An official website of the United States government
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.
more »
« less
Designing and Learning With Pedagogical Agents: An Umbrella Review
Pedagogical agents are virtual characters that instructional designers include in learning environments to help students learn. Research in the area has flourished for thirty years, yet there are still critical questions about the efficacy of pedagogical agents for influencing learning and affect. As such, we conducted an umbrella review to synthesize the field. We located 17 systematic reviews or meta-analyses focused on the use of pedagogical agents in educational settings. We found that agents can have small positive effects on learning, motivation, and other affective variables. However, we still cannot say how one should design a pedagogical agent for any given educational context. We highlight the limitations of existing theory in the area, as well as existing reviews from a practical and methodological perspective, and highlight productive areas for future research.
more »
« less
- Award ID(s):
- 2229612
- PAR ID:
- 10548572
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Journal of Educational Computing Research
- Volume:
- 62
- Issue:
- 8
- ISSN:
- 0735-6331
- Format(s):
- Medium: X Size: p. 1907-1936
- Size(s):
- p. 1907-1936
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Field trips are widely recognized as an essential educational component to connect classrooms with the real world. When students don’t have access to real field trips, virtual ones have been developed by educators and researchers. Pedagogical agents have been applied to serve as a tour guide and educational tool that facilitate students learning in a virtual learning environment. Such agents are computer software generated and controlled entities that replicate or emulate humans. Previous studies have found that adding anthropomorphic traits to pedagogical agents in learning environments has significantly improved students’ learning experience; however, this area has yet been explored in the context of a virtual construction field trip. In this study, a virtual field trip to a complex mechanical room was developed using 360-degree panoramas and a pedagogical agent was employed to lead the tour. This study focuses on one single anthropomorphic trait - deictic gestures, which are pointing gestures used to refer to specific objects – and explores how such trait affects students’ quantitative learning outcomes and feedbacks on four aspects of the agent, including facilitating learning, credibility, human-like, and engaging. It was found that deictic gestures can improve students’ learning performance and attitudes on multiple aspects of the agent.more » « less
-
In recent years, large language models (LLMs) have seen rapid advancement and adoption, and are increasingly being used in educational contexts. In this perspective article, we explore the open challenge of leveraging LLMs to create personalized learning environments that support the “whole learner” by modeling and adapting to both cognitive and non-cognitive characteristics. We identify three key challenges toward this vision: (1) improving the interpretability of LLMs' representations of whole learners, (2) implementing adaptive technologies that can leverage such representations to provide tailored pedagogical support, and (3) authoring and evaluating LLM-based educational agents. For interpretability, we discuss approaches for explaining LLM behaviors in terms of their internal representations of learners; for adaptation, we examine how LLMs can be used to provide context-aware feedback and scaffold non-cognitive skills through natural language interactions; and for authoring, we highlight the opportunities and challenges involved in using natural language instructions to specify behaviors of educational agents. Addressing these challenges will enable personalized AI tutors that can enhance learning by accounting for each student's unique background, abilities, motivations, and socioemotional needs.more » « less
-
Do students learn from video lessons presented by pedagogical agents of different racial and gender types equivalently to those delivered by a real human instructor? How do the race and gender of these agents impact students’ learning experiences and outcomes? In this between-subject design study, college students were randomly assigned to view a six 9-minute video lesson on chemical bonds, presented by pedagogical agents varying in gender (male, female) and race (Asian, Black, White), or to view the original lesson with a real human instructor. In comparing learning with a human instructor versus with a pedagogical agent of various races and genres, ANOVAs revealed no significant differences in learning outcomes (retention and transfer scores) or learner emotions, but students reported a stronger social connection with the human instructor over pedagogical agents. Students reported stronger positive emotions and social connections with female agents over male agents. Additionally, there was limited evidence of a race-matching effect, with White students showing greater positive emotion while learning with pedagogical agents of the same race. These findings highlight the limitations of pedagogical agents compared to human instructors in video lessons, while partially reflecting gender stereotypes and intergroup bias in instructor evaluations.more » « less
-
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.more » « less
