An ontology is a formal, explicit specification of a shared conceptualization that, with problem‑solving and reasoning methods, supports efficient semantic technology development. In ontology engineering, Competency Questions (CQs) capture functional requirements that define an ontology’s application domain. Auditing this domain scope with CQs is challenging because in nature, there are no clear domain boundaries, and ontology engineers must then decide which subdomains to cover (horizontal coverage) and how much detail to model (vertical granularity) in an ontology. LLM‑based systems can generate many candidate CQs to guide these decisions, but current tools underuse this potential: they lack support for users’ divergent (lateral) and convergent (vertical) thinking in a visualized CQs space organized by coverage and granularity. As a result, users struggle to systematically decide which CQs to adopt, discard, or refine. We propose an interaction framework that fills this gap, demonstrated through OntoScope, an LLM‑based interactive system, and a user study with 15 ontology engineers. To our knowledge, this is the first validated interaction framework with an LLM‑based system that helps ontology engineers audit domain boundaries and unifies fragmented, expert‑driven ontology scoping practices into a coherent, accessible approach. More broadly, it shows how LLM‑based systems can transparently and accountably support a wider range of knowledge‑intensive tasks.

An ontology is a formal, explicit specification of a shared conceptualisation, which can be combined with problem-solving methods and reasoning functionality to develop high-quality technology and application systems efficiently. Ontology engineering typically involves extensive manual effort to elicit intended use cases (user stories) from users for the target ontology-based systems. Recent studies have demonstrated the positive potential of large language model-based conversational agents in supporting user story generation in OE. However, we argue that we are not leveraging LLM to its fullest potential by not supporting users in formulating effective prompts. To address this, we identify the prompt guidance users need during user story generation workflows by conducting a formative study (N = 10) using participatory prompting. We demonstrate its usefulness through the design and development of the OntoChat LLM-based system for OE, as well as a user evaluation with knowledge engineers (N = 24). To our knowledge, this is the first work to design and validate a prompt guidance framework that helps users leverage LLM to its fullest potential to generate effective requirements for ontology development. This advances how we interact with LLM for requirements elicitation.

Generative Artificial Intelligence (GenAI) applications in artistic and creative domains have gained substantial attention of late. These intelligent interactive systems, shaped by innovations in Large Language Models (LLMs) and Vision Language Models (VLMs), are materially impacting digital creative domains. While initial work to understand this space has highlighted new models and architectures, we lack a holistic view of how interactive GenAI systems are designed for user interactions across various artistic and creative domains. In this paper, we present a systematic review of interactive GenAI system designs for art and creativity in the HCI literature (N = 189), and a detailed taxonomy of interaction paradigms with design components. We shed light on the communities of design focus and decompose the system interaction designs, mapping these characteristics to creative domains, user interaction patterns, GenAI technologies, detailing under-represented spaces, and future directions of designing interactions for GenAI creativity.