This paper presents HapticMap and Tactile Garden, an art installation with a demo contribution for IDC 2026 that invites participants to explore school-ground textures through vibrotactile interaction, projected drawings, and tactile surfaces. HapticMap features a touchscreen map of a real school site with a vibrotactile stylus and close-up images of bark, leaves, water and other natural materials. Tactile Garden extends this into a table-based installation built from children’s hand-drawn field sketches, projected onto translucent panels that deliver haptic-audio feedback on touch. The demo and table installation is grounded in a three-session study with 11 secondary school students in Edinburgh, in which outdoor sketching, guided haptic exploration and a return to school grounds was used to foreground touch as a mode of attention. Instead of attempting overly realistic representation of the textures, the system utilises a gap between digital and physical touch as a prompt for comparison, description and discussion. We present this installation as a contribution to IDC discussions around sensory design, place-based learning and ways in which interactive technologies can enable children and adults to notice everyday environments differently.

This paper explores the use of Generative AI and ChatGPT for improving the production of distance learning materials. We have conducted participatory design workshops involving cross-disciplinary teams of academics and different stakeholders, such as industry partners and learning material designers that are involved in course production at the UK Open University. The main outcome of these workshops is a set of use cases on how Generative AI tools, such as ChatGPT, can be integrated and augment the existing course production and delivery processes to make them more agile and efficient. Following the workshops, we have developed a proof-of-concept tool that can instantly generate introductions and summaries of course material, automatically generate quizzes and tests, as well as automatically identify, categorise, and transform learning activities. A preliminary evaluation performed with members of the course production teams indicated that in 40% of cases AI generated text of 250-500 words is suitable for use in distance learning materials. Overall, the evaluation participants welcome this use of Generative AI, but there are concerns mainly centred on potential for bias, misinformation, and copyright infringement in the generated learning materials.

A number of initiatives invite members of the public to perform online classification tasks such as identifying objects in images. These tasks are crucial to numerous large-scale Citizen Science projects in different disciplines, with volunteers using their knowledge and online support tools to, for example, identify species of wildlife or classify galaxies by their shapes. However, for complex classification tasks, such as this case study on identifying species of bumblebee, reaching an agreement between volunteers - or even between experts~-~may require consensus-building processes. Collaboration and teamwork approaches to problem solving and decision-making have been widely documented to improve both task performance and user learning in the real world. Most of these processes and projects are mediated online through feedback delivered in an asynchronous manner, and this article thus addresses a central research question: How do participants involved in species identification tasks respond to different forms of feedback provided in online collaboration, designed to support peer-learning and improve task performance? We tested four different approaches to feedback within a collaboration task, where participants reviewed their previously annotated data based on information curated from their peers on a long running online citizen science initiative. The selected interfaces have a strong foundation in social science and psychology literature and can be applied to citizen science practices as well as other online communities. Results showed that while all four approaches increased accuracy, there were differences based on the types of consensus that existed before collaboration. Such differences highlight the usefulness of different forms of feedback during collaboration for increasing data accuracy of identification and furthering users' expertise on identification tasks. We found that anonymised and goal-directed free text comments posted on social learning interfaces were most effective in improving data accuracy as well as creating opportunities for peer-learning, particularly where the species identification task was more difficult. This study has significant implications for extending the practice of citizen science across formal and informal learning environments and reaching out to a variety of users.

Abstract We report on an in‐depth corpus linguistic study on ‘multiple views’ terminology and word collocation. We take a broad interpretation of these terms, and explore the meaning and diversity of their use in visualisation literature. First we explore senses of the term ‘multiple views’ (e.g., ‘multiple views’ can mean juxtaposition, many viewport projections or several alternative opinions). Second, we investigate term popularity and frequency of occurrences, investigating usage of ‘multiple’ and ‘view’ (e.g., multiple views, multiple visualisations, multiple sets). Third, we investigate word collocations and terms that have a similar sense (e.g., multiple views, side‐by‐side, small multiples). We built and used several corpora, including a 6‐million‐word corpus of all IEEE Visualisation conference articles published in IEEE Transactions on Visualisation and Computer Graphics 2012 to 2017. We draw on our substantial experience from early work in coordinated and multiple views, and with collocation analysis develop several lists of terms. This research provides insight into term use, a reference for novice and expert authors in visualisation, and contributes a taxonomy of ‘multiple view’ terms.

We present an incremental Bayesian model that resolves key issues of crowd size and data quality for consensus labeling. We evaluate our method using data collected from a real-world citizen science program, B ee W atch , which invites members of the public in the United Kingdom to classify (label) photographs of bumblebees as one of 22 possible species. The biological recording domain poses two key and hitherto unaddressed challenges for consensus models of crowdsourcing: (1) the large number of potential species makes classification difficult, and (2) this is compounded by limited crowd availability, stemming from both the inherent difficulty of the task and the lack of relevant skills among the general public. We demonstrate that consensus labels can be reliably found in such circumstances with very small crowd sizes of around three to five users (i.e., through group sourcing). Our incremental Bayesian model, which minimizes crowd size by re-evaluating the quality of the consensus label following each species identification solicited from the crowd, is competitive with a Bayesian approach that uses a larger but fixed crowd size and outperforms majority voting. These results have important ecological applicability: biological recording programs such as B ee W atch can sustain themselves when resources such as taxonomic experts to confirm identifications by photo submitters are scarce (as is typically the case), and feedback can be provided to submitters in a timely fashion. More generally, our model provides benefits to any crowdsourced consensus labeling task where there is a cost (financial or otherwise) associated with soliciting a label.