1. INTRODUCTION

Artificial intelligence (AI) is becoming a crucial force in engineering education. Generative AI, intelligent tutoring systems, AI-assisted coding environments, and simulation platforms are reshaping how students search for information, solve technical problems, prepare reports, and approach design tasks. Large language models (LLMs), including ChatGPT, Claude, Copilot, Gemini, and DeepSeek, have also become extensively available to students and educators. Thus, their influence is no longer limited to advanced computing courses; it now affects core engineering subjects such as mathematics, mechanics, materials, structures, and construction management analysis (Holmes et al. 2019; Zawacki-Richter et al. 2019).

Considering the use of AI, like any technology, may inherently have pros and cons in our education, the key question is how AI can be incorporated so that the negative effects are minimized and we can enhance the positive aspects of our engineering education (Luckin et al. 2016; UNESCO 2021). Poorly guided implementation can produce shallow learning and academic integrity concerns, while responsible adoption can enhance access, feedback, creativity, and professional readiness. Similar to earlier transitions involving computer-aided design and numerical modelling tools, AI is likely to shift the emphasis in engineering education rather than eliminate the need for foundational knowledge. Thus, students must still understand first principles, verify outputs, and exercise professional judgment, mainly because engineering decisions often affect public safety. This realization is foundational for our students. Otherwise, fully depending on AI could be disastrous for the next generation. Overall, the opportunities and risks of using AI in engineering education are captured in Figure 1.

2. OPPORTUNITIES FOR ENGINEERING EDUCATION 

AI offers several opportunities to enhance teaching and learning in engineering programs. First, AI systems can support personalized learning by adapting explanations, examples, and practice problems to the requirements of individual students. This is predominantly useful in technically demanding subjects where students often require repeated explanations, step-by-step feedback, and different examples. AI tutors can offer instant support outside scheduled class hours, which may help students close knowledge gaps before they become barriers to progression (Pane et al. 2015).

Figure 1. Overview of the major opportunities and risks of AI adoption in engineering education and the importance of responsible and ethical implementation.

Second, AI can improve accessibility and inclusive education. Students who face language barriers, learning differences, or limited access to tutoring can use AI tools for multilingual explanations, text simplification, writing support, and assisted learning. When properly supervised, AI can help broaden participation and reduce dependence on informal support networks. Still, accessibility benefits would rely on equitable availability, institutional support, and clear guidance on acceptable usage (UNESCO 2021).

Third, AI can increase productivity for both students and educators. Routine tasks such as code debugging, data cleaning, literature organization, formatting, and initial content generation can be accelerated, thereby freeing up time for conceptual understanding, design reasoning, and interpretation. Meanwhile, for educators, AI can assist with drafting examples, generating practice questions, creating rubrics, and offering appropriate feedback. These benefits are strongest when AI is used to support learning processes rather than to bypass them (Luckin et al. 2016).

Fourth, AI literacy aligns engineering education with current professional practice. Modern engineering progressively involves data-driven design, digital twins, smart infrastructure, predictive maintenance, robotics, and automated inspection systems. So, graduates who understand both the abilities and limitations of AI will be better prepared to work in interdisciplinary teams and to assess AI-supported decisions in practice. Thus, AI should be treated as a crucial component of emerging engineering competence (Lee et al. 2018).

AI is transforming engineering education by making learning more personalized, efficient, and accessible. It enables adaptive instruction tailored to individual student needs, enhances creativity and problem-solving through advanced tools, and automates routine tasks so students can focus on higher-level thinking. AI also expands access to quality education via virtual platforms and provides real-time feedback that improves learning outcomes. At the same time, it aligns education with modern industry practices, supports interdisciplinary collaboration, and promotes lifelong learning, preparing future engineers to continuously adapt in a rapidly evolving technological landscape.

3. RISKS AND CHALLENGES

Despite these opportunities, AI adoption introduces important risks. A major concern is overdependence. If students routinely rely on AI-generated solutions without understanding the underlying principles, their ability to perform independent analysis may decline, leading to “black-box” thinking. This risk is particularly serious in engineering, where errors in assumptions, units, boundary conditions, and interpretation can result in unsafe designs. AI tools can generate fluent but incorrect responses, and students may accept these AI-based outputs when they lack the confidence or knowledge to challenge them (Cotton et al. 2024; Selwyn 2019).

