2025 ASEE Annual Conference & Exposition

The rapid evolution of artificial intelligence (AI), automation, and technology will require a paradigm shift in higher education curriculum models in the near-term. Historically, higher education has been slow to adapt to change. Resistance to the redefinition of required professional skills often accompanies the emergence of technological advancements, as exemplified by the initial opposition to handheld electronic calculators, software that can solve advanced engineering equations, the use of personal smart devices, and online learning platforms. This inertia also is rooted in several institutional factors, including program accreditation, institutional culture, complex curricula change approval processes, and perhaps a lag in understanding or incorporation of the state of the practice. Furthermore, the emphasis on standardized testing and traditional assessment methods could soon hinder the development of innovative approaches that measure the individual student’s ability to apply knowledge in open-ended contexts using modern tools like large language models and other methods underlaying AI.
To effectively adapt to the current and future rapidly changing technosphere, educators must adopt a novel approach to curriculum revision and development. This involves not only identifying new knowledge and skills to be taught and how to teach them, but also determining what can be de-emphasized or even eliminated. This process requires a critical evaluation of existing curricula, considering their relevance to the evolving needs of students and the demands of the future workforce. Educators should prioritize soft skills that are transferable, adaptable, and essential for success in a technology-driven world, such as critical thinking, problem-solving, creativity, ethics, and human/human as well as human/machine collaboration while still ensuring a relevant, strong technical foundation. By embracing a future-oriented approach that prioritizes adaptability, interdisciplinarity, and rapid change, higher education can effectively prepare students for a world where technology is not merely a tool but a fundamental force shaping the future.
We propose a framework for an undergraduate environmental engineering curriculum model dynamically informed by a suite of AI features to adapt to changes in the practice engendered by AI. We further propose that the framework operates within an accreditation structure designed around a quantitative weighting of assessed topics that facilitates institutional freedom to programmatically innovate. The framework incorporates the identification of emerging technologies or use of AI in the field that is monitored by tailored, AI-powered WebBots, feedback from students using emerging technologies during study, input from external stakeholders on how they expect graduates to employ technologies within their professions, and risk-based assessments on which topics within existing curricula require modification or replacement to best facilitate the edification of future engineers. Additionally, we suggest supporting assessment structures that will give higher education institutions and future licensing bodies confidence in degree conference and professional practice competency respectively. If adopted by institutions and accrediting bodies (e.g., ABET), this framework would enable faculty members to make timely changes to course content and assessment methods in response to changes in technology. The approach would provide a consensus mechanism that allows faculty members to remove outdated lesson material and add emerging applications of technology in the field without putting their students or program at a disadvantage.
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This first draft of this abstract was prepared by generative AI.