Algorithmically-driven writing and academic integrity: exploring educators' practices, perceptions, and policies in AI era

Abstract

Despite global interest in the interface of Algorithmically-driven writing tools (ADWTs) and academic integrity, empirical data considering educators' perspectives on the challenges, benefits, and policies of ADWTs use remain scarce. This study responds to calls for empirical investigation concerning the affordances and encumbrances of ADWTs, and their implications for academic integrity. Using a cross-sectional survey research design, we recruited through snowball sampling 100 graduate students and faculty members representing ten disciplines. Participants completed an online survey on perceptions, practices, and policies in the utilization of ADWTs in education. The Technology Acceptance Model (TAM) helped us understand the factors influencing the acceptance and use of ADWTs. The study found that teacher respondents highly value the diverse ways ADWTs can support their educational goals (perceived usefulness). However, they must overcome their barrier threshold such as limited access to these tools (perception of external control), a perceived lack of knowledge on their use (computer self-efficacy), and concerns about ADWTs' impact on academic integrity, creativity, and more (output quality). AI technologies are making headway in more educational institutions because of their proven and potential benefits for teaching, learning, assessment, and research. However, AI in education, particularly ADWTs, demands critical awareness of ethical protocols and entails collaboration and empowerment of all stakeholders by introducing innovations that showcase human intelligence over AI or partnership with AI.