We present Canvas Conversation Tales (CCT), an edutainment web application for creating imaginary dialogues about paintings. To encourage participation, CCT employs techniques like introductory onboarding, possible story branching, expression of story preferences, and chat among users. This demo invites users to explore two usage scenarios of CCT, namely the development of a new story, and the creation of a new branch based on an existing story developed by others. We expect the first scenario to engage users open to express their creativity, and the second scenario - those willing to questions and discussions about the conversations of others.

In online Problem-Based Learning (PBL), being able to provide immediate feedback to learners is invaluable, yet difficult to achieve. We examine how well an off-the-shelf Natural Language Processing (NLP) framework is able to detect the absence of an identified responsible stakeholder in ideas generated during security training. Part-of-Speech Tagging and Dependency Parsing are applied on contextualised written learner contributions, collected from a PBL environment and compare the results to an assessment performed by experts. Using grammatical analysis, we aim to detect the absence of an identified responsible stakeholder in collected contributions ($n=1174$) from two security domains. Four heuristics are compared, resulting in a precision of ($PPV=0.929$) on the best of these, sufficient to provide immediate feedback to learners. Our results suggest that for the purposes of scaffolding open-ended PBL exercises, off-the-shelf NLP frameworks can achieve good performance on responsible stakeholder identification.

Educational chatbots come with a promise of interactive and personalized learning experiences, yet their development has been limited by the restricted free interaction capabilities of available platforms and the difficulty of encoding knowledge in a suitable format. Recent advances in language learning models with zero-shot learning capabilities, such as ChatGPT, suggest a new possibility for developing educational chatbots using a prompt-based approach. We present a case study with a simple system that enables mixed-turn interactions and discuss the insights and preliminary guidelines obtained from initial tests. We examine ChatGPT's ability to pursue natural educational conversations, adapt the educational activity to users' characteristics, such as culture, age, and level of education, and its ability to use diverse educational strategies and conversational styles. Although the results are encouraging, challenges are posed by the highly structured form of responses by ChatGPT, as well as their variability, which can lead to an unexpected switch of the chatbot's role from a teacher to a therapist. We provide some initial guidelines to address these issues and to facilitate the development of effective educational chatbots.