Since the emergence of the field of eXplainable Artificial Intelligence (XAI), a growing number of researchers have argued that XAI should consider insights from the social sciences in order to adapt explanations to the expectations and needs of human users. This has led to the emergence of a field called Social XAI, which is concerned with understanding how explanations are actively shaped in the interaction between a human user and an AI system. Recognizing this turn in XAI toward making XAI systems more “social” by providing explanations that focus on human information needs and incorporating insights from human–human explanatory interactions, in this paper we provide a formal foundation for Social XAI. We do so by proposing novel ontological accounts of the key terms used in Social XAI based on Basic Formal Ontology (BFO). Specifically, we provide novel ontological accounts for explanandum, explanans, understanding, explanation, explainer, explainee, and context. In doing so, we discuss multifaceted entities in Social XAI (having both continuant and occurrent facets; e.g., explanation) and the relationship between understanding and explanation. Additionally, we propose solutions to seemingly paradoxical views on some terms (e.g., social constructivist vs. individual constructivist perspective on explanandum).

Conversational XAI systems often follow a limited question–answering paradigm, handling user queries independently without tracking evolving understanding or adapting explanations. We present CoCoXplain, a conversational XAI agent that treats explanation as a co-constructed process shaped jointly by explainer and explainee. Built on the MAPE-K loop and implemented with interacting LLM agents, CoCoXplain monitors user comprehension, maintains an explicit user model, and adapts explanation strategies dynamically. The design draws on human–human explanation, dialogue models, and socio-cognitive factors, operationalizing co-construction through monitoring and scaffolding. In an online study with 192 participants, CoCoXplain achieved comparable objective understanding to a click-based baseline while significantly improving subjective understanding, perceived co-construction (monitoring, scaffolding, collaborative construction), and social dimensions such as sociability, trust, usability, and user–agent alliance. These results show that structured adaptive architectures enable explanations to evolve with the user, offering more effective and socially attuned XAI than static information delivery.

Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an explainer discusses a concept or phenomenon of interest with an explainee. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee’s side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum Explain Like I am Five and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.

To produce explanations that are more likely to be accepted by humans, Explainable Artificial Intelligence (XAI) systems need to incorporate explanation models grounded in human communication patterns. So far, little is known about how an explainee, who lacks understanding of an issue, and an explainer, who has knowledge to fill the explainee’s knowledge gap, actively shape an explanation process, and how their involvement relates to explanatory success in terms of maximizing the explainee’s level of understanding. In this paper, we characterize explanations as dialogues in which explainee and explainer take turns to advance the explanation process. We build on an existing annotation scheme of ‘explanatory moves’ to characterize such turns, and manually annotate 362 dialogical explanations from the “Explain Like I’m Five” subreddit. Building on the annotated data, we compute correlations between explanatory moves and explanatory success, measured on a five-point Likert scale, in order to identify factors that are significantly correlated with explanatory success. Based on a qualitative analysis of these factors, we develop a conceptual model of the main factors that contribute to the success of explanatory dialogues.

The artifacts produced by current (semi-)automatic methods of ontology learning from text have yet to be improved so that they can provide significant support in creating rich and expressive ontologies. Hence, it is our goal in this study to explore ways to create much more enriched ontologies. In this short paper, we discuss the hypotheses of a PhD work, which addresses the problem of how to reuse the freely available knowledge in Linked Open Data as background knowledge beside text in order to extract new ontological or assertional knowledge for creating a more enriched ontology. In other words, we hypothesize that by using the extra knowledge in large RDF datasets in Linked Open Data cloud, the functions associated with the layers of Ontology Learning Stack could be improved, resulting in more enriched ontologies.

Manual creation of ontologies is a time-consuming, costly and complicated process. Consequently, over the past decade a significant number of methods have been proposed for (semi)automatic generation of ontologies from existing data, especially textual ones. However, there are still significant limitations in this area. This study is an early effort towards reusing the semantic knowledge freely available in Web of Linked Data to improve the results of ontology learning from text in terms of multilingual making of ontology terms, classification of dangling instances, recommending appropriate intensions for ontology concepts, and concept hierarchy enrichment. Actually, these are the tasks associated with the second, third and fourth layer of Ontology Learning Stack. In our first stage experimental efforts, Factforge was used to implement the research objectives. Then, the results gained by an expert were compared against those obtained automatically. Finally, the experimental results verified the importance of the proposed approach through the achieved improvements in most of the objectives mentioned.