  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.
