The evolution of Explainable Artificial Intelligence (XAI) has emphasised the
significance of meeting diverse user needs. The approaches to identifying and
addressing these needs must also advance, recognising that explanation
experiences are subjective, user-centred processes that interact with users
towards a better understanding of AI decision-making. This paper delves into
the interrelations in multi-faceted XAI and examines how different types of
explanations collaboratively meet users' XAI needs. We introduce the Intent
Fulfilment Framework (IFF) for creating explanation experiences. The novelty of
this paper lies in recognising the importance of "follow-up" on explanations
for obtaining clarity, verification and/or substitution. Moreover, the
Explanation Experience Dialogue Model integrates the IFF and "Explanation
Followups" to provide users with a conversational interface for exploring their
explanation needs, thereby creating explanation experiences. Quantitative and
qualitative findings from our comparative user study demonstrate the impact of
the IFF in improving user engagement, the utility of the AI system and the
overall user experience. Overall, we reinforce the principle that "one
explanation does not fit all" to create explanation experiences that guide the
complex interaction through conversation.

本文研究了多方位可解释人工智能（XAI）的相互关系，探讨了不同类型的解释如何协同满足用户的 XAI 需求。我们介绍了 Intent Fulfilment Framework（IFF）来创建解释体验，并结合 Explanation Followups 建立 Explanation Experience Dialogue Model，为用户提供对话式界面来探索他们的解释需求，从而创造解释体验。通过定量和定性研究结果，我们证明了 IFF 对于提升用户参与度、AI 系统的效用和整体用户体验的影响。总的来说，我们强调 “一种解释不适用于所有情况” 的原则，以对话的方式引导复杂的互动过程。