Two critical challenges in the design and synthesis of molecular robots are modularity and algorithm simplicity. We demonstrate three modular building blocks for a DNA robot that performs cargo sorting at the molecular level. A simple algorithm encoding recognition between cargos and their destinations allows for a simple robot design: a single-stranded DNA with one leg and two foot domains for walking, and one arm and one hand domain for picking up and dropping off cargos. The robot explores a two-dimensional testing ground on the surface of DNA origami, picks up multiple cargos of two types that are initially at unordered locations, and delivers them to specified destinations until all molecules are sorted into two distinct piles. The robot is designed to perform a random walk without any energy supply. Exploiting this feature, a single robot can repeatedly sort multiple cargos. Localization on DNA origami allows for distinct cargo-sorting tasks to take place simultaneously in one test tube or for multiple robots to collectively perform the same task.

We explore how crowdworkers can be trained to tackle complex crowdsourcing tasks. We are particularly interested in training novice workers to perform well on solving tasks in situations where the space of strategies is large and workers need to discover and try different strategies to be successful. In a first experiment, we perform a comparison of five different training strategies. For complex web search challenges, we show that providing expert examples is an effective form of training, surpassing other forms of training in nearly all measures of interest. However, such training relies on access to domain expertise, which may be expensive or lacking. Therefore, in a second experiment we study the feasibility of training workers in the absence of domain expertise. We show that having workers validate the work of their peer workers can be even more effective than having them review expert examples if we only present solutions filtered by a threshold length. The results suggest that crowdsourced solutions of peer workers may be harnessed in an automated training pipeline.

The public release and surprising capacity of ChatGPT has brought AI-enabled text generation into the forefront for educators and academics. ChatGPT and similar text generation tools raise numerous questions for educational practitioners, policymakers, and researchers. We begin by first describing what large language models are and how they function, and then situate them in the history of technology’s complex interrelationship with literacy, cognition, and education. Finally, we discuss implications for the field.

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