Everyday life is increasingly permeated by interactions between humans and autonomous agents. One example is electric vehicle (EV) drivers using smart charging agents (SCA) based on automated information processing to manage limited interdependent resources (e.g., availability of electrical energy, charging times). These agents must not only manage resources effectively, but also ensure meaningful participation in charging decisions, thereby supporting user autonomy – a fundamental psychological need according to self-determination theory. Understanding how autonomy is maintained or diminished in these interactions is a key cornerstone for optimizing the balance between automation and human decision making in shared resource contexts.

Using a 2x3 mixed-subjects design and a vignette-based approach, N = 70 participants (planned sample size) with EV experience engaged with scenarios simulating interaction with an SCA that calculated charging stops for a long-distance trip. As independent variables, we first varied user autonomy (no autonomy, medium and high autonomy) by giving participants different levels of control over the resulting trip plan. Second, we varied the amount of information (low, high) provided to explain the agent's trip planning decisions. As dependent variables, we assessed participants' perceived autonomy need satisfaction as well as user experience variables (i.e., perceived agent cooperativeness, teaming perception, subjective information processing awareness). We hypothesize that user experience and need satisfaction will be higher with more user autonomy and information provided. The results can be used for human-centered design of SCA interactions aiming at meaningful interactions.