Canada

In intelligent language tutoring systems, student dashboards should display the learning progress and performance and support the navigation through the learning content. Designing an interface that transparently offers information on students’ learning in relation to specific learning targets while linking to the overarching functional goal, that motivates and organizes the practice in current foreign language teaching, is challenging. This becomes even more difficult in systems that adaptively expose students to different learning material and individualize system interactions. If such a system is used in an ecologically valid setting of blended learning, this generates additional requirements to incorporate the needs of students and teachers for control and customizability.
We present the conceptual design of a student dashboard for a task-based, user-adaptive intelligent language tutoring system intended for use in real-life English classes in secondary schools. We highlight the key challenges and spell out open questions for future research.

ACL 2023

Reconciling Adaptivity and Task Orientation in the Student Dashboard of an Intelligent Language Tutoring System

task orientation

intelligent language tutoring system

adaptivity

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Improving conversational proficiency is a key target for students learning a new language. While acquiring conversational proficiency, students must learn the linguistic mechanisms of Repair and Grounding (R&G) to negotiate meaning and find common ground with their interlocutor so conversational breakdowns can be resolved. Task-oriented Spoken Dialogue Systems (SDS) have long been sought as a tool to hone conversational proficiency. However, the R&G patterns for language learners interacting with a task-oriented spoken dialogue system are not reflected explicitly in any existing datasets. Therefore, to move the needle in Spoken Dialogue Systems for language learning we present GrounDialog: an annotated dataset of spoken conversations where we elicit a rich set of R&G patterns.

GrounDialog: A Dataset for Repair and Grounding in Task-oriented Spoken Dialogues for Language Learning

Lectures are a learning experience for both students and teachers. Students learn from teachers about the subject material, while teachers learn from students about how to refine their instruction. However, online student feedback is unstructured and abundant, making it challenging for teachers to learn and improve. We take a step towards tackling this challenge. First, we contribute a dataset for studying this problem: SIGHT is a large dataset of 288 math lecture transcripts and 15,784 comments collected from the Massachusetts Institute of Technology OpenCourseWare (MIT OCW) YouTube channel. Second, we develop a rubric for categorizing feedback types using qualitative analysis. Qualitative analysis methods are powerful in uncovering domain-specific insights, however they are costly to apply to large data sources. To overcome this challenge, we propose a set of best practices for using large language models (LLMs) to cheaply classify the comments at scale. We observe a striking correlation between the model's and humans' annotation: Categories with consistent human annotations (>0.9 inter-rater reliability, IRR) also display higher human-model agreement (>0.7), while categories with less consistent human annotations (0.7-0.8 IRR) correspondingly demonstrate lower human-model agreement (0.3-0.5). These techniques uncover useful student feedback from thousands of comments, costing around $0.002 per comment. We conclude by discussing exciting future directions on using online student feedback and improving automated annotation techniques for qualitative research.

SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts

This paper addresses the problem of providing automatic feedback on orthographic errors in handwritten text. Despite the availability of automatic error detection systems, the practical problem of digitizing the handwriting remains. Current handwriting recognition (HWR) systems produce highly accurate transcriptions but normalize away the very errors that are essential for providing useful feedback, e.g. ortho- graphic errors. Our contribution is twofold: First, we create a comprehensive dataset of handwritten text with transcripts retaining or- thographic errors by transcribing 1,350 pages from the German learner dataset FD-LEX. Second, we train a simple HWR system on our dataset, allowing it to transcribe words with orthographic errors. Thereby, we evaluate the effect of different dictionaries on recognition output, highlighting the importance of addressing spelling errors in these dictionaries.

Recognizing Learner Handwriting Retaining Orthographic Errors for Enabling Fine-Grained Error Feedback

Creating high-quality multiple-choice items requires careful attention to several factors, including ensuring that there is only one correct option, that options are independent of each other, that there is no overlap between options, and that each option is plausible. This attention is reflected in the explanations provided by human item-writers for each option. This study aimed to compare the creation of explanations of multiple-choice item options for reading comprehension by ChatGPT with those created by humans. We used two context-dependent multiple-choice item sets created based on Evidence‐Centered Design. Results indicate that ChatGPT is capable of producing explanations with different type of information that are comparable to those created by humans. So that humans could benefit from additional information given to enhance their explanations. We conclude that ChatGPT ability to generate explanations for multiple-choice item options in reading comprehension tests is comparable to that of humans.

