In the humanities, histories of technology generally examine objects through representations, such as patents, illustrations, fictions, and newspapers. With objects flattened on the page, this approach rarely attends to how technologies worked in context and over time. In response, we prototyped a series of early technologies, which cannot be immediately accessed in museums or archives; they have been lost, destroyed, or broken, yet they deeply inform present day devices. In this talk, we present three of our prototypes: an early wearable technology, an early wire recorder, and an early reading machine. The early wearable prototype features a skull cravat-pin (designed by Gustave Trouvé and Auguste-Germain Cadet Picard in 1867), which was said to snap its jaw and roll its eyes. We replicated the mechanism of the skull using electromagnetic technologies available in the nineteenth century. We found that the size of the skull, as described in historical records, could not have contained the electromagnet necessary to propel the skull’s jaw. This suggests that textual representations of the stick-pin were distorted by Victorian hyperbole. Elsewhere, our wire recorder prototype recreates a precursor to data storage: Valdemar Poulsen’s 1898 experiments with sound, whereby people yelled into a transmitter while running a trolley along a wire. Despite its claims for high fidelity, our findings suggest this process was highly contingent on the bodies and movements of users. Finally, the reading machine prototype explores the maintenance of Fournier d’Albe’s 1913 optophone, which translated images into sound, by one of its blind users, Mary Jameson. Early results of this research stress that Jameson contributed significantly to the optophone’s development, even if she is never mentioned in patents and rarely in other textual accounts. Together, these prototypes highlight why trial-and-error experimentation (as a method for humanities research) matters for historical inquiry into technology off the page.

Fostering students’ motivation and interest in higher education composition classes is vital to sustain student engagement. However, short-term motivation does not necessarily determine long-term success; recent research has found that an individual’s level of grit plays a significant role in his or her long-term success (Duckworth, Peterson, Matthews, & Kelly, 2007). To investigate the role of grit in writing classrooms, we are exploring how writing instructors can foster students’ grit for a greater chance of long-term success. Because Duckworth et al. (2007) consider interest and effort to be crucial factors in grit, we chose to examine whether or not service-learning (SL) can foster students’ grit, as SL been shown to increase student engagement in the past. Using Duckworth et al.’s eight-question grit scale, this project investigated the impact of SL on the grit of students in basic writing classes at Shippensburg University in Pennsylvania, comparing three SL classes (Group 1) and two non-SL classes (Group 2). Data was analyzed using t-tests to compare the average grit scores between the groups at mid- and post-semester. Although results indicated no significant correlation between grit and SL, this study suggests that grit stems more from internal rather than external motivators. Thus, this research can potentially open up a conversation about factors and influences that affect an individual’s grit.

As computational processing speeds and algorithmic models for machine learning continue to advance, depictions of Artificial Intelligence (A.I.) in science fiction (SF) have accordingly increased in production. Such fictional narratives typically anthropomorphize A.I., which brings into question whether intrinsically human concepts, such as gender, can legitimately be mapped onto A.I in the direct manner said fictions tend to portray. If “true” A.I., defined here as any technology that accurately replicates a human consciousness, is created, but gender is a social construction derived from strictly biological sex differences, then can the idea of “gender” be said to maintain any relevance to artificially intelligent beings? This paper intends to investigate that question by first cross-examining convolutional neural network theory with models of consciousness from M.I.T. theorists to establish a working model of how a true A.I. could learn, and then using said model, alongside feminist SF critical theory, as a lens through which to compare several SF works which depict gendered A.I.s. Understanding the relation—or lack thereof—between the human concept of gender and A.I.s is highly relevant to the ongoing discussion concerning the rights of A.I.s as intelligent non-humans.