Proactive and reactive engagement of artificial intelligence methods for education: a review

Abstract

The education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and administrative staff alike. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled these efforts further. In this review article, we investigate how artificial intelligence, machine learning, and deep learning methods are being utilized to support the education process. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety—from students admissions, course scheduling, and content generation in the proactive planning phase to knowledge delivery, performance assessment, and outcome prediction in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 195 original research articles published in the past two decades, i.e., 2003–2022. We discuss the paradigm shifts in the solution approaches proposed, particularly with respect to the choice of data and algorithms used over this time. We further discuss how the COVID-19 pandemic influenced this field of active development and the existing infrastructural challenges and ethical concerns pertaining to global adoption of artificial intelligence for education.

A review of AI-driven pedagogical strategies for equitable access to science education

“…This includes ensuring that AI systems are accessible and inclusive for all students, regardless of their background or circumstances. For example, AI-driven platforms should provide multiple means of representation, engagement, and expression to accommodate diverse learning needs and preferences (Anis, 2023, Mallik & Gangopadhyay, 2023. Additionally, educators should be mindful of the potential for AI technologies to exacerbate existing inequities and actively work to address these disparities through targeted interventions and support.…”

Section 28. Trends in the Use of Information Technologies in Professional Training: Analysis of the Use of Cloud Technologies in Education

“…У четвертому кластері спостерігаємо знову максимальну вагу зв'язків (36) у «machine learning», що свідчить про наявні зв'язки з усіма ключовими словами усіх кластерів. А найменшу вагу (8) має «lidar» із зв'язком з першим кластером (edge computing) та п'ятим кластером (internet of things, sensors).…”

“…У іншій роботі 35 автори аналізують використання ChatGPT в освіті, відзначають неоднозначність сприйняття цього інструменту вчителями і водночас доводять, що поява ШІ вже вплинула на освітній простір, а тому потрібні більш глибокі дослідження його використання. Вплив ШІ, машинного навчання та методів глибокого навчання на організацію освітнього процесу (вступ до університету, складання розкладу і створення курсів) аналізовано у роботі 36 . За результатами аналізу 195 оригінальних наукових автори піднімають етичні проблеми використання штучного інтелекту в освіті.…”

Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence

“…This could allow students to practice these skills at home using avatars and AI chatbots for patient interviews, asking relevant questions and documenting responses. AI also enables immediate, personalized feedback for learners, likely resulting in more efficient and responsive learning [ 14 , 20 ].…”

“…Similar to cluster 1, AI applications in this cluster aim to augment the teaching process, hasten the learning pace, amplify practice opportunities, and facilitate learner competency assessment. These skills are essential for medical trainees before residency and AI could potentially expedite students' proficiency and entrustability achievement, enabling time-variable graduation into the health care workforce [20][21][22][23].…”