Problem
Research questions and friction points this paper is trying to address.
Generative AI
Assessment Validity
Academic Integrity
Authentic Competence
Dynamic Evidence Collection Ecosystem for Assessment Integrity and Authentic Competence
🤖 AI Summary
This study addresses the challenges posed by generative AI to traditional assessment validity and academic integrity by reframing integrity as an assessment design issue. It proposes a continuous authenticity evaluation framework grounded in process evidence. By establishing a dynamic evidence-collection ecosystem that integrates iterative artifact capture, multi-source learning analytics, and AI-assisted transparency mechanisms, the framework enables ongoing verification of student competencies. Furthermore, this research provides actionable institutional implementation scenarios that effectively reinforce the authenticity and robustness of educational assessment in the AI era. Ultimately, this work offers both theoretical foundations and practical paradigms for reshaping evaluation systems within the age of intelligent technologies, ensuring that assessments remain valid and trustworthy amidst rapid technological disruption.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic Evidence Collection Ecosystem
Process Evidence
Authentic Assessment
Assessment Redesign
Academic Integrity
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