AI Uplifting Personalized RPL Processes

Abstract

This presentation explored how generative AI enhances the collective, distributed, and human‑centered aspects of Recognition of Prior Learning (RPL) within organizations and learning ecosystems. 

Drawing on the latest advancements from major AI providers, this session highlights practical, learner‑centered applications to enrich RPL—from automated evidence mapping to tailored assessment pathways. It foregrounds the plurality of intelligences embedded in these systems—social, technical, organizational—and how they can be orchestrated for inclusive and equitable recognition. 

Participants will be introduced to AI‑powered tools that support: 

• Learner expression: AI assists in converting informal experiences into structured portfolios via prompts, storytelling, and narrative generation. 

• Efficient assessment: AI-enabled tools map learner submissions to competency frameworks, offering preliminary feedback or highlighting missing evidence. 

• Guided pathways: AI transforms complex RPL guidelines into clear, step‑by‑step user journeys, responsive to individual backgrounds and contexts. 

• Tailored skill development: AI identifies gaps and recommends contextualized resources, promoting just‑in‑time personalized learning. 

These use cases showcase how layered intelligences—human and AI—can coalesce to reframe RPL as a dynamic, dialogic, and adaptive process. 

Responsible AI is central to this approach. The session addresses AI ethics in RPL: maintaining empathy, preserving learner agency, ensuring fairness, securing data privacy, and promoting transparent, explainable AI decisions. This safeguards the human‑centered core of recognition, even as AI augments capability. 

By session’s end, attendees will: 

• Recognize how AI can scale, personalize, and humanize recognition practices. 

• Gain actionable insights into integrating AI solutions responsibly within RPL workflows. 

• Understand how combining technical and social intelligences offers richer recognition landscapes. 

Keywords: recognition of prior learning, generative AI, personalized learning pathways, competency mapping, responsible AI, learner agency