Agent-based social skills training systems
The Artes architecture, interaction characteristics, learning theories and future outlooks
Dados Bibliográficos
| ID | 21452071 |
|---|---|
| Autores | Mohammed Al Owayyed (0000-0002-9680-9204, Delft University of Technology, autor correspondente), Myrthe L Tielman (0000-0002-7826-5821, Delft University of Technology), Arno Hartholt (0000-0001-7950-0048, USC Institute for Creative Technologies, Los Angeles, CA, USA), Marcus Specht (0000-0002-6086-8480, Delft University of Technology), Willem‐Paul Brinkman (0000-0001-8485-7092, Delft University of Technology) |
| Ano | 2025 |
| Volume | 44 |
| Fascículo | 9 |
| Páginas | 1787-1814 |
| Data de publicação | 2025-05-28 |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Behaviour and Information Technology (JOURNAL) |
| Identificadores do periódico | ISSN: 0144-929X • E-ISSN: 1362-3001 |
| Editora | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/0144929x.2024.2374891 |
| OpenAlex | W4400852591 |
| Idioma | EN |
| Citações recebidas | 1 |
| Referências citadas | 121 |
Agent-based training systems can enhance people's social skills. The effective development of these systems needs a comprehensive architecture that outlines their components and relationships. Such an architecture can pinpoint improvement areas and future outlooks. This paper presents ARTES: a general architecture illustrating how components of agent-based social training systems work together. We studied existing systems and architectures for training and tutoring to design ARTES and identify its essential components and interaction characteristics. ARTES comprises two core components: the agent simulation of social situations, and educational elements to provide guided learning. We link ARTES's crucial components to four primary learning theories (behaviourism, cognitivism, social cognitive theory, and constructivism) to illustrate the role of agent simulation and tutoring elements in establishing desired learning outcomes. Furthermore, we map ARTES's components against eight architectures, 43 systems and three tools to indicate the components' relevance, completeness, generalisation, and deployment potential across contexts. In addition to ARTES, the paper also contributes by identifying future improvements and research directions, such as the agent's thinking, tutoring methods, knowledge transfer, and ethical implications. We believe ARTES can help bridge the gap between virtual human simulations and impactful educational learning, offering training system developers desirable features like understandability and adaptability
Human–computer interaction · Knowledge management · Social learning · Visual arts · AI in Service Interactions · Architecture · Computer Science · Intelligent Tutoring Systems and Adaptive Learning · Multi-Agent Systems and Negotiation · Psychology
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| Citações por ano | 1 |
|---|---|
| Intervalo de citações | 2026 - 2026 (1) |
| Altamente citado | Não |