Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling

PELÁNEK, Radek. Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling. International Journal of Artificial Intelligence in Education. NEW YORK: SPRINGER, 2024, 26 pp. ISSN 1560-4292. Available from: https://dx.doi.org/10.1007/s40593-024-00400-6.

Basic information

• Original name: Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling
• Authors: PELÁNEK, Radek.
• Edition: International Journal of Artificial Intelligence in Education, NEW YORK, SPRINGER, 2024, 1560-4292.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• Impact factor: Impact factor: 4.900 in 2022
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1007/s40593-024-00400-6
• UT WoS: 001190058700001
• Keywords in English: Adaptive learning; Student modeling; Domain modeling; Trade-offs
• Tags: International impact, Reviewed

Abstract

While the potential of personalized education has long been emphasized, the practical adoption of adaptive learning environments has been relatively slow. Discussion about underlying reasons for this disparity often centers on factors such as usability, the role of teachers, or privacy concerns. Although these considerations are important, I argue that a key factor contributing to this relatively slow progress is the inherent complexity of developing adaptive learning environments. I focus specifically on the modeling techniques that provide the foundation for adaptive behavior. The design of these models presents us with numerous challenges, nuances, and trade-offs. Awareness of these challenges is essential for guiding our efforts, both in the practical development of our systems and in our research endeavors.