Advances in large language models (LLMs) enable many new innovations in education.However, evaluating the effectiveness of new technology requires real students, which is timeconsuming and hard to scale up.Therefore, many recent works on LLM-powered tutoring solutions have used simulated students for both training and evaluation, often via simple prompting.Surprisingly, little work has been done to ensure or even measure the quality of simulated students.In this work, we formally define the student simulation task, propose a set of evaluation metrics that span linguistic, behavioral, and cognitive aspects, and benchmark a wide range of student simulation methods on these metrics.We experiment on a realworld math tutoring dialogue dataset, where both automated and human evaluation results show that prompting strategies for student simulation perform poorly; supervised fine-tuning and preference optimization yield much better but still limited performance, motivating future work on this challenging task. 1

Vocabulary acquisition poses a significant challenge for second-language (L2) learners, especially when learning typologically distant languages such as English and Korean, where phonological and structural mismatches complicate vocabulary learning.Recently, large language models (LLMs) have been used to generate keyword mnemonics by leveraging similar keywords from a learner's first language (L1) to aid in acquiring L2 vocabulary.However, most methods still rely on direct IPA-based phonetic matching or employ LLMs without phonological guidance.In this paper, we present PHONI-TALE, a novel cross-lingual mnemonic generation system that performs IPA-based phonological adaptation and syllable-aware alignment to retrieve L1 keyword sequence and uses LLMs to generate verbal cues.We evaluate PHONI-TALE through automated metrics and a shortterm recall test with human participants, comparing its output to human-written and prior automated mnemonics.Our findings show that PHONITALE consistently outperforms previous automated approaches and achieves quality comparable to human-written mnemonics.

Learning Japanese vocabulary is a challenge for learners from Roman alphabet backgrounds due to script differences.Japanese combines syllabaries like hiragana with kanji, which are logographic characters of Chinese origin.Kanji are also complicated due to their complexity and volume.Keyword mnemonics are a common strategy to aid memorization, often using the compositional structure of kanji to form vivid associations.Despite recent efforts to use large language models (LLMs) to assist learners, existing methods for LLM-based keyword mnemonic generation function as a black box, offering limited interpretability.We propose a generative framework that explicitly models the mnemonic construction process as driven by a set of common rules, and learn them using a novel Expectation-Maximization-type algorithm.Trained on learner-authored mnemonics from an online platform, our method learns latent structures and compositional rules, enabling interpretable and systematic mnemonics generation.Experiments show that our method performs well in the cold-start setting for new learners while providing insight into the mechanisms behind effective mnemonic creation.

Recently Processing-in-Memory (PIM) has become a promising solution to achieve energy-efficient computation in data-intensive applications by placing computation near or inside the memory. In most Deep Learning (DL) frameworks, a user manually partitions a model’s computational graph (CG) onto the computing devices by considering the devices’ capability and the data transfer. The Deep Neural Network (DNN) models become increasingly complex for improving accuracy; thus, it is exceptionally challenging to partition the execution to achieve the best performance, especially on a PIM-based platform requiring frequent offloading of large amounts of data. This article proposes two novel algorithms for DL inference to resolve the challenge: low-overhead profiling and optimal model partitioning. First, we reconstruct CG by considering the devices’ capability to represent all the possible scheduling paths. Second, we develop a profiling algorithm to find the required minimum profiling paths to measure all the node and edge costs of the reconstructed CG. Finally, we devise the model partitioning algorithm to get the optimal minimum execution time using the dynamic programming technique with the profiled data. We evaluated our work by executing the BERT, RoBERTa, and GPT-2 models on the ARM multicores with the PIM-modeled FPGA platform with various sequence lengths. For three computing devices in the platform, i.e., CPU serial/parallel and PIM executions, we could find all the costs only in four profile runs, three for node costs and one for edge costs. Also, our model partitioning algorithm achieved the highest performance in all the experiments over the execution with manually assigned device priority and the state-of-the-art greedy approach.