ABSTRACT This longitudinal study investigates students’ beliefs and behaviors regarding generative artificial intelligence (GenAI) utilization for college coursework and the role of instructors’ course policies and messaging in students’ decision-making. Guided by the theory of planned behavior (TPB), we note areas of change and stability in students’ GenAI perceptions and practices within and across semesters using short-term longitudinal data collection over the course of three semesters. Initial findings supported TPB as a viable framework for predicting students’ frequency of submitting AI-generated work as their own, yet more recent results only partially support the model. Results highlight shifts in course policy and instructor message framing and sidedness regarding GenAI and changes in how messaging predicts student perceptions. In particular, the prevalence and impact of two-sided messages changed significantly over time. Theoretical and practical implications for instructional communication are discussed.

ABSTRACT For college students with disabilities, obtaining classroom accommodations requires students to navigate revealing and concealing private disability information during interactions with faculty. Using communication privacy management theory (Petronio, 2002), this study focuses on how students with disabilities negotiate sources of turbulence and strategies they use to communicate the personal boundary for private disability information during interactions about classroom accommodations. We generated two themes related to sources of turbulence: faculty seeking information and reminding faculty. Additionally, we developed two broad categories of strategies for communicating the personal boundary: accommodation-focused strategies and disability information strategies. The boundary turbulence results have implications for perceptions of faculty communication competence.

ABSTRACT Within higher education, the popularity of generative artificial intelligence (AI) has led many administrators and faculty to stress the need for policies regarding its use. However, given its relative novelty, it is unclear how instructors create and communicate such policies to students, in terms of both content and style. We present a detailed content analysis of university instructors’ generative AI syllabi policies, examining substantive themes, linguistic style, and learner-centered style elements. Using a mixed-methods approach combining human coding and automated linguistic analysis, we analyzed 92 AI use policies from various disciplines and institutions. Results revealed that most instructors crafted policies that defined AI through example and allowed its use with restrictions. Thematic warnings about AI’s limitations were positively associated with perceptions of a warmer, student-centered tone. Linguistically, while the use of second-person pronouns was most common, only first-person and collective pronouns correlated with a warmer tone. Most policies analyzed also incorporated learner-centered elements like rationales and student responsibilities, though fewer invited students to request feedback on the topic of AI. Overall, policies tended to place the primary responsibility on students for ethical AI use. Results are interpreted with a functional approach toward syllabi construction, before offering practical applications for instructors.

ABSTRACT Three within-subject experiments were conducted by providing students with answers to content questions across different subject matters (a definition, explanation, and example) offered by a human professor (subject-matter expert) versus generative artificial intelligence (ChatGPT). In a randomized order, students read both the expert and ChatGPT’s responses (both were de-identified and declared to be “professors,” so students were not aware one was artificial intelligence), rated both explanations on teaching clarity and competence, and then reported on their affect toward the content and situational interest. Study 1 (interpersonal communication content) revealed no significant differences in repeated measure ratings comparing the expert versus ChatGPT. However, in Study 2 (business communication content) and Study 3 (instructional communication content), compared with the expert, ChatGPT (impersonating a professor) was rated by the same students as higher in teaching clarity and competence, and it generated more student affect and situational interest. In Study 2 and Study 3, a within-subjects mediation analysis revealed that ChatGPT generated more student affect toward the content through the clarity in responses it provided to students.

ABSTRACT Although artificial intelligence (AI) tools (e.g., ChatGPT) have potential to act as a transformative educational tool, the widespread adoption of such technology also brings forth concerns related to academic integrity and learning gaps. This study examines whether class discussions on the negative aspects of AI-driven writing assistance have any impact on mitigating reliance on such technology. To address the inquiry, data were collected from undergraduate students in the U.S. Findings indicate that these discussions lead to a more critical evaluation of ChatGPT’s usefulness and favorability toward it, resulting in weaker intentions to use it. The study underscores the importance of class discussions and offers insights into how instructors and educators can navigate concerns about negative effects of AI-powered technology in education while leveraging the benefits of it.

"Forum epilogue." Communication Education, 72(4), p. 441 Disclosure statementNo potential conflict of interest was reported by the author(s).