In science fiction writing, ideation demands both novelty to construct fictional worlds and consistency to maintain internal and temporal logic within those worlds. While large language models (LLMs) are increasingly adopted as co-creators, generated ideas often lack surprise and struggle to maintain consistency. Moreover, current interaction paradigms of human-AI co-creation systems fail to support the fragmented and iterative nature of science fiction ideation. To address these challenges, we introduce Sci-Fi Spark, a human-AI co-creation system to support inspiration and organization in the ideation phase. The system features an Ideation Canvas for visualizing relationships between fragmented ideas, a Novelty Generator that applies computational creativity strategies to produce novel worldbuilding inspirations, and a Consistency Generator to produce context-aware storyline suggestions. A technical evaluation and a user study with writers show that Sci-Fi Spark enhances both novelty and consistency, while enabling iterative co-ideation with LLMs.

Social chatbots are increasingly studied for their benefits in providing companionship and emotional support. These benefits rely on forming human-chatbot relationships that require credible social identity and reciprocal interaction. Memory plays a dual role: it strengthens social identity by enabling the chatbot to remember, and supports reciprocal interaction when memories are disclosed mutually. We present RECALLbot, an LLM-driven social chatbot that constructs agentic memories, including life-like Me Memory and co-constructed We Memory, and adaptively applies reciprocal disclosure strategies with user controls. In a two-week between-subjects study (N = 40), RECALLbot was compared with a baseline system lacking agentic memories and reciprocal disclosure strategies. Results show that RECALLbot enhanced perceptions of the chatbot’s social identity, elicited more frequent and deeper self-disclosures, and fostered greater trust.

A design method card deck helps designers understand and provoke thinking by presenting each method in a simple format and allow designers to switch between methods seamlessly by maintaining the same simple format across the deck. However, recent observations have shown designers hesitate to use a card deck due to the lack of support, while other tools have provided identified support with generative AI. Through a formative study, we identified the specific support designers need when applying the design method cards and intentions in integrating generative AI. Accordingly, we developed the intelligent design method card deck, I-Card, which integrates generative AI to provide applicable design methods, design knowledge and data support, and interactive and dynamic support. A user study demonstrates that I-Card improved the design efficiency and applicability by offering personalized guidance, enhanced decision-making with comprehensive data generation and provided more design inspiration via interactive support.

Bio-inspired design (BID) fosters innovations in engineering. Learning BID is crucial for developing multidisciplinary innovation skills of designers and engineers. Current BID education aims to enhance learners’ understanding and analogical reasoning skills. However, it often heavily relies on the teachers’ expertise. When learners pursue independent learning using some educational tools, they face challenges in understanding and reasoning practice within this multidisciplinary field. Additionally, evaluating their learning outcomes comprehensively becomes problematic. Addressing these challenges, we introduce a LLMs-driven BID education method based on a structured ontology and three strategies: enhancing understanding through LLMs-enpowered "learning by asking", assisting reasoning by providing hints and feedback, and assessing learning outcomes through benchmarking against existing BID cases. Implementing the method, we developed BIDTrainer, a BID education tool. User studies indicate that learners using BIDTrainer understood BID knowledge better, reason faster with higher interactivity than the baseline, and BIDTrainer assessed the learning outcomes consistent with experts.

Divergent thinking is a process in design by exploring multiple possible solutions, is crucial in the early stages of design to break fixation and expand the design ideation. Design-by-Analogy promotes divergent thinking, by studying solutions have solved similar problems and using this knowledge to make inferences and solve problems in new and unfamiliar situations. Bio-inspired design (BID) is a form of design by analogy and its knowledge provides diverse sources for analogy, making BID knowledge as a potential source for divergent thinking. Existing BID database has focused on collecting BID cases and facilitating the retrieval of biological knowledge. Despite its success, applying BID knowledge into divergent thinking still encounters challenge, as the association between source domain and target domain are always limited within a single case. In this work, a novel approach is proposed to support divergent thinking from three subsequent phases: encoding, retrieval and mapping. Specifically, biological knowledge is encoded in a triple form by employing a large language model (LLM) to extract key information from a well-known BID knowledge base. The created triples are implemented in a semantic network to facilitate bidirectional retrieval modes: problem-driven and solution-driven, as well as mapping for divergent thinking. The mapping algorithm calculates the semantic similarity between nodes in the semantic network based on their attributes in three progressive steps by following the paradigm of divergent thinking. The proposed approach is implemented as tool called AskNatureNet,1 which supports divergent thinking by retrieving and mapping knowledge in a visualized interactive semantic network. An ideation case study on evaluating the effectiveness of AskNatureNet shows that our tool is capable of supporting divergent thinking efficiently.