Yotam Sechayk

Despite widespread use, charts remain largely inaccessible for Low-Vision Individuals (LVI). Reading charts requires viewing data points within a global context, which is difficult for LVI who may rely on magnification or experience a partial field of vision. We aim to improve exploration by providing visual access to critical context. To inform this, we conducted a formative study with five LVI. We identified four fundamental contextual elements common across chart types: axes, legend, grid lines, and the overview. We propose two pointer-based interaction methods to provide this context: Dynamic Context, a novel focus+context interaction, and Mini-map, which adapts overview+detail principles for LVI. In a study with N=22 LVI, we compared both methods and evaluated their integration to current tools. Our results show that Dynamic Context had significant positive impact on access, usability, and effort reduction; however, worsened visual load. Mini-map strengthened spatial understanding, but was less preferred for this task. We offer design insights to guide the development of future systems that support LVI with visual context while balancing visual load.

Charts, slides, and web pages remain difficult to access for the estimated 295 million people globally with low vision (LV). Much accessibility research translates visual content into speech, sound, or touch, but LV people often prefer to use their residual vision. My dissertation follows ability-based design to create tools motivated by residual vision use. My ongoing work extends this to ability-based AI systems that adapt AI interaction to LV users’ abilities. The central method is visual communication between tools and LV users. My completed work explores tool-to-user communication: VeasyGuide highlights instructor actions for LV learners in videos, while on-cursor interactions restore chart context hidden by magnification. Both systems improved access in studies with LV participants. My ongoing work examines user-to-tool communication through an AI assistant that receives the user’s magnified view and point of interest during chart exploration. My planned work asks how AI can answer visually, combine both directions, and balance assistance with user agency. I plan to extend this communication beyond charts to the web, documents, and eventually the physical world. Ultimately, my dissertation advances personalized AI that uses bidirectional visual communication to support the independence and agency of LV people.

データ可視化グラフは情報伝達の手段として広く利用されているが，弱視者（Low Vision Individuals: LVI）にとっては依然として困難を伴うものであり，注目している情報とその周囲にある文脈情報の両方にアクセスすることが難しい．LVI を支援する最も一般的なツールは画面拡大ツールであるが，一様な拡大によってグラフの一部が視野から外れ，パン操作やレイアウトの記憶が求められるため，高い認知的負荷を引き起こす．これらの課題を明らかにするため，5 名の LVI を対象とした予備的調査を実施した．その結果，参加者は凡例といった文脈的要素を推測に頼りながら探すことが多く，それが余分な認知的負荷を生じさせていた．これらの知見に基づき，Graph Guide を開発した．Graph Guide は Focus+Context の発想に基づき，視野外にある文脈的要素（例: 軸や凡例）を意味的に抽出し，視野内へ投影する手法である．さらに Graph Guide を検証するため，6 名の LVI 参加者を対象に予備的評価を実施した．その結果，Graph Guide は既存の従来ツールによるワークフローと比較して，知覚的アクセスを改善し，労力を削減し，システムユーザビリティ尺度（System Usability Scale: SUS）においてより高いスコアを達成した．本研究の知見は，新たな機能を画面拡大ツールと統合する際に生じる視覚的な情報過多の管理といった課題を明らかにしており，今後さらなる研究の必要性を示唆している．

Instructors often rely on visual actions such as pointing, marking, and sketching to convey information in educational presentation videos. These subtle visual cues often lack verbal descriptions, forcing low-vision (LV) learners to search for visual indicators or rely solely on audio, which can lead to missed information and increased cognitive load. To address this challenge, we conducted a co-design study with three LV participants and developed VeasyGuide, a tool that uses motion detection to identify instructor actions and dynamically highlight and magnify them. VeasyGuide produces familiar visual highlights that convey spatial context and adapt to diverse learners and content through extensive personalization and real-time visual feedback. VeasyGuide reduces visual search effort by clarifying what to look for and where to look. In an evaluation with 8 LV participants, learners demonstrated a significant improvement in detecting instructor actions, with faster response times and significantly reduced cognitive load. A separate evaluation with 8 sighted participants showed that VeasyGuide also enhanced engagement and attentiveness, suggesting its potential as a universally beneficial tool.