About this Research Topic
The question of how digital technologies and AI can improve personalized education systems is currently being widely explored. This trend is driven by a combination of rapid advances in AI and Ubiquitous/Wearable computing on one hand and on the other hand by increasing realization of both the need and the potential for fundamentally new approaches combining them with educational theories and practices.
Besides AI for problem-solving, researchers have been investigating AI in education (AIED) for a variety of other educational activities in more complex domains that can benefit from individualized support, such as learning from examples, learning by exploration or games, or learning by teaching. It has been shown that providing individualized support for these activities poses unique challenges: it requires AIED to model the activities as well as student behaviors, abilities, and states that may not be as well-defined, understood, or easily captured as those involved in problem-solving.
The goal of the Research Topic is to highlight its interdisciplinary nature of providing the audience with different views of the problem and showing how they lead to practicable real-world solutions. This article collection will focus on AI systems that flexibly support individual problem-solving strategies and how their development addresses misconceptions, and helps to overcome them. It will show how AI systems can help understand complex relationships in a way that is optimally adapted to the socio-cultural background, educational background, goals, and personal strengths of the learner.
Contributions should describe the support of knowledge acquisition, for example, for experiments in the field of STEM lab work courses, or for remote and virtual labs as well as for simulations and interactive textbooks in the area of distant learning. This Research Topic addresses a broad range of disciplines and subjects ranging from elementary schools up to university education. The contributions to the collection should empower teaching as well as self-directed learning, especially for intention. This can be done by analyzing when and how it is feasible e.g. to provide guidance in stepping from discovery learning towards theoretical understanding, to offer adapted help, to give context-sensitive feedback, or to offer special tasks and exercises.
Besides AI for problem-solving, researchers have been investigating AI in education (AIED) for a variety of other educational activities in more complex domains that can benefit from individualized support, such as learning from examples, learning by exploration or games, or learning by teaching. It has been shown that providing individualized support for these activities poses unique challenges: it requires AIED to model the activities as well as student behaviors, abilities, and states that may not be as well-defined, understood, or easily captured as those involved in problem-solving.
The goal of the Research Topic is to highlight its interdisciplinary nature of providing the audience with different views of the problem and showing how they lead to practicable real-world solutions. This article collection will focus on AI systems that flexibly support individual problem-solving strategies and how their development addresses misconceptions, and helps to overcome them. It will show how AI systems can help understand complex relationships in a way that is optimally adapted to the socio-cultural background, educational background, goals, and personal strengths of the learner.
Contributions should describe the support of knowledge acquisition, for example, for experiments in the field of STEM lab work courses, or for remote and virtual labs as well as for simulations and interactive textbooks in the area of distant learning. This Research Topic addresses a broad range of disciplines and subjects ranging from elementary schools up to university education. The contributions to the collection should empower teaching as well as self-directed learning, especially for intention. This can be done by analyzing when and how it is feasible e.g. to provide guidance in stepping from discovery learning towards theoretical understanding, to offer adapted help, to give context-sensitive feedback, or to offer special tasks and exercises.
Keywords: Digital Education, AI and Human Interaction
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
