Dynamic Evidence Collection Ecosystem for Assessment Integrity and Authentic Competence
2026-08-17 • Human-Computer Interaction
Human-Computer InteractionArtificial Intelligence
AI summaryⓘ
The authors explain that traditional exams and assignments don’t work well when students can use AI tools to produce final work. They suggest a new way to assess learning by collecting many pieces of evidence over time, like drafts, reflections, and teamwork, not just final products. Their system uses AI to help track progress, give feedback, and keep things clear. They believe this approach focuses on good assessment design rather than just trying to catch cheating. The paper also discusses how their idea can be put into practice at schools.
Generative AIAssessment designAcademic integrityFormative feedbackLearning analyticsAuthentic assessmentPeer collaborationIterative artefactsSelf-reflectionAI in education
Authors
Rajan Kadel, Bellal Hossain, Samar Shailendra, Bushra Naeem
Abstract
Generative Artificial Intelligence (GenAI) can produce high-quality essays, code, and design artefacts, challenging the validity of conventional assessments that rely on single-point submissions and product-only grading. This paper proposes a design framework called "Dynamic Evidence Collection Ecosystem" that shifts assessment toward continuous, authentic, multi-source evidence of student learning over time. The framework collects process evidence through iterative artefacts, design logs, activity rounds, self-reflection, and peer collaboration, supported by an AI-enabled layer for learning analytics, formative feedback, and transparency. The approach is grounded in recent assessment-redesign scholarship in AI-rich contexts and aligned with contemporary views of authenticity in assessment. This paper builds on the hypothesis that academic integrity is strengthened when it is treated as an assessment design rather than as an AI detection problem. The tools have limitations and risks of use that carry academic penalties. This paper presents an implementation scenario to support institutional adoption.