What risks come with using AI in skill assessments—and how can L&D teams stay ahead of them?
By Emily Justin-Szopinski
What we’ve learned so far about AI-powered functionalities for learning and assessment:
- Improves outcomes, increases engagement and knowledge retention, decreases training time, and more ( learn more here)
- Increases ROI on learning initiatives, produces better business outcomes, and boosts employee engagement and retention ( learn more here)
- These positive outcomes are a result of several components working together to form enhanced learning and assessment experiences: algorithms, machine learning, Gen AI, Large Language Models, Natural Language Processing, Data Learning Analytics ( Learn more here)
So, AI is good for learning assessment. It’s good for organizational performance. This is made possible because of its ability to analyze a lot of data, really fast, and use its analysis to produce unique, tailored learning paths, based on the needs of each employee. Great. AI seems like it presents a clear value add in the skills assessment arena.
But like anything that sounds this good, it’s not without its challenges. Before jumping in, it’s worth pausing to look at the risks that come with using AI in learning assessment—and what we can do to manage them. What should L&D leaders be watching out for, and how can they put the right guardrails in place?
AI Assessment Risks at a Glance
The information below breaks down our research findings on key risks associated with AI-powered assessment—along with real-world examples of what those risks can look like in practice for L&D teams.
| AI Component | Associated Risk (brief) | Real-World Scenario |
|---|---|---|
| Algorithms | Bias & Discrimination | An AI tool used to evaluate leadership potential consistently scores women lower than men due to biased historical data—resulting in fewer women being shortlisted for leadership development programs. |
| Machine Learning | Inaccurate or Unreliable Assessments | In a manufacturing firm, a machine learning model misclassifies several proficient workers as “not qualified” during a safety certification assessment. |
| Learning Data Analytics | Data Privacy & Security | A financial organization’s analytics platform leaks assessment scores in a misconfigured cloud repository—triggering a GDPR investigation. |
| GenAI | Overreliance & Reduced Oversight | An L&D team uses a GenAI tool to automatically generate assessment questions and grade responses for a technical upskilling program. |
| NLPs + LLMs | Compliance & Regulatory Risk | A financial services firm uses an NLP tool to score written responses in its FINRA-mandated compliance training. |
Mitigating Risks
AI brings a lot to the skills assessment table, and incorporating it into your learning strategy is more likely a question of “when and how”, than “if”. That being said, our research shows that it’s important to consider all of the possible implications of its implementation in order to safeguard the organization, its data, and their employees. We’ve compiled a list based on our research of few practical ways to do just that:
1. Mitigate Algorithmic Bias
- Use diverse, representative training data.
- Regularly audit models (These tools can help: IBM’s AI Fairness 360, LIME, or SHAP).
- Follow industry ethical guidelines (e.g., IEEE, ACM).
2. Ensure Data Privacy & Security
- Anonymize and encrypt employee data.
- Use role-based access controls to limit exposure.
- Confirm vendor compliance with privacy laws (e.g., GDPR, CCPA, HIPAA, EU AI Act).
Clearly explain how AI is used and what data is collected and provide channels for feedback.
3. Maintain Human Oversight
- Use human reviewers for critical decisions like promotions, compliance training content, or certifications.
- Leverage explainable AI tools so employees understand how assessments are scored. (These tools can help you: ELI5, AIX360, Interpret ML, Anchors, Google Vertex, InterpretML – Microsoft).
- Test before you scale and ensure that edge cases are handled reliably.
- Track KPIs like accuracy, fairness, and learner satisfaction.
4. Upskill the L&D Team
- Offer AI literacy and ethics training.
- Teach teams how to use and evaluate generative AI responsibly.
Learn More!
Check out our previous article in this series.
By Emily Justin-Szopinski
Digital Learning Specialist and Educational Product Developer with over 15 years experience in creating high impact learning experiences for global audiences.