Artificial intelligence (AI) tools are now prevalent in many knowledge work industries. As AI becomes more capable and interactive, there is a growing need for guidance on how to employ AI most effectively. The A2C framework (Tariq, Chhetri, Nepal & Paris, 2024) distinguishes three decision-making modes for engaging AI: automation (AI completes a task, including decision/action), augmentation (AI supports human to decide) and collaboration (iterative interaction between human and AI). However, selecting the appropriate mode for a specific application is not always straightforward. The goal of the present study was to compile and trial a simple set of criteria to support recommendations about appropriate A2C mode for a given application. Drawing on human factors and computer science literature, we identified key criteria related to elements of the task, impacts on worker and support needs. From these criteria we built a scoring rubric with recommendation for A2C mode. As a preliminary test of this approach, we applied the criteria to cognitive task analysis (CTA) outputs from three tasks in the science domain - genome annotation, biological collections curation and protein crystallization - which provided insights into worker decision points, challenges and expert strategies. This paper describes the method for connecting CTA to A2C, reflecting on the challenges and future directions.

翻译：人工智能（AI）工具如今已广泛应用于众多知识型工作领域。随着AI能力日益增强且交互性不断提升，如何最有效地运用AI亟需指导原则。A2C框架（Tariq, Chhetri, Nepal & Paris, 2024）区分了三种运用AI的决策模式：自动化（AI独立完成任务，包括决策/执行）、增强（AI辅助人类决策）与协作（人机间迭代式交互）。然而，为特定应用场景选择合适的模式并非易事。本研究旨在构建并试验一套简明标准体系，以支持针对具体应用场景推荐适宜的A2C模式。借鉴人因工程与计算机科学文献，我们识别出与任务要素、工作者影响及支持需求相关的关键标准，并据此构建了包含A2C模式建议的评分准则。作为该方法的初步验证，我们将标准应用于科学领域三项任务——基因组注释、生物标本库管理与蛋白质结晶——的认知任务分析（CTA）输出结果，这些分析揭示了工作者的决策节点、挑战与专家策略。本文阐述了将CTA与A2C框架对接的方法，并对实施挑战与未来研究方向进行了探讨。