| Title: |
AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs. |
| Authors: |
Angeioplastis, Athanasios1 (AUTHOR); Konstantakis, Markos2 (AUTHOR) mkonstadakis@aegean.gr; Aliprantis, John1,2 (AUTHOR); Ordoumpozanis, Konstantinos2 (AUTHOR); Varsamis, Dimitrios1 (AUTHOR); Tsimpiris, Alkiviadis1 (AUTHOR) |
| Source: |
Information. Feb2026, Vol. 17 Issue 2, p216. 22p. |
| Subject Terms: |
*Educational technology; Learning management system; Web personalization; Instructional systems; Learning analytics; System integration; Inclusive education |
| Reviews & Products: |
Moodle (Computer software) |
| Abstract: |
Learning Management Systems (LMSs) remain largely static and administrative, often failing to support personalization and inclusive access to learning resources. This paper presents AI for All, a practical approach to building an adaptive, accessible, and inclusive learning experience within a mainstream LMS, demonstrated through the PREPARE project (Personalized Education Framework for AI-Enabled Adaptive and AR-Enhanced Learning) implemented in Moodle. PREPARE operationalizes an end-to-end generative AI pipeline that transforms a single authoritative PDF textbook into multimodal learning assets, including chapter summaries, structured notes and slide decks, formative quiz items, video mini-lectures with captions, podcast-style audio, and chapter-level augmented reality (AR) activities. In parallel, the system maintains a hybrid learner model by combining an initial FSLSM/ILS questionnaire with continuous behavior-based profiling derived from Moodle logs. Learner profiles drive non-prescriptive personalization through resource prioritization and recommendations, while preserving learner agency and access to all modalities. We describe the system architecture, Moodle integration mechanisms, and adaptation logic, and report an ongoing mixed-methods evaluation focusing on engagement, interaction diversity, perceived usefulness, and accessibility benefits. The system-level validation and deployment readiness suggest that AI-augmented LMS workflows can reduce instructor authoring effort while improving flexibility and inclusivity, provided that human-in-the-loop validation and privacy-aware analytics are embedded from the outset. [ABSTRACT FROM AUTHOR] |
| Database: |
Library, Information Science & Technology Abstracts |