ChatGPT is a generative Artificial Intelligence (AI) that can produce a variety of outputs, including solutions to problems. Prior research shows that for students to learn from instructional content, they need to actively process the content. To date, existing research has focused on student explanations expressed in words (either spoken or written). Thus, less is known about other forms of expression, such as ones involving spatial elements (eg, flowcharts, drawings). Moreover, to the best of our knowledge, there is not yet work on how students annotate solutions produced by ChatGPT. The study was conducted in the context of a first-year programming tutorial focused on loops and code tracing. Code tracing is a fundamental programming skill that involves simulating at a high level the actions a computer takes when it executes a program. The students annotated a printed-out code trace produced by ChatGPT using the strategy of their choice. Our goal was to describe the visual and verbal strategies students used to annotate the ChatGPT trace as well as how strategies relate to annotation quality, and so we used an observational study design with a single condition. Annotation strategies ranged from words-only strategies to visual representations like flowcharts. As annotation quality increased, the proportions of strategies used changed, suggesting that some strategies may facilitate the production of quality annotations. In particular, the proportion of words-only and flowchart strategies increased as quality increased; in the top quality quartile, there was a similar proportion of each but with slightly more flowcharts.