An Extension-Based Accessibility Framework for Making Blockly Accessible to Blind and Low-Vision Users

• URL: http://arxiv.org/abs/2601.10688v1
• Date: Thu, 15 Jan 2026 18:48:39 GMT
• Title: An Extension-Based Accessibility Framework for Making Blockly Accessible to Blind and Low-Vision Users
• Authors: Rubel Hassan Mollik, Vamsi Krishna Kosuri, Hans Djalali, Stephanie Ludi, Aboubakar Mountapmbeme,
• Abstract summary: Block-based programming environments (BBPEs) such as Scratch and Code.org are now widely used in K-12 computer science classes.<n>However, they remain mostly inaccessible to blind or visually impaired (BVI) learners.<n>We present an Extension-based Accessibility Framework (EAF) to make BBPEs accessible for BVI students.
• Score: 0.45671221781968335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Block-based programming environments (BBPEs) such as Scratch and Code.org are now widely used in K-12 computer science classes, but they remain mostly inaccessible to blind or visually impaired (BVI) learners. A major problem is that prior accessibility solutions have relied on modifications to the Blockly library, making them difficult to apply in existing BBPEs and thereby limiting adoption. We present an Extension-based Accessibility Framework (EAF) to make BBPEs accessible for BVI students. The framework uses a modular architecture that enables seamless integration with existing Blockly-based BBPEs. We present an innovative three-dimensional (3D) hierarchical navigation model featuring stack labeling and block numbering, mode-based editing to prevent accidental modifications, and WAI-ARIA implementation to ensure compatibility with external screen readers. We evaluated our approach by integrating the EAF framework into two BBPEs (covering 177 test cases) and conducting semi-structured interviews with four participants using VoiceOver, JAWS, and NVDA. Participants reported clearer spatial orientation and easier mental model formation compared to default Blockly keyboard navigation. EAF shows that modular architecture can provide comprehensive accessibility while ensuring compatibility with existing BBPEs.

Related papers

• BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning [84.56022893225422]
  Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge.<n>Applying vision-language models such as CLIP to CIL poses two major challenges: (1) adapting to downstream tasks often requires additional learnable modules, increasing model complexity and susceptibility to forgetting; and (2) while multi-modal representations offer complementary strengths, existing methods have yet to fully realize their potential in effectively integrating visual and textual modalities.
  arXiv  Detail & Related papers  (2025-11-14T15:51:40Z)
• EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering [55.56674028743782]
  Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time.<n>We present EasySteer, a unified framework for high-performance, LLM steering built on vLLM.
  arXiv  Detail & Related papers  (2025-09-29T17:59:07Z)
• KonfAI: A Modular and Fully Configurable Framework for Deep Learning in Medical Imaging [1.2556993688873865]
  KonfAI is a modular and fully deep learning framework specifically designed for medical imaging tasks.<n>It enables users to define complete training, inference, and evaluation through structured YAML configuration files.<n>KonfAI is open source and available at https://github.com/vboussot/KonfAI.
  arXiv  Detail & Related papers  (2025-08-13T13:55:43Z)
• CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for Functional Modules in Autonomous Driving [2.9741451632381755]
  We propose a hierarchically decoupled Mixture-of-Experts architecture at the functional module level.<n>CBDES MoE integrates multiple structurally heterogeneous expert networks with a lightweight Self-Attention Router gating mechanism.<n>It consistently outperforms fixed single-expert baselines in 3D object detection.
  arXiv  Detail & Related papers  (2025-08-11T10:44:25Z)
• DART: Dual Adaptive Refinement Transfer for Open-Vocabulary Multi-Label Recognition [59.203152078315235]
  Open-Vocabulary Multi-Label Recognition (OV-MLR) aims to identify multiple seen and unseen object categories within an image.<n> Vision-Language Pre-training models offer a strong open-vocabulary foundation, but struggle with fine-grained localization under weak supervision.<n>We propose the Dual Adaptive Refinement Transfer (DART) framework to overcome these limitations.
  arXiv  Detail & Related papers  (2025-08-07T17:22:33Z)
• Vocabulary-free Fine-grained Visual Recognition via Enriched Contextually Grounded Vision-Language Model [52.01031460230826]
  Traditional approaches rely heavily on fixed vocabularies and closed-set classification paradigms.<n>Recent research has demonstrated that combining large language models with vision-language models (VLMs) makes open-set recognition possible.<n>We propose our training-free method, Enriched-FineR, which demonstrates state-of-the-art results in fine-grained visual recognition.
  arXiv  Detail & Related papers  (2025-07-30T20:06:01Z)
• ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability [10.957111899739926]
  Existing frameworks, such as InterpretDL and OmniXAI, integrate multiple attribution methods but suffer from scalability limitations, high coupling, theoretical constraints, and lack of user-friendly implementations.<n>We propose Attribution-Based Explainability (ABE), a unified framework that formalizes Fundamental Attribution Methods and integrates state-of-the-art attribution algorithms.
  arXiv  Detail & Related papers  (2025-05-03T12:00:59Z)
• LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction [63.668635390907575]
  Existing methods enhance open-vocabulary object detection by leveraging the robust open-vocabulary recognition capabilities of Vision-Language Models (VLMs) We propose the Language Model Instruction (LaMI) strategy, which leverages the relationships between visual concepts and applies them within a simple yet effective DETR-like detector.
  arXiv  Detail & Related papers  (2024-07-16T02:58:33Z)
• AccessLens: Auto-detecting Inaccessibility of Everyday Objects [17.269659576368536]
  We introduce AccessLens, an end-to-end system designed to identify inaccessible interfaces in daily objects. Our approach involves training a detector using the novel AccessDB dataset designed to automatically recognize 21 distinct Inaccessibility Classes. AccessMeta serves as a robust way to build a comprehensive dictionary linking these accessibility classes to open-source 3D augmentation designs.
  arXiv  Detail & Related papers  (2024-01-29T09:27:55Z)
• LAMBO: Large AI Model Empowered Edge Intelligence [71.56135386994119]
  Next-generation edge intelligence is anticipated to benefit various applications via offloading techniques. Traditional offloading architectures face several issues, including heterogeneous constraints, partial perception, uncertain generalization, and lack of tractability. We propose a Large AI Model-Based Offloading (LAMBO) framework with over one billion parameters for solving these problems.
  arXiv  Detail & Related papers  (2023-08-29T07:25:42Z)
• Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computation [7.375613864999323]
  We introduce the first solution for object detection that manages the triple communication-computation-accuracy trade-off with a single set of weights. Our solution shows state-of-the-art results on COCO-2017, adding only a minor penalty on the base EfficientDet-D2 architecture.
  arXiv  Detail & Related papers  (2021-05-03T01:08:34Z)
• Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
  Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
  arXiv  Detail & Related papers  (2020-07-13T20:30:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.