  AI is anticipated to enhance human decision-making in high-stakes domains
like aviation, but adoption is often hindered by challenges such as
inappropriate reliance and poor alignment with users' decision-making. Recent
research suggests that a core underlying issue is the recommendation-centric
design of many AI systems, i.e., they give end-to-end recommendations and
ignore the rest of the decision-making process. Alternative support paradigms
are rare, and it remains unclear how the few that do exist compare to
recommendation-centric support. In this work, we aimed to empirically compare
recommendation-centric support to an alternative paradigm, continuous support,
in the context of diversions in aviation. We conducted a mixed-methods study
with 32 professional pilots in a realistic setting. To ensure the quality of
our study scenarios, we conducted a focus group with four additional pilots
prior to the study. We found that continuous support can support pilots'
decision-making in a forward direction, allowing them to think more beyond the
limits of the system and make faster decisions when combined with
recommendations, though the forward support can be disrupted. Participants'
statements further suggest a shift in design goal away from providing
recommendations, to supporting quick information gathering. Our results show
ways to design more helpful and effective AI decision support that goes beyond
end-to-end recommendations.
