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

J 2025

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

PELÁNEK, Radek

Základní údaje

Originální název

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

Autoři

PELÁNEK, Radek

Vydání

International Journal of Artificial Intelligence in Education, NEW YORK, SPRINGER, 2025, 1560-4292

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 8.500 v roce 2024

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/25:00140231

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

Adaptive learning; Student modeling; Domain modeling; Trade-offs

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 1. 4. 2026 10:19, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Přiložené soubory

s40593-024-00400-6.pdf

Citovat

PELÁNEK, Radek. Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling. International Journal of Artificial Intelligence in Education. NEW YORK: SPRINGER, 2025, roč. 35, č. 1, s. 304-329. ISSN 1560-4292. Dostupné z: https://doi.org/10.1007/s40593-024-00400-6.

@article{2396017,
   author = {Pelánek, Radek},
   article_location = {NEW YORK},
   article_number = {1},
   doi = {https://doi.org/10.1007/s40593-024-00400-6},
   keywords = {Adaptive learning; Student modeling; Domain modeling; Trade-offs},
   language = {eng},
   issn = {1560-4292},
   journal = {International Journal of Artificial Intelligence in Education},
   title = {Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling},
   url = {http://dx.doi.org/10.1007/s40593-024-00400-6},
   volume = {35},
   year = {2025}
}

TY  - JOUR
ID  - 2396017
AU  - Pelánek, Radek
PY  - 2025
TI  - Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling
JF  - International Journal of Artificial Intelligence in Education
VL  - 35
IS  - 1
SP  - 304-329
EP  - 304-329
PB  - SPRINGER
SN  - 15604292
KW  - Adaptive learning
KW  - Student modeling
KW  - Domain modeling
KW  - Trade-offs
UR  - http://dx.doi.org/10.1007/s40593-024-00400-6
N2  - 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.
ER  -

PELÁNEK, Radek. Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling. \textit{International Journal of Artificial Intelligence in Education}. NEW YORK: SPRINGER, 2025, roč.~35, č.~1, s.~304-329. ISSN~1560-4292. Dostupné z: https://doi.org/10.1007/s40593-024-00400-6.

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