This paper explores the integration of Generative AI (GenAI) into software development education, focusing on assessments that evaluate both the coding capabilities and ethical implications of tools like ChatGPT. The authors, from Swansea University and The Open University, conducted surveys and experimental assessments in their Degree Apprenticeship Programme to understand how learners and industry professionals perceive and use GenAI. Key findings include: 1. **Learners' Perceptions**: Most learners found ChatGPT-generated code to be efficient and well-styled but noted its lack of common sense and adherence to coding conventions. 2. **Industry Interest**: While only a few respondents reported integrating GenAI into their workplace, a significant majority saw potential benefits in improving work processes. 3. **Assessment Results**: Formative assessments showed that learners could generate compilable Java code with ChatGPT, but struggled with generating compilable Pep/8 Assembly code. Learners also critiqued the text-based arguments generated by ChatGPT, finding them vague and one-sided. 4. **Ethical and Professional Concerns**: Learners expressed concerns about privacy and ethical considerations when using GenAI in the workplace. The authors conclude that incorporating GenAI into assessments can help students understand its capabilities and limitations, and they plan to further integrate GenAI into more modules to prepare learners for an evolving technology landscape.This paper explores the integration of Generative AI (GenAI) into software development education, focusing on assessments that evaluate both the coding capabilities and ethical implications of tools like ChatGPT. The authors, from Swansea University and The Open University, conducted surveys and experimental assessments in their Degree Apprenticeship Programme to understand how learners and industry professionals perceive and use GenAI. Key findings include: 1. **Learners' Perceptions**: Most learners found ChatGPT-generated code to be efficient and well-styled but noted its lack of common sense and adherence to coding conventions. 2. **Industry Interest**: While only a few respondents reported integrating GenAI into their workplace, a significant majority saw potential benefits in improving work processes. 3. **Assessment Results**: Formative assessments showed that learners could generate compilable Java code with ChatGPT, but struggled with generating compilable Pep/8 Assembly code. Learners also critiqued the text-based arguments generated by ChatGPT, finding them vague and one-sided. 4. **Ethical and Professional Concerns**: Learners expressed concerns about privacy and ethical considerations when using GenAI in the workplace. The authors conclude that incorporating GenAI into assessments can help students understand its capabilities and limitations, and they plan to further integrate GenAI into more modules to prepare learners for an evolving technology landscape.