Integrating Artificial Intelligence in Engineering Education: A Work-in-Progress Systematic Review of Applications and Challenges

Thomas Franklin Hallmark

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Conference: 2025 ASEE -GSW Annual Conference
Location: Arlington, TX, Texas
Publication Date: March 9, 2025
Start Date: March 9, 2025
End Date: March 11, 2025
Page Count: 25
DOI: 10.18260/1-2--55058
Permanent URL: https://peer.asee.org/55058
Download Count: 60

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Thomas Franklin Hallmark Texas A&M University orcid.org/0009-0002-3124-8140

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Abstract

This Work-in-Progress (WIP) study systematically reviews the integration of Artificial Intelligence (AI) and virtual simulation tools in engineering education, focusing on their applications in both theoretical and laboratory settings. By examining how these technologies reshape traditional teaching methods, this study identifies effective practices for enhancing student engagement, performance, and skill development. These tools present transformative opportunities to enhance instructional delivery, personalize learning pathways, and bridge the gap between theoretical knowledge and practical application (Chen & Huang, 2020; Jungwoo & Winke, 2021).

For example, virtual simulation tools like the Mars Exploration Control lab (Wang et al., 2024) enable students to engage in real-world engineering challenges, such as simulating autonomous rover navigation in complex terrains. This virtual lab has demonstrated a 30% increase in student engagement and a 20% boost in conceptual understanding, while improving team-based problem-solving dynamics by 25%. Similarly, AI-driven intelligent tutoring systems, applied in circuit analysis courses, provide real-time feedback on errors during problem-solving. These systems have reduced debugging times by 15% and led to over 85% of participants reporting improved confidence in their technical skills (Chen & Huang, 2020).

This review centers on two primary research questions: (1) What AI and simulation tools are currently implemented in engineering education, and how are they applied? (2) What measurable impacts do these tools have on student outcomes, such as problem-solving accuracy, engagement levels, and the application of theoretical knowledge to real-world challenges? To address these questions, this study synthesizes findings from 25 quantitative empirical research studies published between 2012 and 2024, sourced from IEEE Xplore, ERIC, and Web of Science. Duplicates across databases were removed, and all studies underwent thematic coding to uncover patterns in how these technologies improve critical thinking, foster conceptual understanding, and enhance professional skill development.

Despite these benefits, significant challenges hinder broader adoption. High implementation costs, limited institutional support, and gaps in faculty preparedness remain critical barriers (Zhao & Chen, 2022). For instance, while virtual labs like the Mars Exploration Control platform offer cost-effective solutions for resource-constrained environments, educators often feel unprepared to integrate these tools into their teaching. Targeted faculty workshops have been shown to increase confidence in integrating AI tools by 40%, highlighting the importance of strategic professional development (Zhao & Chen, 2022). Additionally, pilot programs at resource-limited institutions demonstrate that even simplified versions of AI tools can yield success: a deployment of the Mars Exploration Control lab in a remote learning setting improved course retention rates by 12% compared to traditional classrooms, underscoring the scalability of these technologies.

This review uniquely synthesizes findings across diverse AI applications, bridging the gap between virtual simulations and intelligent tutoring systems to provide a holistic framework for engineering education. By focusing on tools that directly link theoretical learning to hands-on engineering challenges, this study addresses a critical need for preparing students for an AI-enhanced workplace. Additionally, it prioritizes equitable adoption by proposing strategies that ensure these tools are accessible to resource-limited institutions. With AI becoming a cornerstone of global engineering innovation, programs integrating these tools not only improve student outcomes but also position graduates for success in a competitive job market. For instance, 70% of students exposed to AI-enhanced labs reported a stronger ability to apply theoretical concepts during internships compared to 50% of their peers in traditional settings (De Silva et al., 2024). Institutions that lead in AI adoption will not only better prepare students but also secure their place as global leaders in STEM innovation, setting benchmarks for excellence in engineering education.

Future work will expand the scope of this systematic review to include a larger set of articles and diverse institutional contexts. Next steps involve refining the thematic coding framework to account for emerging tools and conducting detailed analyses to inform faculty training, curriculum design, and institutional policies. The urgency of adopting these tools cannot be overstated—engineering programs that fail to embrace AI risk falling behind in producing graduates equipped for a rapidly advancing global workforce. By addressing equity challenges and creating scalable strategies, this study aims to empower educators and administrators with actionable insights that transform engineering education for an AI-driven future.

REFERENCES

Chen, L., & Huang, W. (2020). Intelligent tutoring systems in engineering education: A systematic review and analysis. Journal of Engineering Education Research, 29(3), 205–224.

De Silva, D., Kaynak, O., El-Ayoubi, M., Mills, N., Alahakoon, D., & Manic, M. (2024). Opportunities and challenges of generative artificial intelligence: Research, education, industry engagement, and social impact. IEEE Industrial Electronics Magazine, 18(1), 20–34. https://doi.org/10.1109/MIE.2024.3382962

Jungwoo, R., & Winke, K. (2021). Innovative learning environments in STEM higher education: Opportunities and challenges of AI-assisted education. STEAM Education Journal, 15(2), 45–61.

Wang, Z., Liu, Y., Wang, L., & Fu, L. (2024). A Mars exploration control virtual simulation experiment platform for engineering practice in control engineering education. IEEE Transactions on Education, 67(4), 610–619. https://doi.org/10.1109/TE.2024.3392332

Zhao, Y., & Chen, F. (2022). Educator preparedness for AI integration: Addressing the training gap in engineering fields. Journal of Educational Technology & Society, 25(3), 33–44.

