Writing Analytics focuses on the measurement and analysis of written texts for the purpose of understanding writing processes and products, in their educational contexts, and improving the teaching and learning of writing. This workshop adopts a critical, holistic perspective in which the definition of \"the system\" and \"success\" is not restricted to IR metrics such as precision and recall, but recognizes the many wider issues that aid or obstruct analytics adoption in educational settings, such as theoretical and pedagogical grounding, usability, user experience, stakeholder design engagement, practitioner development, organizational infrastructure, policy and ethics.

The aim of this narrative review is to understand how Artificial Intelligence (AI), specifically generative AI (e.g. ChatGPT) is thought to be used to support teachers in content authoring and curriculum production. Our findings indicate that Generative AI is envisioned as a co-design partner in enhancing educational content such as rubrics, lesson plans, interactive exercises, analogies, reflective questions, and case studies tailored to learning goals and instructional needs. The literature suggests that the key issues with the use of ChatGPT in education can be summarised mainly around its accuracy, and reliability, and its potential to lead to plagiarism. The analysis of empirical studies on the use of generative AI in higher education revealed first insights into themes such as ChatGPT's role in administrative tasks, varied perceptions of AI, generational differences in attitudes, multifunctional roles of ChatGPT, concerns about academic integrity and need for AI literacy, and the call for teaching philosophy reform, around assessment.

Generative AI is widely available and has raised the expectation that it will impact Education. Models, such as ChatGPT, can quickly produce plausible texts on a wide range of topics, and this capability may be of potential use in course content production. This paper selects several important course content production tasks, describes the prompts used, and assesses the quality of the automatically generated texts by a team of experts. Across all tasks, the Generative AI produced content that could help solve specific tasks by aiding with brainstorming, creating outlines, and adhering to particular writing guidance. In all cases, the content required adjustments and checking by human experts.

In Industry 5.0 it is essential that humans are in the loop of technology integration of industry processes. With the advancements of Generative Artificial Intelligence (GenAI), a lot of new opportunities and challenges for learning and teaching are present. Many students already use publicly available AI Digital Assistants (p-AIDA) like ChatGPT for academic purposes. However, there are concerns around the use of such p-AIDA tools, particularly in terms of academic integrity, data privacy, intellectual property, and the impact on the quality of education. Furthermore, many higher education institutions have substantial learning materials and data about students that they may not want to share with p-AIDA. Therefore, using the Technology Acceptance Model (TAM) and following a Design-Based Research (DBR) approach, we explored the perspectives and experiences of a beta-test of an institutionally developed AIDA (i-AIDA) with 18 UK students using multiple methods and data sources (including pre-post-test, interviews, think-aloud, and prompt analysis). Our research underscores the potential benefits and limitations of in-house i-AIDA in enhancing learning experiences without compromising academic integrity or privacy, and how higher education institutions can prepare themselves for Industry 5.0.