Future-Ready Nuclear Engineering Education: Integrating AI, Digital Systems, and Advanced Technologies to Close Critical Skills Gaps

The U.S. nuclear sector faces a workforce challenge of national scale. U.S. Department of Energy projections indicate the nuclear workforce may need to grow from roughly 100 000 to approximately 375 000 workers by 2050 to support construction, operations, and supply chain roles across advanced reactor deployment. Meeting this demand requires not only more graduates but also graduates prepared for a qualitatively different operating environment. Modern nuclear facilities function as closed-loop, data-driven, cyber-physical systems in which digital instrumentation and control, artificial intelligence–assisted monitoring, digital twin modeling, and cybersecurity governance are now routine features of plant operation. This paper proposes a general, four-layer curriculum framework (physical systems, measurement and control, digital intelligence, and security and governance) intended to help any ABET-accredited nuclear engineering or nuclear engineering technology program map these competencies onto existing degree structures without necessarily increasing credit hour requirements. The framework preserves the traditional physics-based and fundamentals-based core of nuclear engineering education while adding explicit, regulatory-anchored coverage of digital and cyber-physical competencies as a complement to that core rather than a replacement for it. To illustrate how the framework can be instantiated in practice, the paper presents a single-institution case study: the Thomas Edison State University PSEG-Supported Nuclear Workforce Development Initiative, comprising six stackable undergraduate certificates and three newly developed courses, mapped to ABET Engineering Technology Accreditation Commission and Engineering Accreditation Commission outcomes using a three-level Introduced/Reinforced/Mastered framework. The case study is offered as one worked example of the general framework, not as its primary contribution, and the paper explicitly discusses applicability beyond commercial nuclear power, including fuel cycle, defense, and national laboratory pathways, and the risk that expanded digital systems content could crowd out fundamentals if not deliberately guarded against. The paper’s contributions are (1) a general, system-aligned curriculum architecture usable by any nuclear program; (2) a documented single-institution implementation case study with governance timeline and enrollment projections, offered for adaptation rather than replication in kind; and (3) explicit regulatory anchoring intended to make each curriculum element independently verifiable and transferable.

Authors

Institutions

Publication Details

Journal
Nuclear Technology
Published
2026-11-09
DOI
https://doi.org/10.1080/00295450.2026.2749026
Primary Topic
Engineering Education and Curriculum Development
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Future-Ready Nuclear Engineering Education: Integrating AI, Digital Systems, and Advanced Technologies to Close Critical Skills Gaps

Vaibhav Sinha
Nuclear Technology
Engineering Education and Curriculum Development
article

Future-Ready Nuclear Engineering Education: Integrating AI, Digital Systems, and Advanced Technologies to Close Critical Skills Gaps

Vaibhav Sinha
article en

Abstract

The U.S. nuclear sector faces a workforce challenge of national scale. U.S. Department of Energy projections indicate the nuclear workforce may need to grow from roughly 100 000 to approximately 375 000 workers by 2050 to support construction, operations, and supply chain roles across advanced reactor deployment. Meeting this demand requires not only more graduates but also graduates prepared for a qualitatively different operating environment. Modern nuclear facilities function as closed-loop, data-driven, cyber-physical systems in which digital instrumentation and control, artificial intelligence–assisted monitoring, digital twin modeling, and cybersecurity governance are now routine features of plant operation. This paper proposes a general, four-layer curriculum framework (physical systems, measurement and control, digital intelligence, and security and governance) intended to help any ABET-accredited nuclear engineering or nuclear engineering technology program map these competencies onto existing degree structures without necessarily increasing credit hour requirements. The framework preserves the traditional physics-based and fundamentals-based core of nuclear engineering education while adding explicit, regulatory-anchored coverage of digital and cyber-physical competencies as a complement to that core rather than a replacement for it. To illustrate how the framework can be instantiated in practice, the paper presents a single-institution case study: the Thomas Edison State University PSEG-Supported Nuclear Workforce Development Initiative, comprising six stackable undergraduate certificates and three newly developed courses, mapped to ABET Engineering Technology Accreditation Commission and Engineering Accreditation Commission outcomes using a three-level Introduced/Reinforced/Mastered framework. The case study is offered as one worked example of the general framework, not as its primary contribution, and the paper explicitly discusses applicability beyond commercial nuclear power, including fuel cycle, defense, and national laboratory pathways, and the risk that expanded digital systems content could crowd out fundamentals if not deliberately guarded against. The paper’s contributions are (1) a general, system-aligned curriculum architecture usable by any nuclear program; (2) a documented single-institution implementation case study with governance timeline and enrollment projections, offered for adaptation rather than replication in kind; and (3) explicit regulatory anchoring intended to make each curriculum element independently verifiable and transferable.

Nuclear Technology
Thomas Edison State University (US)
Engineering Education and Curriculum Development
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon