AI systems increasingly support human decision-making across domains of professional, skill-based, and personal activity. While previous work has examined how AI might affect human autonomy globally, the effects of AI on domain-specific autonomy—the capacity for self-governed action within defined realms of skill or expertise—remain understudied. We analyze how AI decision-support systems affect two key components of domain-specific autonomy: skilled competence (the ability to make informed judgments within one's domain) and authentic value-formation (the capacity to form genuine domain-relevant values and preferences). By engaging with prior investigations and analyzing empirical cases across medical, financial, and educational domains, we demonstrate how the absence of reliable failure indicators and the potential for unconscious value shifts can erode domain-specific autonomy both immediately and over time. We then develop a constructive framework for autonomy-preserving AI support systems. We propose specific socio-technical design patterns—including careful role specification, implementation of defeater mechanisms, and support for reflective practice—that can help maintain domain-specific autonomy while leveraging AI capabilities. This framework provides concrete guidance for developing AI systems that enhance rather than diminish human agency within specialized domains of action.

This paper proposes and reflects on a teaching methodology for introducing relational ethics in the engineering curriculum based on a pilot at TU Delft in the Netherlands in a course for robotics engineers. Differently from prevalent models of ethics courses, we shifted from having students apply ethical theories to technologies to having them reflect on different aspects of human-robot relations from a more-than-human perspective. This redesign was prompted by conceptual and practical motivations related to (1) a lack of methodological examples of relational ethics in engineering ethics education, (2) a call for more experiential education, and (3) a push to re-evaluate course assessment due to Generative AI. Aside from the lecture content, students explored various dimensions of relational ethics in a thinking-through-doing manner by crafting a companion robot in the tutorials. This culminated in an individual essay in which students reflected on the question ‘How to live well with robots?,’ reflecting on their developing relations with their robotic companion and supported by visual evidence of their human-robot interactions throughout the course. Finally, we provide reflections on this experimental course redesign, outlining several considerations for those intending to integrate relational ethics into their curricula and suggesting avenues for further work.

Integrating AI systems into workflows risks undermining the competence of the people supported by them, specifically due to a loss of meta-cognitive competence. We discuss a recent suggestion to mitigate this through better uncertainty quantification. While this is certainly a step in the right direction, there is a question whether users are sufficiently supported to engage in critical reflection with literacy and tools alone. We therefore suggest that socio-technical system design focused on the role of AI systems is crucial to preserving autonomy, even when supported by uncertainty quantification.