Aug 2026· Rapid Integration of Software Engineering Techniques· Vol 3, pp. 185-208· 0 citations· 27 references
TL;DR
This study proposes a conceptual framework for reframing engineering education through AI-integrated DTs as engineering learning platforms rather than instructional technologies alone, and provides practical implementation pathways for integrating AI-enabled DTs into future engineering education.
Abstract
The growing intersection of artificial intelligence (AI) and digital twins (DTs) is transforming the design, operation, and management of engineering infrastructure by enabling data-driven modelling, monitoring, prediction, and decision-making, while integrating engineering processes that have traditionally operated in isolation. However, engineering education has not evolved in parallel with these rapid technological advances, often treating AI and DTs as individual tools rather than inputting them within pedagogical models that promote systems thinking and real-world engineering practice. This gap has created a significant disconnect between conventional engineering education and the evolving competencies required by modern industry. This study proposes a conceptual framework for reframing engineering education through AI-integrated DTs as engineering learning platforms rather than instructional technologies alone. The framework was developed through a structured conceptual synthesis of recent literature, examining the limitations of existing educational approaches and identifying opportunities to strengthen systems thinking, uncertainty management, decision-making, and ethical reasoning within engineering curricula. Based on this synthesis, the proposed framework positions DTs as educational infrastructure that supports continuous student engagement with realistic engineering scenarios. Also, it facilitates practice-oriented learning and strengthens the combination of academic learning with engineering practice and industrial applications. The study contributes a structured theoretical framework to guide curriculum transformation and provides practical implementation pathways for integrating AI-enabled DTs into future engineering education.
A systematic review of peer-reviewed studies published between 2015 and 2024 concludes that while tools such as generative artificial intelligence, intelligent tutoring systems, virtual and augmented reality, and predictive analytics are increasingly present, their adoption is concentrated in specific areas, particularly problem-solving, simulation, and assessment.
Firas Almasri· International journal of tec...· 0 citations
This narrative review argues that the most consequential effect of GenAI is not the automation of existing teaching practices but the need to redesign curricula, learning outcomes, pedagogies, and assessment around disciplinary judgment, critical verification, intellectual independence, and transparent, ethical use of GenAI.
C. Papaneophytou, Stella A. Nicolaou· Trends in Higher Education· 0 citations
A holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning is proposed, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills.
Lori B. Doyle, Jill L. Swisher· Educational Point· 0 citations
The rapid emergence of generative artificial intelligence (GenAI) is reshaping engineering education. This study investigates the impact of integrating GenAI tools on student learning in the second-year engineering design course at the University of Prince Edward Island by comparing two instructional approaches: a traditional non-AI design project and a GenAI-integrated design project. The study utilized anonymous, voluntary student surveys to evaluate and compare students' learning experiences across both projects. The survey captured both quantitative and qualitative insights into how students perceive the role of GenAI in their learning, collaboration, creativity, and problem-solving processes. Findings show that students most frequently used GenAI for brainstorming, problem definition, and concept generation, while strongly engaging with ethical verification practices. However, perceptions of GenAI’s impact on collaboration and enhancing the efficiency of the design process were mixed. These results inform future learning outcomes, assessment strategies, and broader approaches for responsibly integrating GenAI across engineering programs.
K. Grewal, Mikkayla Ellsworth-Reid, Prabhnoor Sigh et al.· Proceedings of the Canadian...· 0 citations
Artificial intelligence (AI) is reshaping the architecture, engineering and construction (AEC) sector. However, AEC education remains rooted within traditional disciplinary boundaries and a technology-centric training model, creating a widening mismatch between graduates’ capabilities and the cognitive, collaborative, and interdisciplinary demands of AI-enabled practice. Situated within the Chinese higher education context, this study bridges this gap through a multi-stakeholder survey (n = 352) noting perspectives from academia, industry and research. One-way ANOVA and Tukey’s HSD test were used to examine differences across stakeholder groups and disciplines, while a Bayesian Network was developed to model competency pathways, simulate intervention scenarios and identify key leverage points for curriculum reform. The analysis reveals that meaningful AI integration requires a reconstruction of competency, rather than the mere addition of standalone technical or software courses; it calls for fundamental changes in professional formation, curricula and pedagogy. Four core competencies emerged from the data: professional expertise, systems thinking, interdisciplinary collaboration and AI-enabled problem-solving. Bayesian Network simulations further indicated that curriculum expansion alone improved AI knowledge acquisition by 39.6%, but yielded only modest gains in practical skills (13.9%) and application competencies (4.5%). By contrast, integrated interventions that combined teacher development, university–industry collaboration and project-based practice produced substantial improvements in AI application competencies (37.9%), employment adaptability (29.3%) and industry satisfaction (14.1%). These divergent findings highlight the necessity of coordinated educational interventions to reconcile stakeholder expectations and foster AI-oriented competency development. Based on this evidence, the study proposes a competency-oriented framework and a phased curriculum transformation pathway, providing an empirical foundation for AI-driven curriculum reform and competency reconstruction in AEC education.
Panxiu Wang, Zhiqiang Hua, Dawei Wang et al.· Buildings· 0 citations
The paper proposes a framework for bridging generational differences through lifelong learning, digital capacity building, and human-centred AI integration that preserves the relational and ethical foundations of education while embracing technological advancement.
Davendra Sharma· International journal of soc...· 0 citations
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