VEXA: Evidence-Grounded and Persona-Adaptive Explanations for Scam Risk Sensemaking

• URL: http://arxiv.org/abs/2602.05056v1
• Date: Wed, 04 Feb 2026 21:16:24 GMT
• Title: VEXA: Evidence-Grounded and Persona-Adaptive Explanations for Scam Risk Sensemaking
• Authors: Heajun An, Connor Ng, Sandesh Sharma Dulal, Junghwan Kim, Jin-Hee Cho,
• Abstract summary: Online scams across email, short message services, and social media increasingly challenge everyday risk assessment.<n>We propose VEXA, an evidence-grounded and persona-adaptive framework for generating learner-facing scam explanations.
• Score: 9.22587207148122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online scams across email, short message services, and social media increasingly challenge everyday risk assessment, particularly as generative AI enables more fluent and context-aware deception. Although transformer-based detectors achieve strong predictive performance, their explanations are often opaque to non-experts or misaligned with model decisions. We propose VEXA, an evidence-grounded and persona-adaptive framework for generating learner-facing scam explanations by integrating GradientSHAP-based attribution with theory-informed vulnerability personas. Evaluation across multi-channel datasets shows that grounding explanations in detector-derived evidence improves semantic reliability without increasing linguistic complexity, while persona conditioning introduces interpretable stylistic variation without disrupting evidential alignment. These results reveal a key design insight: evidential grounding governs semantic correctness, whereas persona-based adaptation operates at the level of presentation under constraints of faithfulness. Together, VEXA demonstrates the feasibility of persona-adaptive, evidence-grounded explanations and provides design guidance for trustworthy, learner-facing security explanations in non-formal contexts.

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