The quick evolution of engineering education presents new challenges for students and educators. A clear example is the continuous expansion of engineering curricula, which demand mastering a broader range of concepts within limited timeframes. At the same time, students tend to rely on last-minute studying, often overestimating their theoretical understanding. Additionally, artificial intelligence (AI)-based tools, such as ChatGPT, have altered traditional learning methods, shifting students’ focus toward obtaining quick answers rather than developing a deeper, structured understanding of complex problems. As engineering programs continue to expand their syllabi without extending academic terms, evaluation methods must evolve to ensure that students are not only passing exams but also developing the competencies required in professional practice.

This study evaluates the impact of implementing self-assessment tests in two engineering modules: Industrial Electrical Installations and Electrical Machines. These multiple-choice tests are made available after each theoretical session for students to complete independently. Our findings suggest that these assessments help students measure their actual level of comprehension, adjusting their study strategies accordingly, thus resulting in improved performance.

Beyond their immediate impact on exam performance, self-assessment tests provide metacognitive benefits, helping students identify knowledge gaps early and refine their learning approaches. This is particularly relevant in engineering disciplines, where a solid theoretical foundation is crucial for later problem-solving applications.

Furthermore, the introduction of these tests raises key questions about how students engage with knowledge today:
- Are traditional study methods becoming obsolete in favor of AI-assisted learning?
- Does the instant accessibility of AI-generated explanations encourage superficial understanding over deep learning?
- How can self-assessment tests be adapted to complement these emerging learning tools?

In the full paper, we will analyze grade distributions before and after introducing self-assessment tests, providing quantitative evidence of their effectiveness. Additionally, we will explore student feedback on their perceived usefulness and examine whether they contribute to long-term retention of concepts or merely optimize short-term performance.

By providing both quantitative and qualitative insights, this paper aims to contribute to the ongoing debate on engineering education methodologies, offering practical recommendations on how self-assessment strategies can enhance student preparedness in an era of rapidly changing learning paradigms.

Keywords: Engineering, AI-assisted learning, evaluation, self-assessment.