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Today’s students are diverse and include marginalized groups that have historically been excluded from mainstream education (Ladson-Billings, 2013). In 2021, students of color comprised upwards of 40% of the 15.4 million undergraduates enrolled in U.S. colleges and universities (Nam, 2023; National Center for Education Statistics, 2023). Gloria Ladson-Billings, whose work centers on culturally relevant pedagogy, argues that diverse students require inclusive learning to succeed. “[These students] do not fit neatly into the rigid categories of race, class, gender, or national origin” upon which hierarchies of the past have been built (Ladson-Billings, 2013, p. 5), so authentic representation of diversity in higher education is critical. Adrienne Keene, an assistant professor of American Studies at Brown University, writes that instructors can do their part to support underrepresented students by being honest about their own bias and blind spots, critiquing their course materials, and integrating meaningful representations of diversity into the curriculum (Fuchs et al., 2020; Keene, 2015).

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.