A related concern is loss of foundational skills. Mathematics, mechanics, programming, technical drawing, and analytical reasoning remain vital to engineering formation. Thus, AI can support these skills, but it can also mask flaws if students adopt it to complete assignments without active engagement (Kasneci et al. 2023). Heavy reliance on AI-generated solutions may reduce analytical reasoning, and students may struggle to validate or question outputs. Therefore, engineering programs must distinguish between AI usage that develops competence and that replaces learning (Bender et al. 2021). Virtual tools may reduce exposure to physical experiments and fieldwork, potentially weakening practical engineering intuition, which is critical while engineers serve in the field.

Furthermore, academic integrity is another major challenge. Generative AI complicates conventional evaluation because it can generate essays, code, calculations, and design narratives with minimal student input. This makes authorship difficult to substantiate and may reduce the reliability of assignment evaluations (Cotton et al. 2024). Besides, AI models may reflect biases in their training data, leading students to propagate biased or flawed solutions unknowingly. The use of AI platforms in education may expose sensitive student data, raising ethical concerns about privacy, data ownership, and the risk of increased surveillance. Also, unequal access to advanced AI tools may create disparities between students who can afford premium packages and those who cannot. Thus, institutions must develop transparent policies that address disclosure, fairness, privacy, and responsible usage (Wakjira et al. 2025).

4. IMPLICATIONS FOR CURRICULUM AND ASSESSMENT 

Engineering education should respond to AI by restructuring curricula and assessments rather than by attempting to disregard the tool. Thus, AI literacy should be embedded across programs, including prompt formulation, verification of AI outputs, bias and uncertainty, data privacy, citation practices, and ethical responsibilities. Hence, students should learn that AI-generated responses are not authoritative unless they are independently checked using engineering principles, standards, experiments, or validated models (UNESCO 2021).

Rapid AI evolution may outpace curriculum updates, and faculty may lack the training or confidence to integrate AI effectively. Assessment strategies should also evolve. In-class problem-solving, oral examinations, live coding, design reviews, laboratory demonstrations, and project-based evaluations can support genuine understanding. Assignments should be redesigned to involve reflection, justification of assumptions, comparison with hand calculations, and transparent documentation of AI usage. Traditional assessments may become less effective; rather than asking only for final answers, educators should evaluate reasoning, verification, interpretation, and communication (Cotton et al. 2024; Kasneci et al. 2023). There is a clear need for new evaluation methods, such as project-based evaluations and oral defences.

Finally, engineering programs should highlight human skills that AI cannot replace, including creativity, judgment, teamwork, ethics, communication, and responsibility for public welfare. The future engineer will not simply be a user of AI tools, but a professional who can decide when AI is appropriate, when its outputs are unreliable, and how to integrate it responsibly into engineering workflows (Holmes et al. 2019; Luckin et al. 2016).

The AI boom has recently disrupted the job market in the tech sector. However, its impact will soon be experienced in various other sectors as well. Students may face increasing uncertainty about future roles, underscoring the need to redefine engineering competence to emphasize adaptability, critical thinking, and effective collaboration with AI systems. In engineering education, this uncertainty underscores the need to redesign curricula to focus on adaptable skills, critical thinking, and the ability to work effectively alongside AI, rather than relying solely on traditional technical competencies.

5. CONCLUSIONS

AI must not be considered a threat to our engineering education, though its careless, unstructured implementation can undermine the development of core engineering proficiency. Responsible AI use can improve personalized learning, accessibility, productivity, and industry readiness. Thus, the goal of AI implementation should be to augment, not replace, human thinking. Engineering educators should incorporate AI through clear policies, literacy training, restructured assessments, and a sustained emphasis on foundational principles and professional judgment. Future engineers must be able not only to adopt AI, but also to question, validate, and responsibly apply AI in safety-critical contexts.

Overall, engineering education requires the responsible and ethical integration of AI, ensuring fairness, transparency, privacy, and accountability in both educational practices and technological applications.

ACKNOWLEDGMENTS

The authors acknowledge the partial use of Microsoft Copilot and ChatGPT-5 in assisting with the conceptualization and generation of the figure presented in this work.

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