The ‘keyword method’ is an effective technique for learning vocabulary of a foreign language. It involves creating a memorable visual link between what a word means and what its pronunciation in a foreign language sounds like in the learner’s native language. However, these memorable visual links remain implicit in the people’s mind and are not easy to remember for a large number of words. To enhance the memorisation and recall of the vocabulary, we developed an application that combines the keyword method with text-to-image generators to externalise the memorable visual links into visuals. These visuals represent additional stimuli during the memorisation process. To explore the effectiveness of this approach we first run a pilot study to investigate how difficult it is to externalise the descriptions of mental visualisations of memorable links, by asking participants to write them down. We used these descriptions as prompts for text-to-image generator (DALL-E 2) to convert them into images and asked participants to select their favourites. Next, we compared different text-to-image generators (DALL-E 2, Midjourney, Stable and Latent Diffusion) to evaluate the perceived quality of the generated images by each. Despite heterogeneous results, participants mostly preferred images generated by DALL-E 2, which was used also for the final study. In this study, we investigated whether providing such images enhances the retention of vocabulary being learned, compared to the keyword method alone. Our results indicate that people did not encounter difficulties describing their visualisations of memorable links and that providing corresponding images significantly increases memory retention.

Augmented Reality (AR) is a growing technology that enables interaction with both virtual and real objects. However, in order to support the future development of efficient and usable AR interactions, there is still a lack of systematic knowledge establishing basic interaction performance across different conditions. Therefore, in this paper, we report a user study measuring the impact of objects' physicalization (object's set composed of (i) virtual, (ii) real, or (iii) a composite mix of real and virtual objects) and hand appearance (hand's appearance displayed as (i) the real hand, (ii) an avatar, or (iii) dynamically adapting to the surrounding objects' physicalization) on the speed performance of a pick-and-place task. Overall, our results reveal that objects' physicalization plays a significant role in interaction performance, with the more real objects in a set the better the performance. Moreover, our results also suggest that pick-and-place interaction performances are mostly unaffected by the hand appearance. Interestingly, we also observed that interactions with real objects were less efficient as the object condition required the user to alternate between interactions with virtual and real objects (object condition (iii)), which provides novel insights into an important - mostly AR-specific - factor to consider for designing future AR interactions. Taken together, our results provide a rich characterization of different factors influencing different phases of a pick-and-place interaction, which could be employed to improve the design of future AR applications.