Publication details
Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Periodical |
| Magazine / Source | International Journal of Artificial Intelligence in Education |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/s40593-024-00400-6 |
| Keywords | Adaptive learning; Student modeling; Domain modeling; Trade-offs |
| Attached files | |
| Description | 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. |