Citation
Format

Hallmark, T. F. (2025, March), Integrating Artificial Intelligence in Engineering Education: A Work-in-Progress Systematic Review of Applications and Challenges Paper presented at 2025 ASEE -GSW Annual Conference, Arlington, TX, Texas. 10.18260/1-2--55058

TY - CPAPER
AB - This Work-in-Progress (WIP) study systematically reviews the integration of Artificial Intelligence (AI) and virtual simulation tools in engineering education, focusing on their applications in both theoretical and laboratory settings. By examining how these technologies reshape traditional teaching methods, this study identifies effective practices for enhancing student engagement, performance, and skill development. These tools present transformative opportunities to enhance instructional delivery, personalize learning pathways, and bridge the gap between theoretical knowledge and practical application (Chen &amp;amp; Huang, 2020; Jungwoo &amp;amp; Winke, 2021).

For example, virtual simulation tools like the Mars Exploration Control lab (Wang et al., 2024) enable students to engage in real-world engineering challenges, such as simulating autonomous rover navigation in complex terrains. This virtual lab has demonstrated a 30% increase in student engagement and a 20% boost in conceptual understanding, while improving team-based problem-solving dynamics by 25%. Similarly, AI-driven intelligent tutoring systems, applied in circuit analysis courses, provide real-time feedback on errors during problem-solving. These systems have reduced debugging times by 15% and led to over 85% of participants reporting improved confidence in their technical skills (Chen &amp;amp; Huang, 2020).

This review centers on two primary research questions: (1) What AI and simulation tools are currently implemented in engineering education, and how are they applied? (2) What measurable impacts do these tools have on student outcomes, such as problem-solving accuracy, engagement levels, and the application of theoretical knowledge to real-world challenges? To address these questions, this study synthesizes findings from 25 quantitative empirical research studies published between 2012 and 2024, sourced from IEEE Xplore, ERIC, and Web of Science. Duplicates across databases were removed, and all studies underwent thematic coding to uncover patterns in how these technologies improve critical thinking, foster conceptual understanding, and enhance professional skill development.

Despite these benefits, significant challenges hinder broader adoption. High implementation costs, limited institutional support, and gaps in faculty preparedness remain critical barriers (Zhao &amp;amp; Chen, 2022). For instance, while virtual labs like the Mars Exploration Control platform offer cost-effective solutions for resource-constrained environments, educators often feel unprepared to integrate these tools into their teaching. Targeted faculty workshops have been shown to increase confidence in integrating AI tools by 40%, highlighting the importance of strategic professional development (Zhao &amp;amp; Chen, 2022). Additionally, pilot programs at resource-limited institutions demonstrate that even simplified versions of AI tools can yield success: a deployment of the Mars Exploration Control lab in a remote learning setting improved course retention rates by 12% compared to traditional classrooms, underscoring the scalability of these technologies.

This review uniquely synthesizes findings across diverse AI applications, bridging the gap between virtual simulations and intelligent tutoring systems to provide a holistic framework for engineering education. By focusing on tools that directly link theoretical learning to hands-on engineering challenges, this study addresses a critical need for preparing students for an AI-enhanced workplace. Additionally, it prioritizes equitable adoption by proposing strategies that ensure these tools are accessible to resource-limited institutions. With AI becoming a cornerstone of global engineering innovation, programs integrating these tools not only improve student outcomes but also position graduates for success in a competitive job market. For instance, 70% of students exposed to AI-enhanced labs reported a stronger ability to apply theoretical concepts during internships compared to 50% of their peers in traditional settings (De Silva et al., 2024). Institutions that lead in AI adoption will not only better prepare students but also secure their place as global leaders in STEM innovation, setting benchmarks for excellence in engineering education.

Future work will expand the scope of this systematic review to include a larger set of articles and diverse institutional contexts. Next steps involve refining the thematic coding framework to account for emerging tools and conducting detailed analyses to inform faculty training, curriculum design, and institutional policies. The urgency of adopting these tools cannot be overstated—engineering programs that fail to embrace AI risk falling behind in producing graduates equipped for a rapidly advancing global workforce. By addressing equity challenges and creating scalable strategies, this study aims to empower educators and administrators with actionable insights that transform engineering education for an AI-driven future.

REFERENCES

Chen, L., &amp;amp; Huang, W. (2020). Intelligent tutoring systems in engineering education: A systematic review and analysis. Journal of Engineering Education Research, 29(3), 205–224.

De Silva, D., Kaynak, O., El-Ayoubi, M., Mills, N., Alahakoon, D., &amp;amp; Manic, M. (2024). Opportunities and challenges of generative artificial intelligence: Research, education, industry engagement, and social impact. IEEE Industrial Electronics Magazine, 18(1), 20–34. https://doi.org/10.1109/MIE.2024.3382962

Jungwoo, R., &amp;amp; Winke, K. (2021). Innovative learning environments in STEM higher education: Opportunities and challenges of AI-assisted education. STEAM Education Journal, 15(2), 45–61.

Wang, Z., Liu, Y., Wang, L., &amp;amp; Fu, L. (2024). A Mars exploration control virtual simulation experiment platform for engineering practice in control engineering education. IEEE Transactions on Education, 67(4), 610–619. https://doi.org/10.1109/TE.2024.3392332

Zhao, Y., &amp;amp; Chen, F. (2022). Educator preparedness for AI integration: Addressing the training gap in engineering fields. Journal of Educational Technology &amp;amp; Society, 25(3), 33–44.

AU - Thomas Franklin Hallmark
CY - Arlington, TX, Texas
DA - 2025/03/09
PB - ASEE Conferences
TI - Integrating Artificial Intelligence in Engineering Education: A Work-in-Progress Systematic Review of Applications and Challenges
UR - https://peer.asee.org/55058
DO - 10.18260/1-2--55058
ER -