Towards Automated Planning of Level Structures for Digital Interventions

As global populations continue to age and chronic health conditions rise, the demand for scalable, effective rehabilitation interventions is increasing. Digital interventions offer a cost-effective solution for promoting healthy behavior change and self-management. However, traditional methods of handcrafting structured intervention content are time-inefficient, and not scalable. We propose a new approach to content structuring by modeling it as an automated planning problem. This work introduces the Automated Planning of LEvel Systems (APLES) tool, which leverages automated planning to generate structured intervention content within predefined constraints, balancing automation and designer control. APLES addresses the limitations of existing solutions such as Procedural Content Generation (PCG) systems, which often limit creative input. APLES allows designers to set rules that guide the automated processes. In this work-in-progress paper, we describe the technical progress of the APLES tool, including its planning framework and practical application in digital health interventions. Preliminary results demonstrate the tool’s potential to improve efficiency and scalability in content structuring while maintaining alignment with creative goals. In future work, we will evaluate APLES across multiple digital interventions to assess its impact on user engagement compared to structures handcrafted by campaign managers, and its perceived usefulness by healthcare providers.