As people age, they can expect some of their memory to fail or to become slower while doing mental calculations. However, it is an oversimplification to describe cognitive aging as only cognitive deterioration. This dissertation explores various ways that adult learners can adapt to cognitive changes brought by aging. In chapter 1, we find that older adults are worse than younger adults at a planning task because they use suboptimal planning strategies. Once we teach older adults the optimal strategy, there are no longer differences in performance between the two age groups. In chapter 2, we show how extra practice can help older adults increase their ability to match or exceed the performance of a younger adult on various cognitive tasks. In chapters 3 and 4, we investigate how older adults allocate their effort towards learning new technologies depending on ease of use and the amount of time needed to learn. We found that older adults can learn a complex virtual machine as well as younger adults and that older adults are as willing as younger adults to expend effort towards learning difficult machines. However, we also found that older adults will conserve their effort and choose easier machines when we manipulated the amount of time they have to learn. Altogether, these results support the notion of older adults as adaptive learners and help provide a more nuanced view of cognitive aging, one where older adults are sometimes capable of excelling on cognitive tasks.