The increasing need for competent computing graduates proficient in programming, software development, and related technical competencies [Ca17] is one of the factors exacerbating pressure on higher education institutions to offer high quality, competency-based education [Ra21]. However, the latter requires extensive resources, mentoring, and, for example, formative feedback for learners, especially in introductory programming classes [Je22; Lo24]. This is due to the fact that novices experience a number of challenges in the process, which have been subject to extensive research in the past decades [Du86; Lu18; SS86]. Among them are cognitively demanding competencies [Ki20; Ki24], such as problem understanding, designing and writing algorithms, debugging, and understanding error messages [Du86; ER16; Ki20; Lu18; SS86]). Educators' expectations towards novice learners and what they can achieve in their first semester(s) seem to be too high and unrealistic [Lu16; Lu18; WCL07]. Moreover, the student-educator ratio in introductory programming classes keeps increasing in German higher education institutions, thereby limiting resources to provide feedback and hints, and adequately address heterogeneous prior knowledge and diverse educational biographies [Pe16; SB22].

The advent of Large Language Models (LLMs), such as OpenAI's ChatGPT, Codex, and GitHub's Copilot, affects the educational landscape at its core, as LLMs offer entirely new possibilities, but also challenges for educators, learners, and institutions. Even though LLMs have only appeared very recently to a broader audience, research has started to address their implications on computing education, particularly programming. The generative potential may be used by educators for the design of new programming tasks [Sa22], or for students to gather formative feedback [Ka23, Zh22]. At the same time, implications for programming pedagogy and assessments are being discussed [Be23, BK23, RTT23], as the lowthreshold availability of LLMs raises new questions with regard to adequate task designs, students' contribution, plagiarism, and ethical conduct. Educators and institutions will soon need to reconsider the design of (formative) assessments. In this context, it is crucial to investigate the capabilities and limitations of LLMs for novice learners of programming, whose challenges have a well-documented history [SS86, Mc01, Lu18].