In our field, programming education for non-computer science students (e.g., Business Informatics, Business Administration), we can observe high diversity among our students, for example, with respect to gender differences, cultural differences, differences with respect to age, educational background, or work experience. Introductory programming courses traditionally face high drop-out rates and poor performance and students often perceive learning to program as difficult. Current research on diversity in programming education has primarily focused on gender differences, thus neglecting the influence of other diversity dimensions on students’ performance. The proposed project aims to fill this gap by first identifying how heterogeneous groups of students can be best supported. Based on our findings we will develop a didactic concept with accompanying teaching and learning material to actively support different diversity dimensions in programming education. The concept will include competence models for measuring competences and as a result support individual learning paths. Support for distance learning and flipped classroom methods will be a fundamental part of our concept. To further support a flat learning curve in university programming courses in the future, we will closely work with schools and educational centers for teachers to integrate algorithmic thinking into school curricula. We will empirically evaluate our concept using a mixed method approach, combining both qualitative and quantitative methods for data collection and analysis. In particular, we will explore the effects of our concept in our programming course and will investigate the value of these effects as perceived by lecturers and students.