Hi! I’m Matthew, and I study how we communicate with and through machines.
Specific interests of mine pertain to how people manage their private information and make sense of personalized experiences in the context of human-machine communication and new media. AI is becoming increasingly embedded in everyday technologies without much thought as to how users’ perceptions of privacy management impact their use of new media and, in turn, its impact on them. My current work looks to unravel this.
PhD in Communication & Information, 2024
Kent State University
MA in Communication, 2020
Western Michigan University
BA in Organizational Communication & Gender and Women's Studies, 2017
Western Michigan University
With an experimental design, this study examined the effect of source cues (Human vs. AI) on hostile media bias through heuristic machine evaluation of machine and human social media profiles. This study also explored the effects of affective and cognitive involvement as moderators along with media source-self ideological incongruity (source incongruity). A 2 (human vs. AI) x 3 (CNN vs. USA Today vs. Fox News) experimental study was conducted (n = 434). Participants exhibited less hostile media bias when presented with a news story with AI source cues through heuristic machine evaluation. The mitigating effect was stronger for those viewing news from an incongruent news source. Such moderated mediated effect was further moderated by two types of involvement (i.e., affective and cognitive). Implications for future research surrounding the two types of involvement, source incongruity, machine heuristic evaluations, and hostile media bias are discussed in light of our findings.
Techonlogies in HMC can interrupt a lot of our pedagogical techniques and has the opportunity to change what education looks like for many. Using robots in the classroom is not a new endeavor. From social robots as stand-ins for math tutoring, working with students identified with Autism Spectrum Disorder (ASD), to telepresence robots there are various uses for robots and they don’t need to be overly sophisticated either. AI can be used in the classroom as tools to facilitate individualized learning, however, in its adoption we must understand who is involved in its development and adoption and how these systems can and do harm those most often marginalized. HMC scholars need to be interdisciplinary and holistic in their research about AI in educational contexts. VR and AR systems also have great use in the classroom, especially regarding public speaking. These technologies have a great opportunity for enhancing instructor content by providing immersive experiences for students (and instructors too). Regarding HMC and instructional communication research, variables such as immediacy, credibility, and teacher clarity are important for encouraging positive interactions with machine actors in the classroom settings. This chapter provides a bird’s eye view of the use of HMC technology in the classroom and important avenues of work regarding HMC in instructional communication research. (Abstract provided is not in final published chapter and is only to provide a synopsis here)
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Spring 2024, 2023, 2022
Fall 2023, 2022, 2021 Summer 2023, 2022
Spring 2024
Fall 2019 Spring 2020
Spring 2020
Fall 2019
Fall 2018 Spring 2019