  In the age of artificial intelligence (AI), providing learners with suitable
and sufficient explanations of AI-based recommendation algorithm's output
becomes essential to enable them to make an informed decision about it.
However, the rapid development of AI approaches for educational recommendations
and their explainability is not accompanied by an equal level of evidence-based
experimentation to evaluate the learning effect of those explanations. To
address this issue, we propose an experimental web-based tool for evaluating
multimodal and large language model (LLM) based explainability approaches. Our
tool provides a comprehensive set of modular, interactive, and customizable
explainability elements, which researchers and educators can utilize to study
the role of individual and hybrid explainability methods. We design a two-stage
evaluation of the proposed tool, with learners and with educators. Our
preliminary results from the first stage show high acceptance of the tool's
components, user-friendliness, and an induced motivation to use the
explanations for exploring more information about the recommendation.
