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Education is poised for a transformative shift with the advent of neurosymbolic artificial intelligence (NAI), which will redefine how we support deeply adaptive and personalized learning experiences. The integration of Knowledge Graphs (KGs) with Large Language Models (LLMs), a significant and popular form of NAI, presents a promising avenue for advancing personalized instruction via neurosymbolic educational agents. By leveraging structured knowledge, these agents can provide individualized learning experiences that align with specific learner preferences and desired learning paths, while also mitigating biases inherent in traditional AI systems. NAI-powered education systems will be capable of interpreting complex human concepts and contexts while employing advanced problem-solving strategies, all grounded in established pedagogical frameworks. In this paper, we propose a system that leverages the unique affordances of KGs, LLMs, and pedagogical agents – embodied characters designed to enhance learning – as critical components of a hybrid NAI architecture. We discuss the rationale for our system design and the preliminary findings of our work. We conclude that education in the era of NAI will make learning more accessible, equitable, and aligned with real-world skills. This is an era that will explore a new depth of understanding in educational tools. 

Pedagogical agents (PAs) are increasingly being integrated into educational technologies. Although previous reviews have examined the impact of PAs on learning and learning-related outcomes, it still remains unclear what specific design features, social cues, and other contextual elements of PA implementation can optimize the learning process. These questions are even more prevalent with regards to the K-12 population, as most reviews to date have largely focused on post-secondary learners. To address this gap in the literature, we systematically review empirical studies around the design of PAs for K-12 learners. After reviewing 1374 studies for potential inclusion, we analyzed 44 studies that met our inclusion criteria using Heidig and Clarebout’s (2011) frameworks. Our findings showed that learners had preferences for specific types of PAs. While these preferences were not always associated with increased learning outcomes, there is a lack of research specifically investigating the intersection of perceptions and learning. Our results also showed that pedagogical strategies that are effective for human teachers were effective when used by PAs. We highlight what specific design features instructional designers can use to design PAs for K-12 learners and discuss promising research directions based on the extant work in the field. 

Systematic reviews are a time-consuming yet effective approach to understanding research trends. While researchers have investigated how to speed up the process of screening studies for potential inclusion, few have focused on to what extent we can use algorithms to extract data instead of human coders. In this study, we explore to what extent analyses and algorithms can produce results similar to human data extraction during a scoping review—a type of systematic review aimed at understanding the nature of the field rather than the efficacy of an intervention—in the context of a never before analyzed sample of studies that were intended for a scoping review. Specifically, we tested five approaches: bibliometric analysis with VOSviewer, latent Dirichlet allocation (LDA) with bag of words, k-means clustering with TF-IDF, Sentence-BERT, or SPECTER, hierarchical clustering with Sentence-BERT, and BERTopic. Our results showed that topic modeling approaches (LDA/BERTopic) and k-means clustering identified specific, but often narrow research areas, leaving a substantial portion of the sample unclassified or in unclear topics. Meanwhile, bibliometric analysis and hierarchical clustering with SBERT were more informative for our purposes, identifying key author networks and categorizing studies into distinct themes as well as reflecting the relationships between themes, respectively. Overall, we highlight the capabilities and limitations of each method and discuss how these techniques can complement traditional human data extraction methods. We conclude that the analyses tested here likely cannot fully replace human data extraction in scoping reviews but serve as valuable supplements. 

Over the past three decades the field of pedagogical agents (PAs) has seen significant growth, but no review has specifically focused on the design and use of PAs for K-12 students, despite the fact that an early meta-analysis showed that they receive the most benefits from learning from or with PAs. Our systematic search revealed 112 studies that met the inclusion criteria and were analyzed. Our findings revealed a plethora of studies investigating the use of PAs with K-12 populations and a considerable number of longitudinal studies, both of which the field has long stated did not exist in significant numbers. Our findings contrast long-held findings in the field, further support others, and highlight areas where further experimentation and research synthesis are needed.