You've Got a Friend in ... a Language Model? A Comparison of Explanations of Multiple-Choice Items of Reading Comprehension between ChatGPT and Humans

We introduce a novel technique for automatically summarizing lecture videos using large language models such as GPT-3 and we present a user study investigating the effects on the studying experience when automatic summaries are added to lecture videos. We test students under different conditions and find that the students who are shown a summary next to a lecture video perform better on quizzes designed to test the course materials than the students who have access only to the video or the summary. Our findings suggest that adding automatic summaries to lecture videos enhances the learning experience. Qualitatively, students preferred summaries when studying under time constraints.

Automatically Generated Summaries of Video Lectures May Enhance Students' Learning Experience

Lexical simplification (LS) automatically replaces words that are deemed difficult to understand for a given target population with simpler alternatives, whilst preserving the meaning of the original sentence. The TSAR-2022 shared task on LS provided participants with a multilingual lexical simplification test set. It contained nearly 1,200 complex words in English, Portuguese, and Spanish and presented multiple candidate substitutions for each complex word. The competition did not make training data available; therefore, teams had to use either off-the-shelf pre-trained large language models (LLMs) or out-domain data to develop their LS systems. As such, participants were unable to fully explore the capabilities of LLMs by re-training and/or fine-tuning them on in-domain data. To address this important limitation, we present ALEXSIS+, a multilingual dataset in the aforementioned three languages, and ALEXSIS++, an English monolingual dataset that together contains more than 50,000 unique sentences retrieved from news corpora and annotated with cosine similarities to the original complex word and sentence. Using these additional contexts, we are able to generate new high-quality candidate substitutions that improve LS performance on the TSAR-2022 test set regardless of the language or model.

ALEXSIS+: Improving Substitute Generation and Selection for Lexical Simplification with Information Retrieval

Writing high-quality test questions (items) is critical to building educational measures but has traditionally also been a time-consuming process. One promising avenue for alleviating this is automated item generation, whereby methods from artificial intelligence (AI) are used to generate new items with minimal human intervention. Researchers have explored using large language models (LLMs) to generate new items with equivalent psychometric properties to human-written ones. But can LLMs generate items with improved psychometric properties, even when existing items have poor validity evidence? We investigate this using items from a natural language inference (NLI) dataset. We develop a novel prompting strategy based on selecting items with both the best and worst properties to use in the prompt and use GPT-3 to generate new NLI items. We find that the GPT-3 items show improved psychometric properties in many cases, whilst also possessing good content, convergent, and discriminant validity evidence. Collectively, our results demonstrate the potential of employing LLMs to ease the item development process and suggest that the careful use of prompting may allow for iterative improvement of item quality.

Generating Better Items for Cognitive Assessments Using Large Language Models

Automatic readability assessment (ARA) predicts how difficult it is for the reader to understand a text. While ARA has traditionally been performed at the passage level, there has been increasing interest in ARA at the sentence level, given its applications in downstream tasks such as text simplification and language exercise generation. Recent research has suggested the effectiveness of hybrid approaches for ARA, but they have yet to be applied on the sentence level. We present the first study that compares neural and hybrid models for sentence-level ARA. We conducted experiments on graded sentences from the Wall Street Journal (WSJ) and a dataset derived from the OneStopEnglish corpus. Experimental results show that both neural and hybrid models outperform traditional classifiers trained on linguistic features. Hybrid models obtained the best accuracy on both datasets, surpassing the previous best result reported on the WSJ dataset by almost 13% absolute.

Hybrid Models for Sentence Readability Assessment

Grammatical error correction (GEC) is a challenging task for non-native second language (L2) learners and learning machines. Data-driven GEC learning requires as much human-annotated genuine training data as possible. However, it is difficult to produce larger-scale human-annotated data, and synthetically generated large-scale parallel training data is valuable for GEC systems. In this paper, we propose a method for rebuilding a corpus of synthetic parallel data using target sentences predicted by a GEC model to improve performance. Experimental results show that our proposed pre-training outperforms that on the original synthetic datasets. Moreover, it is also shown that our proposed training without human-annotated L2 learners' corpora is as practical as conventional full pipeline training with both synthetic datasets and L2 learners' corpora in terms of accuracy.

Training for Grammatical Error Correction Without Human-Annotated L2 Learners' Corpora

Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale. It plays a vital role in simplifying or annotating complex words to assist readers. To study lexical complexity in Japanese, we construct the first Japanese LCP dataset. Our dataset provides separate complexity scores for Chinese/Korean annotators and others to address the readers’ L1-specific needs. In the baseline experiment, we demonstrate the effectiveness of a BERT-based system for Japanese LCP.

Next from ACL 2023

GrounDialog: A Dataset for Repair and Grounding in Task-oriented Spoken Dialogues for Language Learning

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