Information access systems such as search engines and generative AI are central to how people seek, evaluate, and interpret information. Yet most systems are designed to optimise retrieval rather than to help users develop better search strategies or critical awareness. This paper introduces a pedagogical perspective on information access, conceptualising search and conversational systems as instructive interfaces that can teach, guide, and scaffold users' learning. We draw on seven didactic frameworks from education and behavioural science to analyse how existing and emerging system features, including query suggestions, source labels, and conversational or agentic AI, support or limit user learning. Using two illustrative search tasks, we demonstrate how different design choices promote skills such as critical evaluation, metacognitive reflection, and strategy transfer. The paper contributes a conceptual lens for evaluating the instructional value of information access systems and outlines design implications for technologies that foster more effective, reflective, and resilient information seekers.

翻译：搜索引擎与生成式人工智能等信息访问系统已成为人们获取、评估与解读信息的核心工具。然而，现有系统大多侧重于优化检索效果，而非帮助用户提升搜索策略或培养批判性认知。本文引入信息访问的教学视角，将搜索与会话系统概念化为具有教学功能的界面，能够对用户的学习过程进行指导、引导与支架式支持。我们借鉴教育与行为科学领域的七个教学框架，分析现有及新兴系统功能（包括查询建议、来源标注、会话式及智能体人工智能）如何支持或限制用户学习。通过两个典型搜索任务案例，我们论证了不同设计选择如何促进批判性评估、元认知反思及策略迁移等能力的培养。本文为评估信息访问系统的教学价值提供了概念框架，并为设计能够培养更高效、更具反思性与适应性的信息获取者的技术体系提出了设计启示。