Papers for

school it administrators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

KIRA screening system improves early reading checks with phoneme feedback

KIRA: A Full-Stack Reading Screening System with Dual-Pathway Analysis for Children

Abstract: Early-grade reading screening is critical for identifying children at risk of reading difficulty. Existing AI-based screeners are often limited in the scope of their analyses and evaluation metrics, or are proprietary systems that lack transparent model specifications and evaluation. We present KIRA, a dual-pathway screening system that pairs word-level fluency scoring with detailed phoneme-level error detection. We adapt pretrained word and phoneme recognizers to children's speech, reducing word error rate from 13.88% to 8.84% and phoneme error rate from 41.66% to 23.00%. Powered by these child-adapted models, we implement Word Reading (WRE) and Oral Reading Fluency (ORF), two foundational tasks in K-2 screening. The system integrates these recognizers with a web-based teacher-facing interface presenting fluency scores alongside phoneme feedback for potential targeted intervention. KIRA brings modern child-speech recognition into a full-stack, deployable, classroom-ready reading screener.

Tue 20 OctComputation and LanguageMachine Learning
The gist
Many young children struggle with reading, and identifying those who need extra help early on is important. The authors created KIRA, a tool that listens to kids reading aloud and checks both whole words and the smaller sounds inside words. By adapting speech recognition models specifically for children's voices, KIRA reduces errors in detecting words and sounds. It also provides teachers with easy-to-understand scores and detailed feedback to help guide reading support.
Open → 2610.28840v1

Face recognition models improve classroom attendance accuracy

Towards Robust Classroom Attendance: A Comprehensive Evaluation of Face Detection and Recognition Models

Abstract: Manual attendance methods, such as paper or register-based systems, take a lot of time, can lead to errors, and are easy to falsify. Face recognition is more reliable, but it frequently struggles in classrooms because lighting and other conditions can vary. Face recognition datasets are designed for regulated environments and do not capture the actual challenges found in classrooms. To address this, a new face detection and recognition dataset, the Visage Face dataset, comprising 16,234 face samples, is proposed for the task of face detection and recognition. The photos are taken from different angles and under varying lighting conditions, with students showing a range of expressions, and some faces partly covered to reflect real-life situations. A YOLO-based system is used to detect faces and tested seven advanced face recognition models with thirteen configurations: LVFace, QCFace, FaceLiVTv2, TopoFR, EdgeFace, TransFace, and GhostFaceNets. Of these, FaceLiVTv2-M performed best, with 99.75% Top-1/Top-5 accuracy and an inference time of 6.459 ms. These results show that the Visage Face Dataset is a realistic and challenging benchmark for face recognition in classroom attendance.

Sat 19 SeptComputer Vision and Pattern RecognitionMachine Learning
The gist
Taking attendance in classrooms can be slow and prone to mistakes when done manually. The authors created a new set of student face photos taken under real classroom conditions to help improve face recognition accuracy. They tested several face recognition models and found that one called FaceLiVTv2-M works best on this challenging data. This shows their new photo set is useful for improving attendance systems in schools using face recognition.
Open → 2609.22750v1