Train the trainer is a pedagogical model widely used across a range of disciplines and fields. Originating in non- governmental organisations, the model was developed in a bid to reduce costs associated with training while also increasing the capacity of staff to effectively disseminate knowledge within organisations. While its use is now widespread, there is little consensus in the literature regarding its efficacy. This paper describes a train-the-trainer programme that was designed to enhance early-career researchers’ ability to deliver training based on their research. The programme was delivered to a pilot group who then delivered training to external stakeholders on two occasions. The train-the-trainer programme was then evaluated from the perspectives of the trained early-career researchers along with the participants who subsequently took part in workshops delivered by this cohort. Results indicate that the programme was largely successful in improving the early-career researchers’ confidence with respect to training delivery, while also creating a positive learning experience for the participants. The paper concludes with a discussion around the future plans for the development of the train-the-trainer programme based on the results from this pilot implementation.

Abstract This paper illustrates the power of extending Language Driven Engineering (LDE) with Domain-Specific Natural Languages (DSNLs) through a case study on two levels. Both cases benefit from the characteristic decomposition feature of LDE, resulting in tasks tailored to the application of domain-specific languages, here with a focus on the application of DSNLs supported by LLM-based code generation. In the first case study, we show how DSNL-supported LDE facilitates the development of point-and-click adventures, whereas the second case study focuses on migration: We demonstrate how the entire LDE scenario for point-and-click adventure games can be migrated to output TypeScript instead of JavaScript using LLM-based code generation exclusively, without manually writing any code. This migration not only infers the required types, but also preserves an important property of the original LDE scenario: generated web applications can be automatically validated by design via automata learning and subsequent model checking. Even better, this property can be exploited to automatically validate the correctness of the migration by learning so-called difference automata that characterize the behavioral differences between the generated JavaScript-based and Type-Script-based applications.