Abstract
The plight of PhD students in the US has been a source of comic intrigue as well as critical concern. The vulnerability of PhD students makes them targets for dehumanization, i.e., the stripping of the dignity and humanity that differentiate humans from objects. Freire (2005) identifies dehumanization as “humankind’s central problem” (p. 43). Emancipatory theories explain the human tendency to oppress the vulnerable and how humans can rise above hegemonic dynamics to achieve societal emancipation. While IS scholars have examined how technology can be used for emancipation (Miranda et al. 2016; Young 2018), there is another way in which emancipatory theories can be interpreted to challenge assumptions in the field of information systems education. That is, dehumanization occurs when we treat humans like machines. As machines become anthropomorphized and it becomes difficult to distinguish when we are interacting with humans versus machines online, it is plausible that cognitive distinctions we make between humans-particularly vulnerable humans-and machines will blur. Fitts (1951) theorizes distinct roles and capabilities for humans and machines. Machines have greater speed, power, consistency, information capacity, and memory than humans. Humans can reason, sense and perceive. Humans are better at mindful and epistemic tasks while machines are better at mindless and pragmatic tasks (Salovaara et al. 2019). How can faculty tell if we are treating our PhD students like machines? One way is to practice the method of reconstruction by replacing the term “human” with “professor” and “machine” with “PhD student”. If faculty assign PhD students only mindless and pragmatic tasks while completing all the mindful and epistemic tasks, we create a hegemonic dynamic in which all parties are dehumanized. Freire warns that dehumanizing education led by well-intentioned professors will not result in true learning, but in domesticated, passive, unquestioning students who lack the capacity to learn. Thus, we challenge faculty to consider how we might humanize PhD students-and ourselves-through partnering in emancipatory pedagogy. This research has implications beyond academe to managers training employees, researchers using Mechanical Turk, developers designing systems, and any other context in which power dynamics may afford dehumanization and oppression.
Recommended Citation
Young, Amber; Young, Eugene; and Farley, Rebecca M., "The Hegemony of Treating Students Like Machines: Insights from Emancipatory Theories and Human-Machine Functions" (2019). AMCIS 2019 Proceedings. 22.
https://aisel.aisnet.org/amcis2019/treo/treos/22
The Hegemony of Treating Students Like Machines: Insights from Emancipatory Theories and Human-Machine Functions
The plight of PhD students in the US has been a source of comic intrigue as well as critical concern. The vulnerability of PhD students makes them targets for dehumanization, i.e., the stripping of the dignity and humanity that differentiate humans from objects. Freire (2005) identifies dehumanization as “humankind’s central problem” (p. 43). Emancipatory theories explain the human tendency to oppress the vulnerable and how humans can rise above hegemonic dynamics to achieve societal emancipation. While IS scholars have examined how technology can be used for emancipation (Miranda et al. 2016; Young 2018), there is another way in which emancipatory theories can be interpreted to challenge assumptions in the field of information systems education. That is, dehumanization occurs when we treat humans like machines. As machines become anthropomorphized and it becomes difficult to distinguish when we are interacting with humans versus machines online, it is plausible that cognitive distinctions we make between humans-particularly vulnerable humans-and machines will blur. Fitts (1951) theorizes distinct roles and capabilities for humans and machines. Machines have greater speed, power, consistency, information capacity, and memory than humans. Humans can reason, sense and perceive. Humans are better at mindful and epistemic tasks while machines are better at mindless and pragmatic tasks (Salovaara et al. 2019). How can faculty tell if we are treating our PhD students like machines? One way is to practice the method of reconstruction by replacing the term “human” with “professor” and “machine” with “PhD student”. If faculty assign PhD students only mindless and pragmatic tasks while completing all the mindful and epistemic tasks, we create a hegemonic dynamic in which all parties are dehumanized. Freire warns that dehumanizing education led by well-intentioned professors will not result in true learning, but in domesticated, passive, unquestioning students who lack the capacity to learn. Thus, we challenge faculty to consider how we might humanize PhD students-and ourselves-through partnering in emancipatory pedagogy. This research has implications beyond academe to managers training employees, researchers using Mechanical Turk, developers designing systems, and any other context in which power dynamics may afford dehumanization and oppression.