AI-enabled adaptive learning systems: A systematic mapping of the literature

Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences.publishedVersio

Artificial intelligence in education

The article is an excerpt from Wayne Holmes/ Maya Bialik/ Charles Fadel, Artificial Intelligence in Education : Promises and Implications for Teaching and Learning, The Center for Curriculum Redesign, Boston, 2019, 151-180 (ISBN-13: 978-1-794-29370-0). Abstract available from: https://discovery.ucl.ac.uk/id/eprint/10139722/).Reprinted with permission of the publisher

Artificial intelligence in education

The article is an excerpt from Wayne Holmes/ Maya Bialik/ Charles Fadel, Artificial Intelligence in Education : Promises and Implications for Teaching and Learning, The Center for Curriculum Redesign, Boston, 2019, 151-180 (ISBN-13: 978-1-794-29370-0). Abstract available from: https://discovery.ucl.ac.uk/id/eprint/10139722/). Reprinted with permission of the publisher

Towards Designing AI-Enabled Adaptive Learning Systems

Paper I, III, IV and V are not available as a part of the dissertation due to the copyright.Among the many innovations driven by artificial intelligence (AI) are more advanced learning systems known as AI-enabled adaptive learning systems (AI-ALS). AI-ALS are platforms that adapt to the learning strategies of students by modifying the order and difficulty level of learning tasks based on the abilities of students. These systems support adaptive learning, which is the personalization of learning for students in a learning system, such that the system can deal with individual differences in aptitude. AI-ALS are gaining traction due to their ability to deliver learning content and adapt to individual student needs. While the potential and importance of such systems have been well documented, the actual implementation of AI-ALS and other AI-based learning systems in real-world teaching and learning settings has not reached the effectiveness envisaged on the level of theory. Moreover, AI-ALS lack transferable insights and codification of knowledge on their design and development. The reason for this is that many previous studies were experimental. Thus, this dissertation aims to narrow the gap between experimental research and field practice by providing practical design statements that can be implemented in effective AI-ALSs.publishedVersio

Data ethics : building trust : how digital technologies can serve humanity

Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century: For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car, from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad, for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world? How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication. The authors and institutions come from all continents. The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust! The book is a continuation of the volume “Cyber Ethics 4.0” published in 2018 by the same editors

Presented Abstracts from the Thirty Third Annual Education Conference of the National Society of Genetic Counselors (New Orleans, LA, September 2014)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146864/1/jgc41067.pd

Bowdoin Orient v.91, no.1-19 (1961-1962)

https://digitalcommons.bowdoin.edu/bowdoinorient-1960s/1002/thumbnail.jp