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

Evolution of the Adoption of Generative AI Among Spanish Engineering Students
Faraón Llorens-Largo, Rafael Molina-Carmona, Alberto Real-Fernández, & Sergio Arjona-Giner
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/10.1007/978-3-031-61691-4_20

https://doi.org/10.1007/978-3-031-61691-4_20

Abstract
The irruption of ChatGPT has led to a technological change that could affect all sectors, concretely education. University students are facing this change, and the adoption of generative AI will be key to both their learning and job performance. This paper aims to study the adoption and evolution of generative AI among engineering students by analysing two surveys conducted in 2022 and 2023. The results show that engineering students are mostly aware and they are using generative AI tools. On the other hand, it has been studied whether gender can influence their adoption, and the results do not indicate significant changes. Regarding the evolution of the adoption of generative AI, a great change is shown, since it is being more used in all fields, also in education. This change indicates the attitude of engineering students to explore and take advantage of innovative tools, which can have a current and future impact on their learning and development.

Keywords
Generative AI · Technology Adoption · ChatGPT

Cite this paper as:
Llorens-Largo, F., Molina-Carmona, R., Real-Fernández, A., Arjona-Giner, S. (2024). Evolution of the Adoption of Generative AI Among Spanish Engineering Students. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. HCII 2024. Lecture Notes in Computer Science, vol 14724. Springer, Cham. https://doi.org/10.1007/978-3-031-61691-4_20