.png){kind=icon}
.png){kind=icon}
Return
Personalized curricula through human-AI collaboration: Open learning platform development and validation from two experiments on knowledge acquisition
.png){kind=icon}
OA
AI
A
M
M
S
G
DOI：10.1016/j.heliyon.2026.e45120.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper aims to contribute to the discussion on open and free online Educational Resources for informal learners by describing the development and validation of a personalized, Artificial Intelligence (AI) driven learning recommendation platform. Specifically, we created an AI-based process to efficiently develop high-quality curricula that are subsequently tailored to the individual learning contexts of learners. The quality assurance of curricula was accomplished through a hybrid, human-AI content co-curation methodology. The curricula and their underlying educational resources were delivered to learners through a personalized learning dashboard. We validated the effectiveness of these methods and components through two pre-post randomized experiments, in which the knowledge acquisition of learners using our learning recommender system prototype was compared to that of learners using the open-source Moodle Learning Management System. The validation focused on two distinct subject areas: Python programming (n = 122) and stress management (n = 127). The results of these experiments not only showed that AI-supported content curators generated higher quality curricula across the two learning areas (in terms of coverage and conciseness) but also that learners in both courses, who engaged with our dashboard, exhibited greater knowledge acquisition relative to those who did so through the Moodle-based platform.
Keywords:
Personalized education
Artificial intelligence
Crowdsourcing
Open educational resources
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
3.8W
Citations:
10.5W
Organization
Cited Papers