Artificial Intelligence Bringing Improvements to Adaptive Learning in Education: A Case Study

Claudio Giovanni Demartini (), Luciano Sciascia, Andrea Bosso and Federico Manuri
Additional contact information
Claudio Giovanni Demartini: Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Torino, Italy
Luciano Sciascia: Fondazione per la Scuola della Compagnia di San Paolo, Piazza Bernini, 5, 10138 Torino, Italy
Andrea Bosso: Links Foundation, Via Pier Carlo Boggio, 61, 10138 Torino, Italy
Federico Manuri: Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Torino, Italy

Sustainability, 2024, vol. 16, issue 3, 1-25

Abstract: Despite promising outcomes in higher education, the widespread adoption of learning analytics remains elusive in various educational settings, with primary and secondary schools displaying considerable reluctance to embrace these tools. This hesitancy poses a significant obstacle, particularly given the prevalence of educational technology and the abundance of data generated in these environments. In contrast to higher education institutions that readily integrate learning analytics tools into their educational governance, high schools often harbor skepticism regarding the tools’ impact and returns. To overcome these challenges, this work aims to harness learning analytics to address critical areas, such as school dropout rates, the need to foster student collaboration, improving argumentation and writing skills, and the need to enhance computational thinking across all age groups. The goal is to empower teachers and decision makers with learning analytics tools that will equip them to identify learners in vulnerable or exceptional situations, enabling educational authorities to take suitable actions that are aligned with students’ needs; this could potentially involve adapting learning processes and organizational structures to meet the needs of students. This work also seeks to evaluate the impact of such analytics tools on education within a multi-dimensional and scalable domain, ranging from individual learners to teachers and principals, and extending to broader governing bodies. The primary objective is articulated through the development of a user-friendly AI-based dashboard for learning. This prototype aims to provide robust support for teachers and principals who are dedicated to enhancing the education they provide within the intricate and multifaceted social domain of the school.

Keywords: education; learning analytics; artificial intelligence; adaptive learning; machine learning; dashboard (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/3/1347/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/3/1347/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:3:p:1347-:d:1333887

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().