Compliant AI in RPL: A Hands-On Workshop for Implementation

Abstract

The integration of Artificial Intelligence (AI) into education and workforce development offers immense potential, yet it demands a robust framework for ethical and compliant deployment. For Recognition of Prior Learning (RPL), AI promises to enhance the validation of skills acquired outside formal education. However, the key to unlocking this potential lies in moving from theoretical discussion to practical, responsible implementation. 

This session was an immersive, hands-on workshop designed to equip participants with the skills to apply AI in RPL processes safely and effectively. The session began by briefly establishing the essential guardrails for compliance, focusing on the guidance provided by ISO/IEC 42001:2023 (AIMS). This standard provides a clear framework for fairness, transparency, and accountability. This standard was also contextualized with the emerging requirements of regulations like the European AI Act, which classifies many educational AI tools as "high-risk" and mandates stringent human oversight and risk management. This foundational knowledge will serve as the launchpad for our practical work.  

The core of this session was a 'Do It' deep dive. Participants broke into small, collaborative groups, each tackling a real-world RPL scenario. Armed with a toolkit of expertly crafted prompts and templates, groups actively worked through the stages of AI-augmented recognition. The groups: 

• Considered the implementation of AI within the RPL workflow to streamline candidate intake, using AI to analyze submitted evidence, and identify gaps. 

• Applied AI prompts to support an assessor's process for validating complex evidence, flagging inconsistencies for human review. 

• Crafted personalized candidate feedback and guidance using AI, empowering individuals in their learning journey. 

• Developed strategies to assist the mitigation plan when identifying and reducing potential bias in an AI-assisted assessment process, ensuring equitable outcomes. 

• Examined the AI audit trail - structuring how an AI system can automate the documentation crucial for accountability and regulatory adherence. 

Throughout these exercises, the emphasis was on maintaining human oversight and ensuring that AI serves as an intelligent assistant that augments—rather than replaces—human expertise. Participants didn’t just hear about compliant AI; they built and applied it. Participants left this workshop with tested tools and the confidence to implement AI in RPL responsibly, fostering trust, and advancing a more accessible lifelong learning ecosystem. 

Keywords: AI, risk, compliance, use-cases, RPL