AI-Augmented Personalised Learning: Unleashing the Potential of Adaptive Learning Technologies in Education
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (31 July 2024)
Special Issue Editors
Interests: Berlin School of Business and Innovation, 12043 Berlin, Germany
Interests: innovation in education; e-learning; virtual learning
Interests: Li-Fi technology; data mining; computer science; AI and innovation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
The objective of this Special Issue is to delve into the promising realm of AI-driven adaptive learning technologies and their profound impact on personalising the learning experience. By gathering research and insights from diverse disciplines, we seek to explore the potential of AI to revolutionise traditional educational approaches, enhance student engagement, and optimise learning outcomes. This Special Issue will cover a wide range of topics related to AI in education, with a specific focus on personalised learning methodologies, learning analytics, ethical considerations, and the collaborative role of teachers and students in AI-enhanced learning environments.
Potential Topics and Subjects
We welcome original research articles, reviews, and case studies that contribute to the advancement of AI-Augmented Personalised Learning. Potential topics for submission include, but are not limited to:
Novel Approaches to Personalised Learning: investigating innovative AI-based methodologies for tailoring educational content and experiences to individual learner needs.
AI-Driven Learning Analytics: exploring how AI algorithms analyse learner data to provide valuable insights for educators and institutions, promoting data-informed decision-making in personalised learning paths.
The Role of AI in Adaptive Learning Platforms: examining the capabilities and limitations of AI-powered adaptive learning systems in catering to diverse learning styles and preferences.
Enhancing Student Engagement through AI: investigating how AI-powered learning environments can foster greater student motivation, participation, and enthusiasm for learning.
AI and Learner Assessment: analysing the integration of AI in formative and summative assessments to provide real-time feedback and support continuous improvement.
Dr. Farshad Badie
Prof. Dr. Kyriakos Kouveliotis
Dr. Duraisamy Balaganesh
Dr. Gregor Tkachov
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI-enhanced learning
- smart education
- learning analytics
- AI-driven adaptive learning
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