Date
Publisher
arXiv
Artificial intelligence-driven adaptive learning systems are reshaping
education through data-driven adaptation of learning experiences. Yet many of
these systems lack transparency, offering limited insight into how decisions
are made. Most explainable AI (XAI) techniques focus on technical outputs but
neglect user roles and comprehension. This paper proposes a hybrid framework
that integrates traditional XAI techniques with generative AI models and user
personalisation to generate multimodal, personalised explanations tailored to
user needs. We redefine explainability as a dynamic communication process
tailored to user roles and learning goals. We outline the framework's design,
key XAI limitations in education, and research directions on accuracy,
fairness, and personalisation. Our aim is to move towards explainable AI that
enhances transparency while supporting user-centred experiences.
Study at a glance
- Question: How can AI learning systems better explain their decisions to meet the needs of different users?
- Setting and data: Not stated in the abstract
- Method: Proposes a conceptual framework design
Why it matters
Many AI-driven adaptive learning systems lack transparency, offering limited insight into how decisions are made. Moreover, most explainable AI techniques focus on technical outputs but neglect user roles and comprehension.
Key findings
- A hybrid approach combining traditional explainable AI with generative models can produce personalized explanations across multiple formats tailored to user needs.
- Adaptive learning systems often lack transparency, and existing explanation techniques focus on technical outputs while neglecting user roles and comprehension.
- AI explainability in education functions as a dynamic communication process tailored to specific user roles and learning goals.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Why use AI?
Study design
