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When you think of a scientist, who comes to mind? If it’s Albert Einstein or Charles Darwin, you’re not alone. Gender stereotypes and a lack of inclusive role models in science, technology, engineering, and math (STEM) have contributed to spaces that have not always been welcoming for African American, Indigenous, and Latino students or those from other historically underserved groups (American Association of University Women, n.d.). Kimberlé Crenshaw’s concept of intersectionality, a term she coined in 1989, provides a framework for understanding Black women’s lived and overlapping experiences of racism and sexism (Center for Excellence in Teaching and Learning, n.d.; TED, 2016). Crenshaw, a law professor and Black feminist scholar, explains that “intersectionality is a lens through which you can see where power comes and collides, where it interlocks and intersects” (Columbia Law School, 2017).

Backward design is, as the name suggests, a process for designing curricula, courses, and lectures by working backwards from big-picture learning goals. The concept, introduced by Grant Wiggins and Jay McTighe (2005), suggests that instructors create assessments, activities, and course content that are explicitly aligned with the broader learning goals of the unit. This is different from the traditional content-driven approach to learning design, which focuses on course content first and only secondarily tries to align that content with learning goals.

Higher education institutions are racing to keep pace with the disruption caused by artificial intelligence (AI) tools. A 2023 QuickPoll survey by Educause found that 83% of higher education stakeholders believe generative AI will "profoundly change" the sector over the next three to five years. Additionally, 65% agreed that "the use of generative AI in higher ed has more benefits than drawbacks" (McCormack, 2023, Table 1). While institutions are exploring AI's potential in areas such as admissions, enrollment, administrative duties, scheduling, and institutional data research, this piece focuses on the overarching risks and rewards AI presents in teaching and learning.

Employing a variety of modes of instruction and assessment, as recommended by Universal Design for Learning (UDL) principles, can enhance the learning experience for students in quantitative courses. Diverse elements such as visual aids, interactive features, and real-world applications can complement, extend, or replace traditional lectures and exams. Since classes consist of students with varying learning preferences and strategies, using multiple modes of representation in a course promotes deeper understanding, engagement, and skill development. This piece details design elements that can be particularly impactful in quantitative courses.

Universal Design for Learning (UDL), which has roots in Ronald Mace’s concept of Universal Design, is a pedagogical framework that supports diverse learning needs. According to CAST, the creator of the framework, UDL seeks “to improve and optimize teaching and learning for all people based on scientific insights into how humans learn” (2018). UDL is not a step-by-step curriculum plan, but rather an approach to pedagogy and curriculum development that aims to make the learning environment as accessible as possible for as many learners as possible (Derer, 2021; CAST, 2018).