Reframing Engineering Education for the Age of AI-Integrated Digital Twins: A Conceptual Framework for Practice-Oriented Learning

Abiola Ayopo Abiodun

doi:10.70148/rise.v3i5.11

Pengarang

Abiola Ayopo Abiodun Faculty of Engineering, Final International University, 99370, Catalkoy, Kyrenia, North Cyprus via Mersin 10, Turkey Author

DOI:

https://doi.org/10.70148/rise.v3i5.11

Kata kunci:

engineering education, digital twin learning, artificial intelligence, practice-oriented learning, decision-based learning, curriculum innovation, industry–academia integration

Abstrak

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.

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