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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.

Discussions are an impactful way to build engagement and discourse in asynchronous online courses. When properly designed, discussions can encompass the three pillars of engagement: student-content, student-student, and student-instructor. Asynchronous discussions allow students time to reflect prior to participating, which can lead to deeper insights and richer discourse. Moreover, when participating in discussions, students have the opportunity to collaborate with others, participate in an online learning community, and gain insights from others’ unique experiences and perspectives (Ransdell, Borror & Su, 2018). Facilitating multiple types of engagement in an online course can improve student motivation, satisfaction, and achievement (Dailey-Hebert, 2018).