Ontology-Driven Adaptive Motivational Support For Sustained Engagement In Personal Development Systems

Digital applications play a central role in personal develop- ment and behavior change, yet sustaining long-term user engagement remains a persistent challenge. While gamification is widely employed to enhance motivation, many systems rely on static, one-size-fits-all imple- mentations that do not account for individual motivational differences or dynamic behavioral change over time. In addition, personalization approaches often emphasize trait-based profiling while underutilizing be- havioral metrics and context within a reusable, formally specified adap- tive architecture. This doctoral research proposes an ontology-driven adaptive motiva- tional framework that integrates user traits, behavioral metrics, and con- textual information to support dynamic personalization across domains such as physical exercise and cognitive training, including evaluation with older adult users. The framework models motivational mechanics, user characteristics, and behavior in a semantic structure intended to support adaptive decisions and facilitate reuse across application domains. Initial empirical investigations examine the distribution of motivational traits and gamification preferences, providing evidence to inform adapta- tion design. Expected contributions include (i) a reusable architecture for motivational adaptation, (ii) empirical insights into the relative contri- bution and limitations of trait-based and behavior-based personalization signals, and (iii) methodological guidance for combining trait-based and behavior-driven adaptation to support sustained engagement.