Recent literature in automated creativity measurement explores the use of large language models to measure creative tasks at scale, for example using text-based large language models to score text-based brainstorming activities or neural nets to rate images from a creative drawing task. Prior research has shown that educators of all levels want to gauge and support students' ability to be creative, but are often limited due to the time-consuming nature of existing creativity measurements, as well as a disconnect between validated instruments and class-based activities. This paper expands creativity measurement in several important ways. We leverage state-of-the-art multimodal large language models (MLLMs), trained on text, image, and other data, to not only model creativity tasks in a unitask approach (one model per task), but also in a multitask approach (one model for several tasks). We connect multimodal large language models to benchmarks established in psychological creativity research, and demonstrate that some MLLMs (notably native multimodal Llama 3.2 and Qwen 3-VL models) surpass the best scoring measurements by up to 5%, while other MLLMs (notably Llama 4-109B) need additional training procedures to accommodate limited fine-tuning data. We offer evidence of the ability of MLLMs to measure creativity based on human ratings, and explore future opportunities to advance multimodal creativity assessment within complex, real-world learning environments.

Responsible AI (RAI) is the science and practice of ensuring the design, development, use, and oversight of AI are socially sustainable---benefiting diverse stakeholders while controlling the risks. Achieving this goal requires active engagement and participation from the broader public. This paper introduces "We are AI: Taking Control of Technology," a public education course that brings the topics of AI and RAI to the general audience in a peer-learning setting. We outline the goals behind the course's development, discuss the multi-year iterative process that shaped its creation, and summarize its content. We also discuss two offerings of "We are AI" to an active and engaged group of librarians and professional staff at New York University, highlighting successes and areas for improvement. The course materials, including a multilingual comic book series by the same name, are publicly available and can be used independently. By sharing our experience in creating and teaching "We are AI", we aim to introduce these resources to the community of AI educators, researchers, and practitioners, supporting their public education efforts.

Responsible AI (RAI) encompasses the science and practice of ensuring that AI design, development, and use are socially sustainable--—maximizing the benefits of technology while mitigating its risks. Industry practitioners play a crucial role in achieving the objectives of RAI, yet there is a persistent a shortage of consolidated educational resources and effective methods for teaching RAI to practitioners. In this paper, we present a stakeholder-first educational approach using interactive case studies to foster organizational and practitioner-level engagement and enhance learning about RAI. We detail our partnership with Meta, a global technology company, to co-develop and deliver RAI workshops to a diverse company audience. Assessment results show that participants found the workshops engaging and reported an improved understanding of RAI principles, along with increased motivation to apply them in their work.

This case study examines an interactive, low barrier process, termed instructor-in-the-loop, by which an instructor defines and makes meaning from exploratory metrics and visualizations, and uses this multimodal information to improve a course iteratively. We present potentials for course improvement based on automated learning analytics insights related to students’ participation in small active learning sessions associated with a large lecture course. Automated analytics processes are essential for larger courses where engaging smaller groups is important to ensure participation and understanding, but monitoring a large total number of groups throughout an instructional experience becomes untenable for the instructor. Of interest is providing instructors with easy-to-digest summaries of group performance that do not require complex set up and knowledge of more advanced algorithmic approaches. We explore synthesizing metrics and visualizations as ways to engage instructors in meaning making of complex learning environments, but in a low barrier manner that provides insights quickly.

This paper proposes an overarching framework for automated collaboration feedback that bridges theory and tool as well as technology and pedagogy. This pragmatic and theory-driven framework guides our thinking by outlining the components involved in converting theoretical collaboration constructs into features that can be automatically extracted and then converted into actionable feedback. Focusing on the pedagogical components of the framework, the constructs are validated by mapping them onto a selection of multi-disciplinary collaboration frameworks. The resulting behavioral indicators are then applied to measure collaboration in a sample scenario and those measurements are then used to exemplify how feedback analytics could be calculated. The paper concludes with a discussion on how those analytics could be converted into feedback for students and the next steps needed to advance the technological part of the framework.