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Human-designed systems are increasingly leveraged by data-driven methods and artificial intelligence. This leads to an urgent need for responsible design and ethical use. The goal of this conceptual paper is two-fold. First, we will introduce the Framework for Design Reasoning in Data Life-cycle Ethical Management, which integrates three existing frameworks: 1) the design reasoning quadrants framework (representing engineering design research), and 2) the data life-cycle model (representing data management), and 3) the reflexive principles framework (representing ethical decision-making). The integration of three critical components of the framework (design reasoning, data reasoning, and ethical reasoning) is accomplished by centering on the conscientious negotiation of design risks and benefits. Second, we will present an example of a student design project report to demonstrate how this framework guides educators towards delineating and integrating data reasoning, ethical reasoning, and design reasoning in settings where ethical issues (e.g., AI solutions) are commonly experienced. The framework can be implemented to design courses through design review conversations that seamlessly integrate ethical reasoning into the technical and data decision-making processes. 

Abstract BackgroundThis study posits that scaffolded team-based computational modeling and simulation projects can support model-based learning that can result in evidence of representational competence and regulatory skills. The study involved 116 students from a second-year thermodynamics undergraduate course organized into 24 teams, who worked on three two-week-long team-based computational modeling and simulation projects and reflected upon their experience. ResultsResults characterized different levels of engagement with computational model-based learning in the form of problem formulation and model planning, implementation and use of the computational model, evaluation, and interpretation of the outputs of the model, as well as reflection on the process. Results report on students’ levels of representational competence as related to the computational model, meaning-making of the underlying code of the computational model, graphical representations generated by the model, and explanations and interpretations of the output representations. Results also described regulatory skills as challenges and strategies related to programming skills, challenges and strategies related to meaning-making skills for understanding and connecting the science to the code and the results, and challenges and strategies related to process management mainly focused on project management skills. ConclusionCharacterizing dimensions of computational model-based reasoning provides insights that showcase students’ learning, benefits, and challenges when engaging in team-based computational modeling and simulation projects. This study also contributes to evidence-based scaffolding strategies that can support undergraduate students' engagement in the context of computational modeling and simulation.