Tools to Support the Design of Network-Structured Courses Assisted by AI
Juan-Luis López-Javaloyes, Alberto Real-Fernández, Javier García-Sigüenza, Faraón Llorens-Largo, & Rafael Molina-Carmona
26th International Conference on Human-Computer Interaction (HCII 2024)
Washington DC, USA
29 June – 4 July 2024
11th International Conference on Learning and Collaboration Technologies (LCT 2024).
Lecture Notes in Computer Science book series (LNCS,volume 14724) (https://link.springer.com/chapter/10.1007/978-3-031-61691-4)
https://link.springer.com/chapter/10.1007/978-3-031-61672-3_4
https://doi.org/10.1007/978-3-031-61672-3_4
Abstract
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.
Keywords
learning design · AI tools · graph structure
Cite this paper as:
López-Javaloyes, JL., Real-Fernández, A., García-Sigüenza, J., Llorens-Largo, F., Molina-Carmona, R. (2024). Tools to Support the Design of Network-Structured Courses Assisted by AI. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. HCII 2024. Lecture Notes in Computer Science, vol 14722. Springer, Cham. https://doi.org/10.1007/978-3-031-61672-3_4
