Digital and AI-Enabled Scaffolding in RPL Assessment: A Systematic Literature Review of Mechanisms, Evidence, and Design Principles

Abstract

This study aims to synthesize the mechanisms, evidence, and principles of digital scaffolding design and artificial intelligence assistance in the assessment of Recognition of Prior Learning (RPL). The study employs a systematic literature review design, adhering to PRISMA 2020 reporting guidelines, with a publication deadline from January 2021 to September 2026. The initial synthesis of the selected literature combines thematic analysis of support mechanisms and structured analysis of evaluation results, distinguishing empirical evidence from conceptual propositions. Four mechanisms were identified: articulation of lessons learned from experience, portfolio development and evidence mapping, formative feedback, and interpretive support for assessors. The literature documented candidate readiness, narrative traceability, and document processing performance, but did not provide adequate evidence regarding improved validity and fairness of recognition decisions. Classification accuracy and annotation consistency measured outcomes that differed from the adequacy of competency evidence. The proposed originality lies in the integration of mechanisms, assessment tasks, evaluation results, and application conditions within a single analytical framework. The study contributes by clarifying the differences between presentation quality, evidentiary quality, and decision validity, and by formulating design principles regarding claim ownership, traceability, assistance adjustment, uncertainty, accessibility, and human accountability. Unfinished corpus selection and quality assessments limit the determination of effectiveness and generalization. This framework provides a basis for testing candidate and assessor support in a variety of RPL contexts.