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Academic misconduct poses a growing challenge for higher education institutions worldwide. While AI presents valuable opportunities for learning enhancement, Unauthorized Content Generation (UCG) poses a significant threat to academic integrity. This paper addresses the challenges posed by UCG and explores innovative approaches to detection, focusing on the underutilised concept of authorship verification (AV). Despite the recognition of AV’s potential, its application in education has been limited. This study investigates the feasibility of utilising students’ academic writing profiles for AV to detect contract cheating and unacknowledged AI usage in academic contexts. Building upon previous research, this study enhances the existing Feature Vector Difference (FVD) AV method by introducing improvements to support better analysis, explainability, and interpretability of the classification process in an educational context. The refined classifier provides probability-based outputs, offering a transparent alternative to traditional “black box” binary outputs, and is able to identify stylometric features suitable for differentiating student’s writing profiles. Through this research, we contribute to the advancement of AV technology in education towards explainability, providing educators with a valuable tool to uphold academic integrity and combat the proliferation of UCG in educational environments.

Motivational goal modelling has evolved from agent-oriented models to allow a shared understanding of a project by diverse stakeholders. Building a motivational model is in the spirit of user-centred design. Requirements artefacts such as user stories and personas should be developed consistently with the model. This paper describes a method to generate user stories from motivational models. The generated stories are checked by users and developers to ensure readabilty and clarity. The method has been partially automated within an extension to an editing tool.

CONTEXT The ongoing coronavirus pandemic required us to quickly adapt and familiarise ourselves with new skills and technologies in the shift to online teaching. Irregular communication due to extended lockdowns has meant that while knowledge on effective online teaching has been developed, this knowledge has not been properly disseminated to our junior teaching staff. As they operate predominantly in student-facing positions, it is essential that our junior staff be equipped with information on best practice in online teaching as well as with an awareness of the resources available to support them. PURPOSE OR GOAL To address the gap outlined above, we developed a new professional development program for our junior teaching staff, focusing mainly on online teaching. The goal was to share our collective knowledge on best practice in online teaching, and to demonstrate how various technologies could aid in promoting active learning in an online setting. The program also aimed to initiate a community of practice around teaching and the online teaching space. APPROACH OR METHODOLOGY/METHODS In designing our program, we considered student feedback from previous semesters, and more recent feedback on the online teaching experience from 2020. The final program covered the following topics: general advice, navigating Zoom and physical setup for online teaching, online tools for active learning, engagement within teaching teams, online feedback, and blended synchronous learning. Tools and technologies showcased in the program were embedded in the delivery to allow first-hand experience. ACTUAL OR ANTICIPATED OUTCOMES An exit survey indicated that in general, participants found the program useful, with an average rating of 8.27 (out of 10). The top areas that participants indicated that they would like more assistance were quizzes and tools for active learning (31%), providing feedback to students (22%), and blended synchronous learning (20%). Zoom (12%) and the physical setu