacy‐enhancing practices in learning analytics (LA). In this study, we develop and validate a model to explore the students' privacy concerns (SPICE) regarding LA practice in higher education. The SPICE model considers privacy concerns as a central construct between two antecedents—perceived privacy risk and perceived privacy control, and two outcomes—trusting beliefs and non‐self‐disclosure behaviours. To validate the model, data through an online survey were collected, and 132 students from three Swedish universities participated in the study. Partial least square results show that the model accounts for high variance in privacy concerns, trusting beliefs, and non‐self‐disclosure behaviours. They also illustrate that students' perceived privacy risk is a firm predictor of their privacy concerns. The students' privacy concerns and perceived privacy risk were found to affect their non‐self‐disclosure behaviours. Finally, the results show that the students' perceptions of privacy control and privacy risks determine their trusting beliefs. The study results contribute to understand the relationships between students' privacy concerns, trust and non‐self‐disclosure behaviours in the LA context. A set of relevant implications for LA systems' design and privacy‐enhancing practices' development in higher education is offered. Practitioner notesWhat is already known about this topic Addressing students' privacy is critical for large‐scale learning analytics (LA) implementation.Understanding students' privacy concerns is an essential first step to developing effective privacy‐enhancing practices in LA.Several conceptual, not empirically validated frameworks focus on ethics and privacy in LA.What this paper adds The paper offers a validated model to explore the nature of students' privacy concerns in LA in higher education.It provides an enhanced theoretical understanding of the relationship between privacy concerns, trust and self‐disclosure behaviour in the LA context of higher education.It offers a set of relevant implications for LA researchers and practitioners.Implications for practice and/or policy Students' perceptions of privacy risks and privacy control are antecedents of students' privacy concerns, trust in the higher education institution and the willingness to share personal information.Enhancing students' perceptions of privacy control and reducing perceptions of privacy risks are essential for LA adoption and success.Contextual factors that may influence students' privacy concerns should be considered. [ABSTRACT FROM AUTHOR]

In the last decades, researchers in Human-computer interaction (HCI) took numerous efforts to investigate people's social media interaction. To understand how researchers in HCI have studied social media interaction, we examined 149 peer-reviewed articles published between 2008 and 2020 in major HCI conference proceedings and journals. We systematically reviewed the methodologies HCI researchers applied, the research topics these methods covered, and the types of data collected. Through the analysis, we make three contributions: We (1) pinpoint the topic trends by identifying three phases in the study of social media interaction in HCI. Namely, the early phase (2008–2012) focused on user behavior, the growing phase (2013–2016), focused on privacy and health, and the latest phase (2017–2020) focused on design. (2) We map methodological trends in the study of social media interaction in HCI. We also illustrate the trends in relation to the types of data collected in the selected works and, (3) identify underexplored study areas. • The systematic review of HCI research methods applied to study social media interaction includes 149 peer-reviewed articles published between 2008 and 2020 in major HCI venues and journals. • The review identifies three research phases: the early phase focused on user behavior (2008-2012), the growing phase on privacy and health (2013–2016), and the latest phase on design (2017–2020). These phases differ according to the research methods applied and the studied topics in each phase. • Questionnaires and surveys (40 out of 149) and interviews and focus groups (39 out of 149) are the most popular methods in the study of social media interaction in HCI over time. Physical trace logs and diary documentation are the least used. • Twenty-two articles out of 149 (15%) apply mixed methods. These articles show high potentials to go beyond one dimension of social media data to the various dimensions of the context behind it. • No HCI methods, explicitly building upon the feminist approaches, were found for the study of social media interaction although the great research opportunities that the feminist lens brings to this research domain. [ABSTRACT FROM AUTHOR]