Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues (2025)

Abstract: The ability to generate explanations that are understood by explainees is the quintessence of explainable artificial intelligence. Since understanding depends on the explainee’s background and needs, recent research focused on co-constructive explanation dialogues, where an explainer continuously monitors the explainee’s understanding and adapts their explanations dynamically. We investigate the ability of large language models (LLMs) to engage as explainers in co-constructive explanation dialogues. In particular, we present a user study in which explainees interact with an LLM in two settings, one of which involves the LLM being instructed to explain a topic co-constructively. We evaluate the explainees’ understanding before and after the dialogue, as well as their perception of the LLMs’ co-constructive behavior. Our results suggest that LLMs show some co-constructive behaviors, such as asking verification questions, that foster the explainees’ engagement and can improve understanding of a topic. However, their ability to effectively monitor the current understanding and scaffold the explanations accordingly remains limited.

## introduction the computational generation of natural language explanations has gained research interest due to its importance for explainable artificial intelligence (xai), which aims to explain decisions made by ai systems to humans @xcite . a key challenge in xai is the asymmetry between system and human, not only in terms of information access (e.g., only the system has access to information on internal decision processes) but also in terms of the human's knowledge and level of expertise @xcite . therefore, to achieve understanding, it is important to account for the diverse backgrounds and individual abilities of humans @xcite . re- ## co-constructive explanations an effective way to improve explanations is through a co-constructive explanation dialogue @xcite . in this section, we briefly summarize the basic concepts of co-constructive explanations, which we investigate in this study. co-construction is considered an important microlevel aspect in a conversation between an explainer (i.e., the party who explains), and an explainee (i.e., the party who is explained to) @xcite . the goal of a co-constructive explanation is to co-construct understanding of the topic being explained, the so-called explanandum @xcite . this can be achieved by both parties monitoring each other's understanding and scaffolding explanations accordingly @xcite . below, we detail the central concepts of co-constructive processes which are relevant to our evaluation. since we evaluate the co-constructive behavior of llms acting as explainers, we present the concepts from the explainer's perspective. ## co-constructive llms conceptually, the introduced process of coconstructing explanations appears to fit well the interaction capabilities of instruction-tuned llms: although such llms are not inherently designed for the role of a co-constructive explainer, their pretraining and fine-tuning allow them to follow concrete instructions in a dialogical setting @xcite . therefore, it might also be possible to enable co-constructive behavior in explanation dialogues by instructing such llms accordingly. we thus explore whether instruction-tuned llms can effectively lead co-constructive explanation dialogues. in particular, we assess the coconstructive potential of existing llms that is accessible to all users through a system prompt, without the need for complex tuning. we evaluate two settings that resemble different levels of prompt complexity, a base setting and an enhanced setting. figure 2 presents the prompt of each setting (see appendix a.1 for further details). base setting in the base setting, the prompt provides only basic information about the scenario; namely, it instructs the llm to act as an explainer. this setting is used to assess how llms behave by default and assess the impact of a detailed prompt. enhanced setting the enhanced setting uses a prompt that provides more details about the coconstructive explanation setting. specifically, it instructs the llm to act as a co-constructive explainer by applying monitoring and scaffolding, and provides definitions of these two core concepts. we expect that the prompts can be further optimized towards the co-constructive setting, which could result in better co-constructive behavior of the llm. however, this may require a rigorous evaluation of different prompt settings @xcite . since our focus is the co-constructive behavior of llms in general rather than optimizing the system prompt, we leave this to future work. ## experimental setup to evaluate the co-constructive behavior of llms in explanation dialogues, we conduct a user study in which participants receive explanations about one of three explananda by interacting with an llm acting as an explainer. the participants complete pre-and post-interaction questionnaires to assess their understanding of the explanandum, their motivation to learn about it, and their perceptions of the llm's co-constructive behavior. the user study was conducted on prolific. we hired participants who are fluent in english and have completed at least 500 submissions with an approval rate of at least 95%. ## results and discussion in total, 300 participants completed the study. to ensure high-quality data, we excluded from the study those who did not ask questions about their initial explanandum. after filtering, 277 participants in total and at least 45 per llm setting remain (see appendix b.2 for exact counts per topic). below, we first evaluate the co-constructive behavior in both llm settings, followed by the participants' understanding to assess explanatory success. ## qualitative analysis in addition to our quantitative analysis, we examine nine dialogues qualitatively to gain deeper insight into the llm's co-constructive behavior. appendix b.5 shows the dialogue selection and excerpts referenced here by participant id (pid). co-constructive explanations rely on the explainee's active participation. in the base setting, we find that the interactions often follow a teacherstudent setting, with the llm providing long monologues and minimal opportunities for active participation (e.g., pid 4be6). this challenged the processing capacity of some participants (e.g., pid 4be6, yeah, this is a lot can we take it bit by bit). the enhanced setting yields evidence of better monitoring by assessing the explainee's understanding and assigning tasks to reproduce information that has been explained previously (e.g., pid 43b6). in both settings, however, the explainee often has to invest extra effort to enable monitoring and scaffolding from the llm by answering multiple preference questions (e.g., pid 943a, would you like to know more about [...]?), leading to a misalignment with the explainee's needs (e.g., pid 943a, 1570). our asynchronous unimodal chat-like setup likely contributes to this issue by limiting backchanneling of implicit and unconscious signals, that are typically present in synchronous multimodal interactions @xcite . we further observe behavioral differences across topics. the topic black holes led to more monologues that the explainees consumed passively, possibly due to its factual nature. in contrast, sleep and quarto seemingly also allow the explainee to talk about personal experiences (e.g., pid 2417) or made-up game situations (e.g., pid 943a). overall, we observe surface-level adaptation capabilities in the enhanced setting. however, the adaptation appears static due to seemingly incomplete scaffolding capabilities of the llm. in line with our quantitative analysis, the findings emphasize that the out-of-the-box capabilities of the llm benefit motivated participants, but not everyone. ## conclusion in this paper, we have investigated to what extent out-of-the-box llms behave co-constructively in explanation dialogues as well as how this behavior may improve the success of generated explanations. through a user study in which human explainees interacted with an llm explainer, we have evaluated how well llms can follow instructions to apply co-constructive behaviors (monitoring and scaffolding) while explaining a topic to an explainee. in our quantitative and qualitative analyses of the dialogues and the participants' understanding of the topic, we find that explicit prompting can enable the evaluated llm to explain co-constructively to some extent. on the one hand, it actively utilized coconstructive patterns common to monitoring and scaffolding behavior, such as asking verification questions, adjusting the readability of explanations, and encouraging active participation. these patterns show potential to increase the explainees' understanding. on the other hand, the patterns are used inconsistently and rather statically, instead of being adjusted to the explainee's needs. overall, the evaluated llm shows promising results that can serve as a good basis to successfully lead explanation dialogues, but further advancements are needed to enable real co-construction. in particular, we expect that truly co-constructive llms not only engage users in a dialogue but also dynamically adapt through consistently successful monitoring and scaffolding to ensure explanatory success. the insights of this work contribute to the understanding of the adaptivity of llms and thus define a starting point for further work on coconstructive approaches in xai.