In a Socratic dialogue, a teacher engages a student in a series of questions to prompt critical thinking, guiding the student to clarify and elaborate on their own view and to consider alternative perspectives. In this experience report, we analyse the suitability of large language models (LLMs) as automated Socratic tutors. Postgraduate computer science students from two universities and multiple courses held one-to-one interactive dialogues with LLMs on professional ethics and technology ethics scenarios. We found that the LLMs' questions were diverse and broadly scaffolded critical thinking. Students were able to demonstrate most of the cognitive levels of Bloom's Taxonomy in their answers. However, we also observed some significant limitations of LLMs as Socratic tutors, including unfounded praise of poor responses and reformulating answers for students rather than guiding students to do this themselves. Further, although many students interacted effectively with the LLM, others did not engage authentically with the process. These limitations can potentially be mitigated via prompt engineering and activity framing. Our work contributes an LLM-supported learning activity and our lessons learned on using current state-of-the-art LLMs as Socratic tutors for teaching ethics in computing. In addition, our codebooks provide a set of criteria to drive the evaluation of LLMs used for similar activities.

Cyber security is becoming more complex due to the exponential growth of interconnected systems and the global threat landscape. To mitigate those risks, there is a need for a skilled cyber security workforce that can navigate the complex decision-making in rapidly evolving cyberspace. Artificial intelligence (AI) is rapidly adopted into cyber defence operations, but we can not effectively train human and AI-assisted cyber defence operators without understanding the underlying learning theory and eco-systems. Cyber security exercises (CSXs) are popular teaching methods for cyber-readiness. However, applying learning analytics (LA) methods and AI-based approaches to exercise design and implementation is still in the early stages. We propose a holistic human-AI interaction model within the LA and CSX context. The model brings together elements and processes of human-AI interactions, as well as cyber ranges, cyber security, and LA tools, and a wider lens of multimodal learning analytics, exercise life-cycle, and overall pedagogical approach. We also discuss the opportunities and challenges for LA and AI in the context of cyber security training. We analyse the role of AI from the learning, instruction, and administration lens in cyber security training, specifically in the exercises. We aim to stimulate further discussions on the future of human-AI collaboration and how to enhance cyber security training with novel LA and AI capabilities.