Crowd-powered conversational systems (CPCS) solicit the wisdom of crowds to quickly respond to on-demand users' needs. The very factors that make this a viable solution - -such as the availability of diverse crowd workers on-demand - - also lead to great challenges. The ever-changing pool of online workers powering conversations with individual users makes it particularly difficult to generate contextually consistent responses from a single user's standpoint. To tackle this, prior work has employed conversational facts extracted by workers to maintain a global memory, albeit with limited success. Through a controlled experiment, we explored if a conversational agent, dubbed ContextBot, can provide workers with the required context on the fly for successful completion of affective support tasks in CPCS, and explore the impact of ContextBot on the response quality of workers and their interaction experience. To this end, we recruited workers (N=351) from the Prolific crowd-sourcing platform and carried out a 3×3 factorial between-subjects study. Experimental conditions varied based on (i) whether or not context was elicited and informed by motivational interviewing techniques (MI-adherent guidance, general guidance, and no guidance), and (ii) different conversational entry points for workers to produce responses (early, middle, and late). Our findings show that: (a) workers who entered the conversation earliest were more likely to produce highly consistent responses after interacting with ContextBot; (b) showed better user experience after they interacted with ContextBot with a long chat history to surf; (c) produced more professional responses as endorsed by psychologists; (d) and that interacting with ContextBot through task completion did not negatively impact workers' cognitive load. Our findings shed light on the implications of building intelligent interfaces for scaffolding strategies to preserve consistency in dialogue in CPCS.

A multi-element and multi-phase internal oxidation model that couples thermodynamics with kinetics is developed to predict the internal oxidation behaviour of Fe–Mn–Cr steels as a function of annealing time and oxygen partial pressure. To validate the simulation results, selected Fe–Mn–Cr steels were annealed at 950 °C for 1–16 h in a gas mixture of Ar with 5 vol% H₂ and dew points of − 30, − 10 and 10 °C. The measured kinetics of internal oxidation as well as the concentration depth profiles of internal oxides in the annealed Fe–Mn–Cr steels are in agreement with the predictions. Internal MnO and MnCr₂O₄ are formed during annealing, and both two oxides have a relatively low solubility product. Local thermodynamic equilibrium is established in the internal oxidation zone of Fe–Mn–Cr steels during annealing and the internal oxidation kinetics are solely controlled by diffusion of oxygen. The internal oxidation of Fe–Mn–Cr steels follows the parabolic rate law. The parabolic rate constant increases with annealing dew point, but decreases with the concentration of the alloying elements.