<![CDATA[The rapid adoption of generative artificial intelligence (GenAI) in higher education has changed how students plan, draft, revise, and complete assessment tasks. However, there is still limited evidence on how instructional guidance shapes students’ experiences with these tools. This study examined whether guided use of generative AI, defined as the use of structured prompting protocols, required human-in-the-loop critical reflection, and verification workflows, produces different outcomes from unguided use. A quantitative, quasi-experimental, cross-sectional comparison was employed involving 50 undergraduate students from information technology-related programs, with 25 students in the guided AI use group and 25 in the unguided AI use group. Student engagement and learning satisfaction were measured using multi-item Likert-scale subscales administered through an online post-assessment survey, and group differences were analyzed using independent-samples t-tests after assumption checks for normality and homogeneity of variance. Results showed that the guided AI use group reported significantly higher student engagement than the unguided group. The guided group likewise obtained significantly higher learning satisfaction than the unguided group. These findings indicate that AI does not improve educational outcomes through access alone. Instead, its value depends on pedagogical structure, explicit expectations, and instructor support. The study provides quantitative evidence that guided AI use can promote more active engagement and more satisfying assessment experiences than unguided use. It also offers practical implications for designing AI-supported assessment in higher education.]]>
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