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Structuring Early Design of AI-Infused Information-Intensive Applications

  • Xiang ‘Anthony’ Chen

Received: 15 June 2026 | Revised: 20 August 2026 | Accepted: 12 October 2026 | Published: 30 October 2026

Abstract

AI holds the promise of augmenting a wide range of knowledge workers’ tasks, many of which involve collecting, organizing, interpreting, comparing, transforming, and acting on domain-specific information. Designing such AI-infused information-intensive applications (A3I) is difficult in the early, divergent stage because designers must decide what role AI should play, where AI should enter a human information-processing workflow, and how initiative should be divided between people and AI. Existing human-AI guidelines and pattern collections provide important support, but they do not necessarily offer a systematic way to surface and compare these early design possibilities before teams commit to an interaction direction. We contribute A3I: a conceptual framework and workbook for structuring early-stage design exploration of AI-infused information-intensive applications. The framework organizes exploration around two design questions. First, what can an AI system do to help a human process information? We distinguish sense-making (via a hierarchic view and filters) from decision-making (using predictive and/or generative AI). Second, when and how should AI be invoked as a human processes information? We consider AI automation (revealing findings upfront or in due course) and mixed initiative (AI acting reactively and/or proactively). We operationalized the framework as the A3I WORKBOOK and conducted a formative, three-part evaluation: (i) deployment in graduate-level course projects (25 students across nine teams); (ii) expert interviews with three industrial researchers; and (iii) a week-long design exercise with seven UX designers in industry. Findings document how participants interpreted and used the workbook, including reported value in structuring thought and noticing previously implicit design dimensions, as well as interpretive difficulties around hierarchy. We discuss lessons for translating HCI theory into actionable design scaffolds and clarify the limits of our evidence, including the need for future comparative and longitudinal studies.

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Chen, X.'A. Structuring Early Design of AI-Infused Information-Intensive Applications. Human-Machine Interaction 2026, 1 (1), 5.
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