GenAI in the classroom: Engineering students embrace AI for smarter learning

Key motivations for GenAI adoption include enhancing comprehension of complex concepts, improving the quality of assignments, and keeping up with emerging technologies. Many students use AI tools to deconstruct complex problems, generate research ideas, validate coding solutions, and streamline the writing process. The study highlights that students see AI as an enabler of efficiency rather than a replacement for learning, with most using it as a complementary tool to traditional study methods.

Artificial intelligence is rapidly transforming the educational landscape, with generative AI (GenAI) playing an increasingly prominent role in student learning and academic work. As AI-powered tools become more sophisticated, engineering students are leveraging them for problem-solving, coding, research assistance, and knowledge enhancement. However, this adoption also raises questions about ethical considerations, academic integrity, and long-term societal impacts.

A new study, “Assessing Student Adoption of Generative Artificial Intelligence across Engineering Education from 2023 to 2024,” by Jesan Ahammed Ovi, Gabe Fierro, and C. Estelle Smith, provides empirical insights into how engineering students at the Colorado School of Mines have integrated GenAI into their academic pursuits. The research, based on surveys conducted in 2023 and 2024, explores the motivations behind GenAI usage, ethical concerns, and students' perceptions of its benefits and risks. The findings reveal a notable increase in GenAI adoption rates while highlighting polarization in opinions regarding its long-term effects on education, careers, and society.

Growing adoption of generative AI in engineering studies

The study shows that engineering students are increasingly incorporating generative AI tools into their academic workflows. Between 2023 and 2024, the percentage of students regularly using AI-powered chatbots for academic purposes rose significantly, with nearly half of the student body now classified as either regular or superusers of GenAI tools. Notably, students in fields like computer science and electrical engineering demonstrated higher engagement with GenAI compared to their counterparts in traditional engineering disciplines such as civil or mechanical engineering.

Key motivations for GenAI adoption include enhancing comprehension of complex concepts, improving the quality of assignments, and keeping up with emerging technologies. Many students use AI tools to deconstruct complex problems, generate research ideas, validate coding solutions, and streamline the writing process. The study highlights that students see AI as an enabler of efficiency rather than a replacement for learning, with most using it as a complementary tool to traditional study methods.

Interestingly, the research also identifies a shift in GenAI usage patterns. While earlier adopters relied on AI primarily for content generation, newer users increasingly leverage it for analysis, synthesis, and refining their own work. This suggests that students are becoming more discerning about the role of AI in their education, using it strategically rather than as a shortcut for academic tasks.

Ethical concerns and polarization in student perceptions

Despite the growing integration of AI into engineering education, students exhibit divided opinions about its ethical implications. The study reports a widespread concern over misinformation, academic dishonesty, and bias in AI-generated content. A significant number of students worry that unchecked reliance on GenAI may lead to a deterioration of fundamental learning skills, especially in disciplines that require problem-solving and critical thinking.

One of the most striking findings of the study is the measurement of “P(doom),” a term referring to the probability of catastrophic consequences resulting from AI. When asked about their estimated likelihood of AI causing severe societal disruptions, students displayed a bimodal distribution- some expressing strong optimism about AI’s benefits, while others voiced deep skepticism and concerns about its long-term effects on employment, ethics, and societal stability.

Furthermore, ethical concerns vary by academic discipline and experience level. Graduate students, for instance, tend to exhibit greater concern about AI’s long-term consequences than undergraduates, possibly due to their exposure to more advanced discussions on ethics, policy, and AI regulation. Additionally, students with prior AI experience tend to perceive the technology as more beneficial, while those with limited exposure are more apprehensive about its risks.

Role of AI in reshaping learning and assessment

The rise of GenAI is not just altering how students complete assignments - it is also challenging traditional assessment models and pedagogical strategies. The study highlights the need for educational institutions to evolve alongside AI advancements. With students increasingly turning to GenAI for coursework, faculty members must find ways to incorporate AI into learning without compromising academic integrity.

The research suggests that engineering educators should rethink evaluation methods to focus on conceptual understanding and problem-solving rather than rote memorization or output-based assessments. Some proposed strategies include:

• Encouraging AI-assisted learning frameworks that teach students how to responsibly interact with GenAI while maintaining academic rigor.
• Designing assignments that emphasize process over product, where students must document their reasoning and critical analysis rather than merely presenting solutions.
• Integrating GenAI discussions into the curriculum, allowing students to explore its potential while critically examining its limitations and ethical dilemmas.

By adapting to these changes, engineering education can strike a balance between leveraging AI for enhanced learning and ensuring that students develop independent problem-solving abilities.

Future of GenAI in engineering education

The study suggests that future research should explore AI’s impact across a broader range of institutions to determine whether trends observed at the Colorado School of Mines are representative of engineering education on a larger scale. Additionally, longitudinal studies could track how students’ perceptions and usage of AI evolve as the technology becomes more deeply embedded in academic and professional environments.

Moreover, collaboration between academia and industry will be crucial in shaping how GenAI is used in engineering practice. As companies increasingly adopt AI-driven tools for engineering design, simulation, and data analysis, universities must prepare students for AI-enhanced workplaces while instilling ethical responsibility.

By fostering responsible AI use and refining assessment methods, universities can ensure that students are equipped with both the technical proficiency and critical thinking skills needed to navigate the AI-driven future of engineering.