Evaluating search engine results pages (SERPs) to assess result relevance is a demanding step in academic search. In a formative mixed-methods design study, we examine AI-generated SERP-level summaries as a support feature in an academic search engine for social science information. First, we manually evaluated summaries of the top five results for 10 queries using two general-purpose models, one commercial and one open, deriving an exploratory six-category error taxonomy and five safeguards for scholarly deployment. We then conducted a within-subjects user study (n = 30) comparing interfaces with and without AI summaries. Confirmatory analyses showed consistent but non-significant trends favoring AI summaries for subjective workload, perceived usefulness, satisfaction, and decision-making confidence. Exploratory analyses suggested lower mental demand, with frustration also tending to be lower. Behaviorally, participants rarely expanded the summaries and descriptively made slightly fewer result clicks and query reformulations when summaries were available. Drawing on Information Foraging Theory and participant feedback, we suggest that AI summaries may concentrate SERP-level information scent to support early triage. Overall, the findings indicate that SERP-level AI summaries are a context- and user-dependent aid rather than a universal improvement, while contributing an error taxonomy, safeguard-aware deployment guidance, and concrete design implications for scholarly search.

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