This paper presents a comprehensive study on assisting new AI programmers in making responsible choices while programming. The research focused on developing a process model, incorporating design patterns, and utilizing an IDE-based extension to promote responsible Artificial Intelligence (AI) practices. The experiment evaluated the effectiveness of the process model and extension, specifically examining their impact on the ability to make responsible choices in AI programming. The results revealed that the use of the process model and extension significantly enhanced the programmers' understanding of Responsible AI principles and their ability to apply them in code development. These findings support existing literature highlighting the positive influence of process models and patterns on code development capabilities. The research further confirmed the importance of incorporating Responsible AI values, as asking relevant questions related to these values resulted in responsible AI practices. Furthermore, the study contributes to bridging the gap between theoretical knowledge and practical application by incorporating Responsible AI values into the centre stage of the process model. By doing so, the research not only addresses the existing literature gap, but also ensures the practical implementation of Responsible AI principles.
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Introduction: Depression can be a serious problem in young adult students. There is a need to implement and monitor prevention interventions for these students. Emotion-regulating improvisational music therapy (EIMT) was developed to prevent depression. The purpose of this study was to evaluate the feasibility of EIMT for use in practice for young adult students with depressive symptoms in a university context. Method: A process evaluation was conducted embedded in a larger research project. Eleven students, three music therapists and five referrers were interviewed. The music therapists also completed evaluation forms. Data were collected concerning client attendance, treatment integrity, musical components used to synchronise, and experiences with EIMT and referral. Results: Client attendance (90%) and treatment integrity were evaluated to be sufficient (therapist adherence 83%; competence 84%). The music therapists used mostly rhythm to synchronise (38 of 99 times). The students and music therapists reported that EIMT and its elements evoked changes in all emotion regulation components. The students reported that synchronisation elicited meaningful experiences of expressing joy, feeling heard, feeling joy and bodily responses of relaxation. The music therapists found the manual useful for applying EIMT. The student counsellors experienced EIMT as an appropriate way to support students due to its preventive character. Discussion: EIMT appears to be a feasible means of evoking changes in emotion regulation components in young adult students with depressive symptoms in a university context. More studies are needed to create a more nuanced and evidence-based understanding of the feasibility of EIMT, processes of change and treatment integrity.
While literature and practice acknowledge the potential of service innovation as well as digitally enabled innovation processes, the diverse innovation process literature lacks a process model which combines these two aspects. This systematic literature review aims at filling this gap by analysing innovation process theories and approaches with a specific focus on service and digital innovation. 25 conceptualisations of innovation processes were distilled and analysed in detail to present a ‘digital innovation process for services’ model which includes steps on three levels. Consequently, this literature review expands the current state-of-research and acts as the groundwork for further innovation research projects.
