3  Problem Statement

This chapter identifies the research gap and frames our study’s objectives. It begins by summarizing the problem context and need detailed in the preceding chapters. It then briefly outlines the problem, bounded by gaps and limitations of the current literature. The primary research question and supporting questions are identified. Finally, the work’s primary contributions are identified.

3.1 Problem Identification

Despite a demonstrated need for operator assistance, the emergence of promising technology, a theoretical basis for performance enhancement, a “solution” that connects it all, and frameworks to support its development and assessment, XR adoption remains slow in manufacturing. Until a compelling business case can be made for the substantial investment required, it is unlikely that industry will move beyond isolated proof of concept studies. With continued delays to adoption comes the risk that hardware providers lose their appetite for the massive ongoing R&D budgets these systems demand and reduce or end their commitment in the sector. Therefore, there is a pressing need to assess the business benefits of augmented instruction, which may come in the form of improved operator performance, learning outcomes, long-term retention, user comfort, quality, or other measurable returns on the investment.

As enumerated in Section 2.14.3, our review of the literature has highlighted a number of gaps. This list includes few direct comparisons of AR/MR technologies, a focus on immediate performance measures, a lack of ecological validity, and heterogeneous designs. More specifically, too many studies focus on comparing a single AR/MR intervention to traditional training methods, leaving unanswered questions about how various AR/MR technologies stack up against each other, and which are most suitable for specific training contexts and desired learning outcomes. The prevailing emphasis on immediate performance measures, such as task completion time and error rates, provides a limited view of training success and knowledge transfer. Furthermore, a significant portion of existing research on AR/MR in manufacturing training relies on simplistic tasks, irrelevant environments, or novice participants. While these controlled studies provide valuable insights into the potential of AR/MR technologies, they may not accurately reflect the complexity and challenges of real-world industrial settings. Finally, the literature includes a wide variation in research designs, technologies, training content, and outcome measures employed across studies. While this diversity reflects the breadth and complexity of the field, it also poses significant challenges for comparing results, synthesizing findings, and drawing conclusive insights about the effectiveness of AR/MR interventions.

Collectively, these limitations hinder our ability to draw definitive conclusions about the effectiveness of AR/MR technologies in manufacturing training, leaving organizations without clear guidance on how to leverage these tools for optimal learning outcomes and return on investment. In short, additional data and more comprehensive results are needed to provide an accurate assessment of AR/MR efficacy across the diverse problem and solution space.

To address these shortcomings, this research proposes an affordance-based framework that conceptualizes AR/MR not as transient technologies, but as bundles of theoretically-grounded affordances designed to optimize learning processes. By systematically operationalizing these affordances through carefully designed instructional treatments, we aim to provide empirical insights into the factors that most influence the efficacy of AR/MR-augmented training. Grounded in the literature and tailored to real-world manufacturing contexts, the proposed study will yield actionable recommendations to accelerate the adoption of these innovative technologies where their benefits can be maximized.

3.2 Research Questions

This gives rise to the central research question of this work:

How do different AR/MR instructional methods, designed to leverage specific affordances, impact operator learning, recall, and retention in a real-world manufacturing assembly training context?

Several supporting questions are also identified:

A. What are the relative effects of various AR/MR technologies on immediate learning outcomes, such as task completion time and error rates, compared to traditional paper-based instructions?

B. How do these AR/MR technologies influence long-term recall and retention of assembly skills, as measured by performance on the same task after a designated period without further training?

C. To what extent do the specific affordances of each AR/MR technology, such as hands-free interaction, spatial registration, and user-centric displays, contribute to the observed learning, recall, and retention outcomes?

D. How do operator characteristics, such as related experience or demographics, influence the effectiveness of each instructional method?

E. What are the perceived workload, usability, and user satisfaction associated with each AR/MR technology, and how do these factors relate to learning, recall, and retention outcomes?

3.3 Key Contributions

By addressing key gaps in the existing literature, this research will contribute to the development of evidence-based guidelines and best practices, ultimately advancing the field and informing future efforts to optimize AR/MR-assisted training programs for improved learning outcomes and return on investment. Three key contributions of this work are:

  1. An innovative, affordance-based study design for comparing the impact of multiple AR/MR technologies on learning, recall, and retention in a real-world manufacturing training context. A pilot study is incorporated to assess the ability of this design to address limitations identified in the literature while also incorporating its best practices.

  2. A comprehensive analysis of the immediate learning effects of different AR/MR technologies, considering multiple performance metrics, cognitive load, usability, qualitative feedback, and the role of specific affordances.

  3. An examination of the factors influencing the long-term effectiveness of AR/MR training, including the relationships between initial learning outcomes, recall and retention, user experience, and individual differences.

To address these research questions and deliver the outlined contributions, a comprehensive methodology is outlined in the following chapter. It details the the selection of AR/MR technologies, development of instructional treatments, and the methods for data collection and analysis.