This paper introduces innovative software for efficient English language learning that incorporates machine learning and natural language processing techniques to personalize the vocabulary acquisition process for English language learners. The software was designed to enhance extensive reading by generating English language learning materials for learners based on their interests and proficiency levels. The software begins by administering a brief and straightforward vocabulary test to learners. The test is used to identify words that may be unknown to the learners from 12,000 words. The machine-learning algorithm identifies words that are most likely to be unknown to the learner based on their test performance. The software then prompts the learner to select a topic of interest such as science or music. Thereafter, the software generates personalized English language learning materials for the learner, which contain texts with specific vocabulary related to the selected topic. The material is generated using ChatGPT. The software highlights unknown words in the text, which the learner can check using a dictionary. The software then generates new material incorporating the unknown words that the learner has checked, ensuring that the learner is exposed to a wide vocabulary in his area of interest. This process is repeated multiple times with the software generating new materials and incorporating new words that the learner has checked, thereby facilitating the efficient acquisition of new vocabulary. Through this process, the learners can engage in extensive reading, enabling them to read more in English and develop their reading skills, while simultaneously acquiring new vocabulary related to their interests. The innovative approach of the software in English language learning offers a personalized, adaptive, and efficient approach to extensive reading. This can help learners improve their English proficiency by reading in a manner tailored to their interests and proficiency levels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Plan recognition is a key component of player modeling. Player plan recognition focuses on modeling how and when players select goals and formulate action sequences to achieve their goals during gameplay. By occasionally asking players to describe their plans, it is possible to devise robust plan recognition models that jointly reason about player goals and action sequences in coordination with player input. In this work, we present a player plan recognition framework that leverages data from player interactions with a planning support tool embedded in an educational game for middle school science education, CRYSTAL ISLAND. Players are prompted to use the planning tool to describe their goals and planned actions in CRYSTAL ISLAND. We use this data to devise data-driven player plan recognition models using multi-label multi-task learning. Specifically, we compare single-task and multi-task learning approaches for both goal prediction and action sequence prediction. Results indicate that multi-task learning yields significant benefits for action sequence prediction. Additionally, we find that incorporating automated detectors of plan completion in plan recognition models improves predictive performance in both tasks.

Knowledge Tracing (KT) models based on attention mechanisms have demonstrated in literature the capability to predict student performance more accurately than previous models in some datasets. However, they fail to directly infer student knowledge. In this paper, we apply a proposed extension already seen in KT literature in order to infer latent knowledge to these models. We apply the extension to four different attention-based KT models, to investigate whether these models can better infer the knowledge outside the learning system than previous models. We find that attention-based models can generate better knowledge estimate correlations with student's scores than the previous models.