Tools to Support the Design of Network-Structured Courses Assisted by AI

The integration of Information Technologies into education has lately focused on implementing learning systems to enhance the educational experience through innovative teaching methodologies. The rise of Artificial Intelligence has enabled the development of advanced strategies and algorithms, catering to individual learning styles. However, implementing these innovative approaches requires teachers to learn during the design and structuring of course content. As an example of this we have Khipulearn, a learning platform based on Customised Adaptive Learning Model (CALM), that offers a personalized educational experience. It allows the teachers to structure knowledge into interconnected competences, forming a competence graph for learners to navigate. The platform also employs an AI algorithm to select activities based on learners’ characteristics and needs. We propose two tools for teachers to optimize course design on Khipulearn. The first tool, a shortest path viewer, helps identify critical competences, providing control over essential knowledge of the course. The second tool visualizes the number of activities required for each competence, aiding in improving the efficiency on an adaptive activity selection. These tools aim to streamline the design process, ensuring teachers can leverage CALM’s adaptability and personalization principles without hindrance on the Khipulearn platform.