The pedagogical social contract in the AI ERA: Explicit usage guidelines as instructional anchors for student course satisfaction - Kwon, Jungin (Edelweiss Applied Science and Technology, 2026)
The increasing use of generative AI in higher education has raised concerns about how instructional guidance shapes students’ learning experiences. While AI tools are becoming widely accessible, uncertainty regarding acceptable use may influence satisfaction, learning anxiety, and trust. This study examined the effects of explicit generative AI usage guidelines in undergraduate general education courses using a quasi-experimental design. Students were divided into an experimental group (n = 150) that received explicit AI usage guidelines and a comparison group (n = 147) that did not. Independent-samples t-tests revealed statistically significant differences across all measured variables. Students in the experimental group reported significantly higher overall course satisfaction (t = 5.34, p < .001), greater course understanding (t = 5.21, p < .001), and higher instructor trust (t = 5.67, p < .001). Furthermore, they experienced significantly lower perceived assignment burden (t = −4.32, p < .001) and learning anxiety (t = −4.11, p < .001). These findings indicate that the educational impact of generative AI depends less on technology availability than on pedagogical framing. Explicit guidelines serve as instructional anchors that support clarity, trust, and psychological stability in higher education.