Conference paper 2026

Ambient Intelligence Integration in Vocational Education: Evaluating Smart Learning Environments for Digital Skills Development

Lecture Notes in Computer Science
Conference · Vol. 16202 LNCS · pp. 52-63
Abstract

The rapid advancement of Artificial Intelligence (AI) and Internet of Things (IoT) technologies demands a fundamental transformation in vocational education curricula to prepare students for the evolving digital economy. This paper presents the evaluation of the AIM4VET project, an Erasmus+ funded initiative that integrates ambient intelligence and IoT technologies into Vocational Education and Training (VET) programs across Portugal, Spain, and Slovenia. The study focuses on the implementation and assessment of twelve modular teaching units centered around smart home energy management systems, utilizing Arduino-Raspberry Pi architectures and KNIME analytics platform. Employing project-based learning methodologies, the curriculum addresses critical competencies in sensor integration, data processing, machine learning, and ambient intelligence applications. Results from 14 VET students demonstrate significant improvements in technical understanding, with post-assessment scores showing mastery across all evaluation criteria. Student feedback revealed high satisfaction levels with instructional clarity, while teacher assessments confirmed strong performance in autonomy, creativity, and collaborative problem-solving. The majority of students rated module difficulty as appropriate, indicating well-calibrated instructional design. The findings validate the effectiveness of integrating ambient intelligence environments in VET curricula, demonstrating enhanced student engagement, practical skill development, and preparation for Industry 5.0 contexts. This research contributes to the growing body of knowledge on technology-enhanced vocational education and provides a scalable framework for implementing AI and IoT literacy across European VET systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Author Keywords

Project-Based Learning Vocational education Ambient Intelligent Digital Skills Internet of Things Smart Learning Environments

Index Keywords

E-learning Employment Learning systems Teaching Curricula Engineering education Personnel training Apprentices Education computing Students Project based learning Vocational education Problem solving Machine learning Vocational education and training Computer aided instruction Internet of things Skills development Ambient intelligence Automation Energy management Ambient intelligent Ambients Digital skills Internet of things technologies Learning environments Smart learning environment
Author Affiliations
ALGORITMI Centre/LASI, Universidade do Minho, Braga, Braga, Portugal
Funding & Acknowledgements
Grant: AI4VET, AIM@VET-2022-1ES01-KA220-VET-000089813
This work has been supported by Erasmus+ Programme of the European Union-Artificial Intelligence learning modules to adapt VET to labour market needs (AI4VET), AIM@VET-2022-1ES01-KA220-VET-000089813.
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