Generative AI-Powered Pedagogical Agents in Immersive Environments for Social Sciences and Humanities Education: A Scoping Review

Abstract

Title: Generative AI-Powered Pedagogical Agents in Immersive Environments for Social Sciences and Humanities Education: A Scoping Review Purpose This research project investigates how generative artificial intelligence (GenAI)-powered pedagogical agents and virtual instructors are being used within immersive virtual reality (VR) and augmented reality (AR) environments, specifically in the context of social sciences and humanities education. While research on generative-AI agents in immersive learning environments has grown rapidly since 2023, no existing systematic or umbrella review has yet mapped this specific intersection — most prior reviews either predate the generative-AI/large-language-model (LLM) era or focus predominantly on STEM, medical, and engineering education. Given that social sciences and humanities education (e.g., history, geography, civics) involves distinctive pedagogical and ethical demands — such as historical empathy, multi-perspective reasoning, and open-ended interpretive dialogue — that differ meaningfully from technical or procedural training domains, the project aims to determine what is currently known about this intersection, how mature the field is methodologically, and where meaningful gaps remain. To address this aim, the study was designed as a scoping review, following Arksey and O'Malley's (2005) five-stage methodological framework and reported according to the PRISMA-ScR (PRISMA Extension for Scoping Reviews) guideline. A scoping-review design was chosen deliberately over a systematic review or meta-analysis because the objective is to map the breadth, characteristics, and trends of an emerging body of literature — rather than to statistically synthesize effect sizes or assess a narrow effectiveness question — which is appropriate given how new and heterogeneous this specific research area still is. Research Questions The project is guided by four research questions: RQ1: What are the design features (embodiment, mode of interaction, role assumed) of GenAI-based pedagogical agents/virtual instructors used in immersive VR/AR environments in social and humanities education? RQ2: What learning outcomes have been reported in studies on these agents, and in which direction do the findings trend? RQ3: At which educational levels and in which social sciences/humanities subfields have these studies been conducted? RQ4: What are the methodological trends and limitations in the field, and what directions are recommended for future research? Methodology A systematic search was conducted across Scopus, Web of Science, and ERIC (August 2026), combining terms related to pedagogical agents/virtual instructors, generative AI/LLMs, immersive VR/AR/XR technologies, and education. The search was restricted to English-language, peer-reviewed journal articles published between 2023 and 2026 — a window chosen to capture the generative-AI/LLM era specifically. Of 105 records initially identified, a multi-stage screening and eligibility process (title/abstract screening, full-text assessment, and data-charting verification) resulted in 9 studies meeting all inclusion criteria. Data extracted from each study included agent design characteristics, technology used, research design, educational level and subject area, reported learning outcomes, and author-stated limitations and future-research recommendations. Findings were synthesized narratively (rather than statistically) around the four research questions and subsequently interpreted through the theoretical lenses of Presence Theory and Embodied Cognition. Expected/Actual Outcomes The review's findings indicate that the included agents are predominantly designed as embodied 3D characters built on GPT-family models, most often assuming peer or mentor roles within VR environments. Reported effects on learning outcomes (motivation, engagement, partner perception, and, in some cases, academic performance) trend positive overall, though effect sizes vary considerably across studies and are notably smaller in the few studies employing control-group comparisons than in single-group, pre-/post-test designs. A key substantive finding is that the existing literature is concentrated almost entirely at the higher-education level and clusters around language education and AI ethics/literacy — it has not yet reached classic social-studies subfields such as history, geography, or civics education, despite the conceptual gap the study set out to address. Interpreted through Presence Theory and Embodied Cognition, the findings further suggest that an agent's educational impact depends less on its technical sophistication (e.g., visual realism) than on whether an appropriate balance between presence and embodiment has been achieved relative to the nature of the learning task — an "embodiment paradox" identified across several included studies. The project's broader contribution is threefold: (1) it provides the field's first dedicated mapping of the generative-AI/LLM generation of pedagogical agents within the social sciences/humanities education context, filling a gap left by earlier, pre-generative-AI-era reviews; (2) it offers a theoretically grounded interpretive lens (Presence Theory/Embodied Cognition) for understanding why and how these agents affect learning, rather than only cataloguing whether they do; and (3) it identifies concrete directions for future research — including extending investigation to K-12 contexts, directly targeting classic social-studies content, adopting more rigorous control-group designs, and incorporating physiological/multimodal measures alongside self-report data. The review also transparently documents its own methodological limitations (a single-researcher screening stage, no prior protocol registration, no formal quality/risk-of-bias appraisal, and a modest final sample of nine studies), consistent with the exploratory nature of scoping reviews and intended to guide readers in appropriately weighing the strength of the evidence presented.