| Summary: | Abstract Tasks in accounting textbooks play a vital role when it comes to learning processes. However, hardly any empirical evidence on the quality of accounting tasks exists regarding accounting-relevant characteristics. This is why a new category system containing accounting-relevant aspects was developed to analyze a total of 3,361 tasks from 14 different German accounting textbooks. Descriptive analysis and correlation analysis were performed to assess task characteristics and identify relationships between categories. In addition, in light of the large number of tasks to be analyzed, AI assisted the content analysis, and its usefulness was evaluated. The results indicate that tasks are not sufficiently able to instill accounting competencies such as interpreting data, assessing the relevance of information, or identifying and solving underlying accounting problems. The findings further show that AI and human coding yield similar results in most categories, suggesting that AI assistance is useful for content analysis when evaluating a large number of tasks.
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