AI Agents, Humans and Untangling the Marketing of Artificial Intelligence in Learning Environments

This exploratory study identifies the tangling of proposed relationships between human and non-human agents by providing an analysis on how AI technologies are marketed for learning subjects through a critical discourse analysis of corporate advertisements. We ask: Amid these emerging technologies, how are humans and AI technologies framed as agents with agency? How are learners framed by corporate advertising as part of this blurring? We used a public, open-access cultural analytics database and repository, Fabric of Digital Life (‘Fabric’, https://fabricofdigitallife.com/), to identify a set of artifacts as a dataset for such analysis. Results indicate that advertising promotes corporate products while also promoting idealized social practices for human-computer interaction and human-robot interaction in learning contexts. Using AI to automate relationships between students and teachers frames AI systems as authorities in both robot and non-robot platforms, blurring and minimizing student and instructor agency in learning environments

Learning About Metadata and Machines: Teaching Students Using a Novel Structured Database Activity

Machines produce and operate using complex systems of metadata that need to be catalogued, sorted, and processed. Many students lack the experience with metadata and sufficient knowledge about it to understand it as part of their data literacy skills. This paper describes an educational and interactive database activity designed for teaching undergraduate communication students about the creation, value, and logic of structured data. Through a set of virtual instructional videos and interactive visualizations, the paper describes how students can gain experience with structured data and apply that knowledge to successfully find, curate, and classify a digital archive of media artifacts. The pedagogical activity, teaching materials, and archives are facilitated through and housed in an online resource called Fabric of Digital Life (fabricofdigitallife.com). We end by discussing the activity’s relevance for the emerging field of human-machine communication

Transfer learning for multicenter classification of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a lung disease which can be quantified using chest computed tomography (CT) scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multi-center dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains, can further improve the results. To encourage further research into transfer learning methods for classification of COPD, upon acceptance of the paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copdComment: Accepted at Journal of Biomedical and Health Informatic

Metaphors, mental models, and multiplicity: Understanding student perception of digital literacy

This study examines student perception of digital literacy from their engagement with the Fabric of Digital Life, a digital archive of emerging technologies. Through grounded theory analysis we identified the ways students make sense of an unfamiliar technology. Our results show students assign metaphors to understand a new digital platform, apply mental models transferred from previous conceptual domains onto new technologies, and express multiply-layered approaches that facilitated their digital literacy development––an indication for instructors to orient toward an expansive description of digital literacy that caters to student learning needs as well as their professional futures

Female economic dependence and the morality of promiscuity

This article is made available through the Brunel Open Access Publishing Fund. Copyright @ The Author(s) 2014.In environments in which female economic dependence on a male mate is higher, male parental investment is more essential. In such environments, therefore, both sexes should value paternity certainty more and thus object more to promiscuity (because promiscuity undermines paternity certainty). We tested this theory of anti-promiscuity morality in two studies (N = 656 and N = 4,626) using U.S. samples. In both, we examined whether opposition to promiscuity was higher among people who perceived greater female economic dependence in their social network. In Study 2, we also tested whether economic indicators of female economic dependence (e.g., female income, welfare availability) predicted anti-promiscuity morality at the state level. Results from both studies supported the proposed theory. At the individual level, perceived female economic dependence explained significant variance in anti-promiscuity morality, even after controlling for variance explained by age, sex, religiosity, political conservatism, and the anti-promiscuity views of geographical neighbors. At the state level, median female income was strongly negatively related to anti-promiscuity morality and this relationship was fully mediated by perceived female economic dependence. These results were consistent with the view that anti-promiscuity beliefs may function to promote paternity certainty in circumstances where male parental investment is particularly important