Head-mounted AR (HMD-AR) is entering museums and exhibitions, yet creators such as AR developers and designers lack a reusable, parameterized, and verifiable interaction vocabulary for first-time visitors. We present a pattern language for cultural-heritage HMD-AR organized into three classes—PoI Guiding, Experience Indicating, and Experience Presenting—with nine sub-patterns that specify default safety bands and composition rules. Patterns map one-to-one to Unity prefabs on an instrumented runtime with unified logging. Using a Design Science Research (DSR) combined with Research-through-Design (RtD) approach, we instantiated two in-situ prototypes at European natural-history exhibits and evaluated them through creator panels (AR developers and cultural-heritage practitioners, N = 10) and visitor studies (N = 68) on HoloLens 2 and Quest 3. Experts judged the language clear and recommendable, and in-museum results met accepted thresholds for usability, workload, user experience, and presence, with stable behavioral logs under the studied operational conditions. Cross-site synthesis across two natural-history museum deployments showed directionally consistent outcomes and suggested that the interaction layer could be transferred with low effort within this scope: deployments required parameter localization, while core compositions and state machines remained unchanged. We contribute a parameterized, composable interaction language; aligned reference implementations and prototypes; creator- and user-side evidence; and a standardized mapping from constructs to metrics, measures, and decision rules for reproducible cross-study comparison. We release pattern cards, parameter documentation, and reusable Unity prefabs through a public GitHub repository to support transparent reuse, replication, and cross-study comparison.

Emerging advancements in augmented reality (AR) offer substantial potential to enrich museum exhibits with interactive and engaging content, yet head-mounted AR devices pose usability challenges for novice users.This study investigates how three distinct game mechanics-jigsaw puzzles, object catching, and spatial drawing-can address these challenges in a natural science museum setting.Implemented on Microsoft HoloLens 2, these mechanics were designed to enhance visitor engagement, learning, and immersion, while also contributing to the ongoing refinement of an interaction design pattern language tailored for AR applications.A structured evaluation framework combining usability, cognitive load, immersion, and engagement metrics suggests that each mechanic can improve user participation and comprehension of exhibit content, despite environmental distractions and gesture-recognition errors.Additionally, our findings illuminate how integrating game mechanics within a pattern language can guide developers in creating playful, instructive interactions for cultural heritage contexts.By situating the developed prototypes within a design pattern framework, this research demonstrates both theoretical and practical advances in gamified AR experiences, providing tangible guidelines for adapting and personalizing museum exhibits and informing similar applications in education and other cultural heritage contexts.