University resources are limited, and strategic admission management is required in certain fields that have high application volumes but limited available study places. Student admission processes need to select an appropriate number of applicants to ensure the optimal enrollment while avoiding over- or underenrollment. The traditional approach often relies on the enrollment yields from previous years, assuming fixed admission probabilities for all applicants and ignoring statistical uncertainty, which can lead to suboptimal decisions. In this study, we propose a novel machine-learning-based approach to improving student admission decisions. Trained on historical application data, this approach predicts the number of enrolled applicants conditionally based on the number of admitted applicants, incorporates the statistical uncertainty of these predictions, and derives the probability of the number of enrolled applicants being larger or smaller than the available study places. The application of this approach is illustrated using empirical application data from a German university. In this illustration, first, several machine learning models were trained and compared. The best model was selected. This was then applied to applicant data for the next year to estimate the individual enrollment probabilities, which were aggregated to predict the number of applicants enrolled and the probability of this number being larger or smaller than the available study places. When this approach was compared with the traditional approach using fixed enrollment yields, the results showed that the proposed approach enables data-driven adjustments to the number of admitted applicants, ensuring controlled risk of over- and underenrollment.

Artificial intelligence (AI) and digital technology provide extensive possibilities for education. But focusing only on their implementation does not address the challenges associated with them and they may even have a negative impact on learners. So far, the disciplines of psychology and computer science have not provided a theoretical framework for the competence needed to develop and use AI and digital technology in education in order to prepare learners for successful participation in modern societies. The main aim of this paper is to theoretically specify competent use of AI and digital technology in education and to provide a standardized instrument to evaluate courses that teach this competence. Therefore, we (a) combine theoretical contributions from both scientific disciplines to formulate a theoretical framework with four levels for the “Competence to Use Artificial Intelligence and Digital Technology in Educational Processes” (AIEDTEC competence), (b) introduce a questionnaire to evaluate courses that teach AIEDTEC competence, and (c) present results regarding its psychometric properties ($N=240$). The questionnaire showed good to very good psychometric properties and the assumed factor structure was supported by confirmatory factor analyses. The paper connects research on systems thinking and learning and instruction with recent developments regarding AI and digital technology and thereby provides an essential base for creating effective, modern, and safe learning environments in the future as well as a psychometric evaluation instrument.