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Behaviorally Adaptive Visual Diversion for Inclusive and Resilient Digital Assessment Delivery

● high · 2026-08-05assessment, accessible-learning, privacy, academic-integrity, equity, adaptive-learning, higher-ed

Behaviorally Adaptive Visual Diversion for Inclusive and Resilient Digital Assessment Delivery — Proposes BAVD, a theoretical framework for adaptive visual diversion in digital assessment that resists screen-capture cheating while accommodating learners with visual-processing accommodations. Formulates the model using coupled dynamical systems (... Assessment Accessible Learning Privacy Academic Integrity Equity Adaptive Learning

Proposes BAVD, a theoretical framework for adaptive visual diversion in digital assessment that resists screen-capture cheating while accommodating learners with visual-processing accommodations. Formulates the model using coupled dynamical systems (Diversion Field Generator, Rendering Tensor, Behavior Tensor, Multi-dimensional Entropy Model). Establishes theoretical properties for content fidelity, rendering stability, entropy boundedness, and closed-loop adaptation stability. Explicitly addresses the trade-off between accessibility and capture resistance.

Abstract

Institutions increasingly rely on browser lockdown, webcam monitoring, and behavioral analytics to secure high-stakes digital assessments, yet these mechanisms are commonly designed and evaluated independently and often overlook learner accessibility. This paper introduces Behaviorally-Adaptive Visual Diversion (BAVD), a theoretical framework in which a synthetic, non-semantic visual field is composited with assessment content and adaptively modulated according to observed candidate behavior. The underlying assessment content is never altered; only its visual presentation is modified to reduce the usefulness of unauthorized screen capture or screen sharing while remaining minimally intrusive for legitimate candidates.

Connected Concepts

  • Assessment
  • Accessible Learning
  • Privacy
  • Academic Integrity
  • Equity
  • Adaptive Learning

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    • Citation

    Gupta Lovi Raj, Kamalpreet Kaur, Dama Sriram, & Parali Prajithaa (2026). Behaviorally Adaptive Visual Diversion for Inclusive and Resilient Digital Assessment Delivery. arXiv:2608.03531. arXiv:2608.03531 [cs.AI].