Laboratories have played an integral part in science and engineering education for a long time. It makes sense that students have opportunities to put theoretical knowledge in a practical context. Another aspect of laboratory learning is to introduce student to aspects of professional practice, for example in relating to how tasks are completed in an engineering workplace.

The learning outcomes of laboratories have been much discussed, and there are several frameworks to support the analysis of their contribution to the educational objectives, such as the widely cited work of Feisal and Rosa (2005).

As the world is seeing immense change in the way that we interact with laboratory learning activities, and the capabilities of generative AI, it is timely to re-evaluate if our laboratory learning activities are still fit for purpose.

Previous work has shown that laboratory learning activities still have a place in a modern engineering curriculum (Kist et al, 2024).

When traditional laboratory learning activities were developed, it is likely that the learning objectives focused on the procedural nature of the activity. It was assumed that students learned things by completing the activities. As we move to virtual and remote activities, and as AI becomes more capable of performing procedural tasks, it is critical that we regularly review our approach.

In this paper, a framework will be introduced to assist educators in a review of existing laboratory activities, and the development of new laboratory learning activities, to ensure that the learning objectives focus on the real intent of the activity. This will include clearly identifying the purpose of the learning activities and also identifying where AI can be used within the activity.

The goal is to provide a tool to support regular review of learning activities to ensure that the integrity of the learning activities is maintained while fostering the requisite AI capabilities in students.