### Methodological Issues in Measuring AR's Potential  
The evaluation of the impact of AR on learning primarily focused on the technology's potential to convey knowledge and/or skills. Previous research typically paired performance assessments with qualitative interviews on interest and attitudes toward the technology [4; 13; 54; 56; 57]. Some studies also evaluated motivational effects [31; 49; 53] and impact on cognitive load [31; 49; 57; 60]. While the impact on self-efficacy in technology-enhanced learning environments examined--both in terms of subject self-efficacy [54; 58; 61] and technology-specific self-efficacy--AR [45; 48], the body of evidence remains fragmented.  
Despite a substantial amount of research focused on knowledge acquisition, the findings were often inconclusive [62] and hard to compare [43; 63; 64]. Investigation of AR for science teaching combines a versatile technology with a diverse field, comprising several different subjects and topics. Each subject faces specific challenges and calls for its own specific didactical principles. The target group may differ, the duration and scope of the AR learning environment in question may vary, and the design may also have its impact. Additionally, as the definition of learning gain, performance, or academic achievement differs, so do the measuring instruments and methods.  
Moreover, for the most part, current research on game-based AR learning for science merely conducted preliminary testing with small sample sizes and no control group designs. In a systematic review of research on educational AR, [PERSON] [63] found that the majority of studies used individual measuring instruments and insufficient research designs (duration, sample size). This impeded comparability generalization of AR's learning effectiveness. He (eds.) stressed the need for more research using effective evaluation methods.  
In their recent extensive literature review, [PERSON] et al. [43] highlighted that the effects of AR and VR on academic achievement and cognitive load remain inconclusive. The authors suggested that inconsistencies in findings may arise from variations in use, design, participant characteristics, and the measuring instruments and constructs employed.  
Furthermore, game-based AR designs under investigation often need to differentiate control group design in the combined components, AR and game. [PERSON] et al. [37] emphasized the need for research on integrating game mechanisms into AR-aided science education. [PERSON] et al. [65] investigated the rise in technology-enhanced game-based learning for teaching sustainability. They (eds.) found a lack of empirical research and experimental study designs and pointed out the need for robust research designs in this domain.  
[PERSON] et al. [64] systematically reviewed AR research on performance and cognitive load, concluding that most findings reported AR to boost performance, yet some also contradicted this. Regarding reliable empirical findings, they highlighted the difficulty of media comparison studies, as providing precisely equivalent content and instructional methods in both intervention groups could be challenging to ensure. To investigate quality and tackle the quality of AR designs, value-added studies were proposed. Therefore, this approach compares AR conditions with different added features and provides deeper insight into the potential of different AR designs.  
In summary, the current literature revealed gaps in the understanding of AR's effectiveness in educational contexts, particularly regarding the integration of game-based elements, robust empirical designs, and measuring the instruments employed. This research aims to address these gaps by employing a robust design, differentiating the nuanced impacts of AR and game-based learning on educational variables (knowledge and computer self-efficacy), thereby contributing to a more comprehensive understanding of their potential in science education.