Lucas Guarenti Zangari

Kai Alexander Hackney, Lucas Guarenti Zangari, Jhonathan Sora-Cardenas et al.

To foster effective human-agent interactions, designers must understand how vocal cues influence the perception of agent personality and the role of user-agent alignment in shaping these perceptions. In this work, we examine whether users can perceive extroversion in voice-only artificial agents and how perceived personality relates to user-agent synchrony. We conducted a study with 388 participants, who evaluated four synthetic voices derived from human recordings, varying by gender (male, female) and personality expression (introverted, extroverted). Our results show that participants were able to differentiate perceived extroversion in female agent voices, but not consistently in male voices. We also observed evidence of perceived personality synchrony, particularly in participants' evaluations of the first agent encountered, with this effect more pronounced among male participants and toward male agents. We discuss these findings in light of limitations in stimulus diversity and voice representation, and outline implications for the design of voice-based agents, particularly regarding the interaction between gender, personality perception, and initial user impressions. This paper contributes findings and insights to consider the interplay of user-agent personality and gender synchrony in the design of human-agent interactions.

Chandler C. Payne, Kai A. Hackney, Lucas Guarenti Zangari et al.

Academic dishonesty has long been a concern in computing education, and the rapid growth of online learning and generative artificial intelligence (AI) has further complicated how cheating is perceived and addressed. We report on a study examining how different actors in the computer science (CS) classroom interpret potential cheating scenarios and the motivations behind academic dishonesty. Participants included instructors (n = 6), teaching assistants (TAs; n = 21), and undergraduate students (n = 538) enrolled in two CS courses at a large Southeastern institution in the United States. Respondents classified scenarios as serious cheating, trivial cheating, or not cheating and answered to an open-ended question about motivations for academic dishonesty. Our findings reveal notable discrepancies across groups: instructors most often attribute cheating to grade pressure and laziness, while students and TAs emphasize gaps in prerequisite knowledge and time management challenges. These results highlight misaligned perceptions of academic dishonesty and underscore the need for clearer communication and curricular strategies in computing education, particularly in post-COVID learning environments where hybrid instruction, increased reliance on digital resources, and AI-assisted tools have reshaped students' approaches to coursework and learning.