The field of education is seeing a significant shift with the integration of Artificial Intelligence (AI) and Large Language Models (LLMs) in various aspects of learning. One of the key areas of focus is the use of AI-powered tools to support student learning, particularly in subjects such as programming and data science. Researchers are exploring the potential of LLMs to automate tasks, provide personalized feedback, and enhance student engagement. However, there are also concerns about the potential risks of AI-assisted cheating and the need for effective strategies to promote productive use of LLMs. Noteworthy papers include:

• Integrating Generative AI in BIM Education: Insights from Classroom Implementation, which highlights the challenges and opportunities of using GenAI in education.
• Enhancing Student Learning with LLM-Generated Retrieval Practice Questions: An Empirical Study in Data Science Courses, which demonstrates the effectiveness of LLM-generated questions in improving student learning outcomes.
• Assessing the Prevalence of AI-assisted Cheating in Programming Courses: A Pilot Study, which raises important questions about the prevalence of AI-assisted cheating and the need for effective detection methods.