<![CDATA[This study aims to test the effectiveness of Augmented Reality (AR) integration in vocabulary learning to improve students' motivation for English learning at UIN Prof. K.H. Saifuddin Zuhri Purwokerto. The study employed a quantitative approach with a nonequivalent control-group quasi-experimental design. Two classes were selected as the experimental group (AR-based learning) and the control group (conventional learning), each receiving pre- and posttests. The intervention consisted of six sessions of AR object- or scene-exploration activities, vocabulary labelling, pronunciation audio, meaning exercises, and contextual tasks in the form of sentence construction and mini-dialogues. Primary data were collected using the ARCS model motivation questionnaire, which covers attention, relevance, confidence, and satisfaction. Supporting data, including class engagement observations and short student reflections, were used to strengthen the interpretation. Data analysis was conducted using descriptive statistics, prerequisite and initial equivalence tests, and ANCOVA to assess the effect of treatment, with the pretest as a covariate, and included effect sizes. The results showed that the AR group had higher posttest motivation scores than the control group, particularly on the attention and relevance dimensions, and also reported greater confidence and satisfaction. These findings indicate that AR can make vocabulary learning more engaging and meaningful and facilitate successful learning experiences through multimodal scaffolding. The implications of this study emphasise the importance of purposeful task design, technical readiness, and classroom management to prevent AR from becoming a distraction. This study recommends the gradual implementation of AR for visually rich vocabulary themes and the use of formative evaluation to sustain learning motivation in higher education. Further research is recommended to examine medium-term vocabulary retention, to examine the roles of digital literacy and prior abilities as moderators, and to compare a more diverse set of blended and flipped models.]]>
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