Generative AI Steering and Human Authoring: Complementary Search-as-Learning Scaffolds in Cross-Session Aggregated Academic Search

Abstract

Aggregated academic search interfaces combine resources from heterogeneous sources, such as scholarly libraries and reference encyclopedias, into a single workspace. Such tasks frequently span multiple sessions, requiring learners to return to and resume work after hours or days. End-of-session consolidation, the activity of organizing and synthesizing gathered information so it can be resumed later, is important in this setting but underexplored in search-as-learning. Generative AI (GenAI) has the potential to make consolidation more efficient, but it may also shift cognitive work away from the learner. This study compares two consolidation scaffolds embedded in the workspace framework of an existing academic search interface that aggregates and clusters saved search results from two different sources (our university library and Wikipedia), which was developed in earlier work. STAIS (Steerable AI Summaries) provides a workspace enhancement that produces GenAI summaries that users can correct, reorganize, and fact-check, automating parts of the consolidation activity. NT (Note-Taking) provides a workspace enhancement that includes rich-text note editors that learners can use to author their own consolidations of what they have found. In a controlled between-subjects study (n=36), participants completed two sessions one week apart while searching an unfamiliar topic using either STAIS or NT. The two conditions produced contrasting post-resumption outcome profiles: STAIS supported faster task resumption and was associated with broader conceptual coverage and higher self-reported domain knowledge, whereas NT was associated with richer inter-concept connections and higher self-reported certainty.