Tien, Lee Tien
Unknown Affiliation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Integrating AR–AI–STEAM for 6C Skill Development in Wetland Learning: Trends and Knowledge Mapping from 2019–2025 Rusmansyah, Rusmansyah; Syahmani, Syahmani; Erika, Farah; Kusuma, Arief Ertha; Tien, Lee Tien
Online Learning In Educational Research (OLER) Vol. 5 No. 2 (2025): Online Learning in Educational Research
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/oler.v5i2.945

Abstract

Augmented Reality (AR), Artificial Intelligence (AI), and STEAM-oriented learning have received increasing attention as educational technologies and pedagogical approaches that may relate to 21st-century competencies, including the 6C framework (critical thinking, creativity, collaboration, communication, citizenship, and character). However, the intersection of AR–AI–STEAM research with wetland and environmental education remains conceptually fragmented, and evidence is still limited regarding how this literature is structured and evolving. This study conducts a bibliometric mapping to profile publication trends, collaboration patterns, and conceptual structures of AR–AI–STEAM literature with 6C-related discourse in wetland/environmental learning. A total of 755 records were retrieved from Scopus on February 12, 2025 (covering 2019–2025), and an AI-assisted exploration using ResearchRabbit was employed as a complementary tool to expand citation trails and semantically proximate literature candidates. Performance analysis and science-mapping techniques were conducted using Bibliometrix (R) and VOSviewer. The results show a marked increase in publications after 2022, with China, India, and the United States among the most productive contributors. Keyword co-occurrence indicates “artificial intelligence” as a dominant conceptual hub, while wetland-related terms appear peripheral and weakly connected, suggesting a thematic gap between emerging AI-driven education discourse and ecologically grounded wetland learning contexts. This study contributes a structured overview of the research landscape and identifies underexplored linkages that can inform future empirical and design-based studies in wetland education. Because the 2025 records were retrieved early in the year (February 12, 2025), year-to-year comparisons involving 2025 should be interpreted as provisional