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The purpose of this research study was to examine the overall effect of adaptive learning systems deployed using artificial intelligence technology across a range of relevant variables (e.g., duration, student level, etc.). Following a systematic procedure, this meta-analysis examined literature from 18 academic databases and identified N = 45 independent studies utilizing AI-enabled adaptive learning. This meta-analysis examined the overall effect of AI-enabled adaptive learning systems on students' cognitive learning outcomes when compared with non-adaptive learning interventions and found that they have a medium to large positive effect size (g = 0.70). The effect was significantly moderated by publication type, origin of study, student classification level, student discipline, duration, and research design. In addition, all three adaptive sources (cognitive, affective, and behavioral) and adaptive targets (navigation and assessment) were significant moderators. The type of AI used in the adaptive engine did not moderate the effects. Implications for both practice and research are provided.

The purpose of this study was to explore the competencies of those professionals working in the nascent area of Learning Experience Design (LXD). Following systematic procedures and inspiration from our conceptual framework of LXD that emphasizes knowledge, skill, and ability statements (KSA), we collected and coded N = 388 unique LXD job announcements over a four-month period. Using LXD job announcements as the unit of analysis, this research used a concurrent mixed-methods approach first to qualitatively code LXD job announcements on the presence or absence of a range KSA statements and then used Exploratory Factor Analysis (EFA) for dichotomously scored data to discern the core competencies required among LXD professionals. Our processes resulted in a total of 69 knowledge statements, 38 skills statements, and 72 ability statements. Our findings unveil core competencies across the KSA domains such as knowledge of multimedia production technologies and software, emerging technology design and development skills, and the ability to apply human-centered design to create learning products and experiences. We discuss our findings in relation to the more established domain of instructional design (ID) and present potentially unique facets of LXD in this regard. Limitations and delimitations are provided along with closing remarks.

Extended reality (XR) such as Virtual Reality (VR) and Augmented Reality (AR) has been heralded as a particularly promising technology for autistic people. However, prior studies have centered around curing or ameliorating deficits and impairments and are typically conducted by non-disabled and non-autistic researchers. Using co-design and a variety of learning experience design methods and processes, Project PHoENIX (Participatory, Human-centered, Equitable, Neurodiverse, and Inclusive XR) speaks to the need for research that applies a social-ecological perspective to the design and evaluation of VR experiences for autistic users, with the goal of decreasing environmental barriers and promoting a more inclusive society. In this study, we describe a multi-cycle process of educational design research (EDR), consisting of iterative human-centered formative design, development, implementation, and evaluation of Project PHoENIX from Spring 2021 to Spring 2022. A framework for conducting co-design and collaborative educational design research with autistic individuals in a VR environment is provided along with design principles that support this framework. Findings from 3 meso-cycles illustrate the dual outcomes of educational design research, namely, (1) a consistently maturing intervention and (2) improving theoretical understanding. Findings underscore the feasibility of our approach, and demonstrate potential to scale.