This paper synthesizes recent developments in intelligent textbooks over the last five years and identifies potential research areas of interest to the AIED community. It characterizes traits that make a textbook intelligent. It discusses hot spots in the AIED community such as a) the prediction of academic performance based on students’ reading behaviors, b) the assessment of learner skills based on their reading behaviors, and c) the automatic extraction of concepts taught in textbooks and their interdependencies (e.g., prerequisite, outcome, currency). It highlights key components of adaptivity that lead to full-fledged personalization and advocates the need for intelligent adaptivity as a trade-off between personalized provision of reading/learning materials and development and measurement of self-regulatory traits and grit. It concludes with a proposal to embed observational research methods as part of intelligent e-textbooks to automatically and continually infer causality between reading habits, reading activities, subject-matter competences, and metacognitive competences.
|Number of pages||13|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2019|
|Event||1st Workshop on Intelligent Textbooks, iText 2019 - Chicago, United States|
Duration: 25 Jun. 2019 → …
- Artificial intelligence
- Observational data