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The emergence of generative artificial intelligence (GAI) has started to introduce a fundamental reexamination of established teaching methods. These GAI systems offer a chance for both educators and students to reevaluate their academic endeavors. Reevaluation of current practices is particularly pertinent in assessment within engineering instruction, where advanced generative text algorithms are proficient in addressing intricate challenges like those found in engineering courses. While this juncture presents a moment to revisit general assessment methods, the actual response of faculty to the incorporation of GAI in their evaluative techniques remains unclear. To investigate this, we have initiated a study delving into the mental constructs that engineering faculty hold about evaluation, focusing on their evolving attitudes and responses to GAI, as reported in the Fall of 2023. Adopting a long-term data-gathering strategy, we conducted a series of surveys, interviews, and recordings targeting the evaluative decision-making processes of a varied group of engineering educators across the United States. This paper presents the data collection process, our participants’ demographics, our data analysis plan, and initial findings based on the participants’ backgrounds, followed by our future work and potential implications. The analysis of the collected data will utilize qualitative thematic analysis in the next step of our study. Once we complete our study, we believe our findings will sketch the early stages of this emerging paradigm shift in the assessment of undergraduate engineering education, offering a novel perspective on the discourse surrounding evaluation strategies in the field. These insights are vital for stakeholders such as policymakers, educational leaders, and instructors, as they have significant ramifications for policy development, curriculum planning, and the broader dialogue on integrating GAI into educational evaluation.