We live in times of rapid change, where unprecedented and unexpected events unfold with increasing speed and complexity, often disrupting familiar patterns and norms. In the midst of this uncertainty, AI plays a growing role, shaping how we interact, work, and live. To navigate such ambiguity, humans rely on sensemaking —a process of interpreting the unknown, settling on plausible explanations, and adapting their actions accordingly. Despite AI’s growing influence on nearly every aspect of life, studies repeatedly show that users often have a limited understanding of it, leading to misconceptions and unrealistic expectations. This lack of understanding not only results in frustration when AI systems fail to meet users’ needs but also hinders effective interaction and collaboration with these technologies. This dissertation views AI as a socio-technical umbrella term to explore how users make sense of it across three application domains: AI-assisted decisionmaking, AI-mediated social platforms, and agentic AI technologies. Guided by three research questions, it focuses on (1) how users make sense of AI in everyday encounters, (2) the empowerment needs that arise from these interactions, and (3) how design can support sensemaking and foster user agency. Recognizing the importance of context and the situated nature of sensemaking, this work combines various qualitative methods, such as semi-structured interviews, role-playing workshops, and experience sampling. Building on Weick’s sensemaking framework, the findings reveal that sensemaking of AI shares familiar characteristics—it’s enactive, driven by plausibility, inherently social, and often triggered by unexpected events. But AI sensemaking also has unique aspects, such as the influence of AI "folk concepts"—users’ assumptions and expectations about AI shaped by cultural narratives and societal definitions. These perceptions are also influenced by human traits like trust and intelligence and vary depending on stakeholder roles. AI sensemaking not only guides users’ actions but also informs counteractions—how people push back against or adapt to algorithmic systems. To empower users, this research takes a holistic approach, exploring their needs along three dimensions: feeling, knowing, and doing. It highlights how users’ sensemaking is influenced by both episodic power—arising from immediate, specific interactions—and systemic power, which operates at a structural level through institutional norms and opaque algorithmic designs. For designing AI systems that better support users’ sensemaking, the dissertation emphasizes two critical aspects. First, it frames sensemaking as an interactional element, suggesting that AI systems should foster continuous, context-specific engagement by helping users build competencies. Second, it stresses the importance of diversifying user participation in the design process. Involving users as co-creators and knowledge-makers empowers them to engage not just in sensemaking, but also in sense-unmaking, sense-giving, and sense-breaking—creating a more collaborative and inclusive design process that addresses both individual needs and systemic challenges. Finally, this dissertation argues for moving beyond Weick’s retrospective sensemaking framework to apply the concept of prospective sensemaking in the context of AI. This approach emphasizes designing systems that not only help users make sense of past interactions but also enable them to anticipate, adapt to, and shape the uncertainties inherent in their ongoing and future engagements with AI technologies.

This dissertation investigates the impact of job preemptions and restarts in online machine scheduling, with a focus on three fundamental objectives. An online scheduling algorithm determines whether to process a job or not without knowing the future events. One important extension to the classical online scheduling framework is the use of restarts. Allowing restarts means that the processing of a job may be interrupted, but in this case the interrupted job loses all its previous progress and must be started again later, until it is completed without interruptions. This model also known as preemption with restarts. Although restarts can potentially be very beneficial in the context of online scheduling, there has been relatively little research on this topic up until now. First, we study the problem of minimizing the total completion time on a single machine. We present a simple deterministic online algorithm that achieves a competitive ratio better than 1.4568, improving upon the previous best bound of 3/2. The algorithm follows an increasing-size processing order and interrupts a running job only when doing so reduces the completion time of an arriving job by more than a factor of 1.4568 compared to processing it immediately. Second, we extend the results to the minimization of total weighted completion time. For the case of equal processing times, we design a deterministic online algorithm that achieves a competitive ratio better than 1.325 based on carefully structured decision intervals. The algorithm uses at most three restarts for the entire schedule and redefines these decision intervals in certain situations to avoid further interruptions. Finally, we consider the weighted makespan minimization problem. This problem is generalization of the classical makespan minimization problem. We present a deterministic online algorithm that achieves a competitive ratio better than 1.3098 in the case where all jobs have equal processing times.

Objective and theory. This cumulative dissertation examines the subjectively perceived satisfaction of basic psychological needs among adolescents (aged 13 to 18) in the life areas of family, school and social media in different cultural contexts. The work is based on Grawe's consistency theory, which is complemented by perspectives from developmental and needs-based psychology as well as cultural studies. Methodology. Methodologically, three (cultural) comparative cross-sectional studies were conducted using standardised questionnaires: Study 1 (N = 214; Germany/Peru) used the GBKJ-SB, Studies 2 (N = 1.317; Germany/Italy) and 3 (N = 155; comprehensive vs. clinic schools) used the further developed MYNEEDZ questionnaire. Psychometric testing has shown high reliability (α/Ω > .80) and discriminatory power; a limited CFA fit (RMSEA/SRMR acceptable, CFI/TLI reduced) indicates systemic interdependencies in young people's life areas. The statistical analysis includes t-tests, Mann-Whitney U and Kruskal-Wallis tests, MANOVA/ANCOVA and GLM procedures. Results. Adolescents living in Germany report higher values in terms of satisfaction of the basic need for orientation/control than those in Peru, while girls living in Peru report a higher self-esteem. Adolescents in Italy show higher values in term of subjective psychological satisfaction of basic needs in family and school. In Germany girls report the lowest values of basic psychological need satisfaction in relation to school, while special needs students report higher values than secondary school students. Young people at hospital schools experience higher subjective psychological need satisfaction in social media than comprehensive school students, while retrospective assessments of their regular school are significantly lower than those of the hospital school. Interpretation/implication. The results demonstrate the role of cultural norms, institutional protective factors and digital contexts in satisfying basic psychological needs from the perspective of adolescents. The dissertation thus contributes to theoretical modelling of adolescents subjective perception of the satisfaction of basic psychological needs, to the methodological development of measuring this construct through the MYNEEDZ questionnaire, and to the deduction of design principles for education-related prevention, intervention and support systems.

Both first-principles-based and data-driven methods provide powerful tools for modeling physical systems. As such, there is a strong push for harnessing both approaches by combining them in the form of gray-box models. Automatic differentiation of ordinary differential equation (ODE) models and physical model parts enables flexible gray-box modeling in system identification tasks. Local model networks (LMNs) with their excellent interpretability and sparing use of model parameters are an ideal model class to provide data-driven elements in a gray-box model. Unfortunately, LMNs’ intricate training procedures currently do not allow for end-to-end training within larger model architectures. To remedy this, we propose a new LMN training algorithm and corresponding model called rectified linear unit model tree (ReLUMoT). ReLUMoT provides fully gradient-based training of LMNs, which permits us to train within gray-box ODE models. In essence, ReLUMoT pioneers the use of piecewise linear neural networks (PLNNs) as a training engine for LMNs. This is accomplished by distilling the locally linear structure of a PLNN into an LMN. We thoroughly review all theoretical concepts needed to train gray-box ODE models, ranging from state space modeling to initializing latent states during the training procedure. In particular, we give a critical review of sensitivity analysis methods, discussing their suitability for system identification tasks. We provide extensive experimental validation of ReLUMoT’s usefulness in practical modeling tasks. ReLUMoT is benchmarked against a variety of state-of-the-art modeling algorithms in learning both static and dynamic processes. In black-box settings, ReLUMoT matches or exceeds the performance of existing LMNs. Properly incorporating prior knowledge is shown to lead to vast improvements in model performance and size.