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Face-to-Face Interaction with Pedagogical Agents, Twenty Years Later
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Johnson et al. ( International Journal of Artificial Intelligence in Education , 11 , 47–78, 2000 ) introduced and surveyed a new paradigm for interactive learning environments: animated pedagogical agents. The article argued for combining animated interface agent technologies with intelligent learning environments, yielding intelligent systems that can interact with learners in natural, human-like ways to achieve better learning outcomes. We outlined a variety of possible uses for pedagogical agents. But we offered only preliminary evidence that they improve learning, leaving that to future research and development. Twenty years have elapsed since work began on animated pedagogical agents. This article re-examines the concepts and predictions in the 2000 article in the context of the current state of the field. Some of the ideas in the paper have become well established and widely adopted, especially in game-based learning environments. Others are only now being realized, thanks to advances in immersive interfaces and robotics that enable rich face-to-face interaction between learners and agents. Research has confirmed that pedagogical agents can be beneficial, but not equally for all learning problems, applications, and learner populations. Although there is a growing body of research findings about pedagogical agents, many questions remain and much work remains to be done.
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“Further, computer-generated animated agents can use non-verbal facial cues, paralinguistic cues (e.g., intonation in speech), and gestures as an additional form of feedback (Johnson & Lester, 2016).”
From Looking Beyond the Hype: Understanding the Effects of AI on Learning (2025) · cites this paper as Johnson & Lester, 2016
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محمدي, إ. س., & الحميد, ه. ح. ع. (2022) · المجلة العلمية للتربية النوعية والعلوم التطبيقية · doi:10.21608/sjseas.2022.231346
Proposed Learning Agent for Reducing Different Mathematics Anxiety Levels
(2018) · AUSTRALIAN JOURNAL OF BASIC AND APPLIED SCIENCES · doi:10.22587/ajbas.2018.12.8.18
Effects of Gesture by Pedagogical Agents in Multimedia Learning
Wang, F. (2019) · American Educational Research Association Annual Meeting (AERA) · doi:10.3102/1436279
Imersivní virtuální realita ve vzdělávání: SWOT analýza
Černý, M. (2022) · Pedagogická orientace · doi:10.5817/pedor2022-1-33
Metadata
- Authors
- Johnson, W. L., & Lester, J. C.
- Affiliations
- Alelo (United States) (US)
- North Carolina State University (US)
- Corresponding author
- Johnson, W. Lewis
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 26
- Issue
- 1
- Pages
- 25-36
- ISSN
- 1560-4292 (ISSN-L)
- OpenAlex
- W2171876690
- Cited by
- 255
- References
- 37
- Citations by year
- 2026: 49 · 2025: 24 · 2024: 20 · 2023: 24 · 2022: 24 · 2021: 36 · 2020: 24 · 2019: 20 · 2018: 14 · 2017: 15 · 2016: 5
- FWCI
- 11.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Artificial Intelligence in Education (publishedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation (1321056, 1409639, 1138497, 1344803, 0822200, 1020229)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- educational agents, animated agents, face-to-face interaction, smart learning environments, game-based learning, immersive interfaces, educational robotics, human-agent interaction, learning outcomes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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