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TRUVACE RECORD VERSION
record: TRV-2026-1642
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-17T01:09:46.749258Z
status: published
lens: g_space
sector: education
headline: Transforming constructivist learning in engineering education through Comnet-Art: An AI-driven augmented reality learning environment
dek: The rapid integration of artificial intelligence (AI) and augmented reality (AR) in educational technology has opened new opportunities for enhancing constructivist learning, particularly in computer network education. This study aimed to design, develop, and evaluate Comnet-Art, an AI-based AR learning application, to improve students' understanding and constructivist skills in computer networks. The research involved 234 undergraduate students and employed the ADDIE model (Analysis, Design, Development, Implem…
gain_title: Students using the Comnet-Art AI and AR application improved their mean computer-networks test score from 68.21 to 86.47, a relative gain of 26.76%.
problem_title: (none)
trace_subject: (none)
gain_reading: Students using the Comnet-Art AI and AR application improved their mean computer-networks test score from 68.21 to 86.47, a relative gain of 26.76%.
gain_evidence: the mean score increasing from 68.21 to 86.47 and a relative improvement of 26.76%
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built Comnet-Art, a learning application combining artificial intelligence with augmented reality, to help undergraduates grasp computer networks. They developed it using the ADDIE model and tested it with 234 students after expert review of its content and media.

The reported results suggest AI-supported AR could aid active, constructivist learning in engineering courses. The abstract does not say how the gains compare with other teaching methods, or whether they persist beyond the post-test.
limitation: 
tag: Evidence-backed gain
key_points: The study involved 234 undergraduate students learning computer networks. | Media experts gave an average Aiken's V of 0.92 and material experts 0.89, rated very valid. | Educators rated practicality at 91.85% and students at 90.71%. | Mean test scores rose from 68.21 to 86.47 between pre-test and post-test.
rundown: The project followed the ADDIE model of analysis, design, development, implementation and evaluation. It also used expert validation, practicality testing and an effectiveness assessment to judge the application.

Validation scores indicated strong content and media quality. Practicality ratings from both educators and students were above 90%, which the authors interpret as high usability and engagement.

The authors say the pre-test and post-test comparison shows gains in engagement, collaboration, problem-solving and constructivist learning outcomes, and conclude that AI and AR can help transform engineering education.
sources:
- peer_reviewed | Social Sciences & Humanities Open | https://doi.org/10.1016/j.ssaho.2026.103831 | 2026-10-15
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