- Explainable AI (XAI) is essential for ensuring transparency and trust in AI-powered e-learning platforms.Educators need to understand how algorithms personalize learning paths, assess performance, or generate recommendations. This allows them to identify potential biases and ensure the system aligns with their pedagogical goals.
- With explainable AI, students can gain insights into why the system suggests certain learning materials or assesses their work in a particular way. This fosters a sense of agency and allows students to learn from the feedback provided by the AI system.
- Explainable AI can help developers identify and address potential biases within the algorithms. By analyzing the decision-making process of the AI, developers can ensure fairness and inclusivity in the e-learning experience for all students.
Impact of EU Act and AAA on AI Explainability:
- Both acts highlight the need for explainable AI and require developers to be able to explain how their algorithms reach specific conclusions. This will likely lead to the development of new tools and techniques for explaining the inner workings of AI models used in e-learning platforms.
- Regulations might mandate specific levels of explainability depending on the risk associated with the AI application. For instance, high-stakes assessments like automated grading may require more sophisticated explainability methods compared to personalized content recommendations.
Resources:
- A Survey on Explainable Artificial Intelligence for Education: https://arxiv.org/abs/2101.09429
- Towards Explainable AI in Education: https://arxiv.org/pdf/2301.06676
- Explainable AI and the EU Artificial Intelligence Act: https://medium.com/@eClearAG/the-eu-ai-act-explained-315bb8e49e2e (This article explores explainability in the context of the EU Act but is relevant to both regulations)
By incorporating explainable AI, e-learning systems can become more transparent, trustworthy, and effective tools for enhancing the learning experience for both educators and students.