Toward more effective and equitable learning: Identifying barriers and solutions for the future of online education.

Abstract

The increased reliance on online education and educational technologies more generally has laid bare the need to more deeply consider how researchers, designers, and educators can improve the quality of technology-mediated learning. To address this need, more than two dozen experts from a variety of fields came together to discuss the challenges that educational technology must address in the immediate future. These experts were tasked with identifying barriers to and potential solutions for delivering highquality and equitable online and remote education. This article examines the themes and topics that emerged from these discussions and proposes a Collaborative Framework for Accelerating Online Education. This framework highlights the need for rapid experimentation within larger design cycles as well as the coordination and cooperation of multiple stakeholders across all phases of research and development. The themes, topics, and framework that emerged from this work serve as a call to action for innovative approaches to developing and studying online education.

Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools

“…We critically discuss the opportunities and risks that AI could bring to education, in terms of supporting teachers and learners, as well as in enabling a more inclusive educational system. This goal has always been significant, yet rethinking the future of education is all the more important following the COVID-19 pandemic, which further widened educational inequality and put a spotlight on the lack of resilience of our educational systems [4,12]. Post-pandemic retrospectives provide further evidence on how the conventional school systems were severely disrupted in developing nations and how more resilient blended learning approaches (combining face-to-face and online learning seamlessly and in a complementary way) should be sought going forward [13].…”

“…One of the most ambitious use cases of AIEd, Intelligent Tutoring Systems (ITSs), has been shown to lead to similar learning gains to face-to-face one-on-one instruction in both individual experimental studies [16] and metareviews of the field [17], while personalised learning, in general, can improve the learning gains of an average student by the order of two standard deviations [18]. Despite this immense theoretical potential of personalised technology-enhanced education and a large amount of monetary investment in these learning technologies, such innovations have not delivered many practical results yet [12,19]. Less ambitious AIEd technologies (e.g., support tools for teachers) are, however, slowly starting to play a role in providing support to teachers, students, and the learning process more broadly [20,21].…”

Reading Comprehension and Constructive Learning: Policy Considerations in the Age of Artificial Intelligence

“…Corrective feedback (i.e., correct/incorrect) is relatively easy to provide, but is less effective for complex learning tasks than higher information feedback that offers suggestions and strategies on how to improve (Wisniewski et al, 2020). AI tools that go beyond corrective feedback, either through offering constructive feedback directly to learners or by recommending feedback messages for instructors to use, have effects on immediate performance and long-term learning (McCarthy et al, 2022).…”

Features of lexical complexity: insights from L1 and L2 speakers

“…With distance learning becoming ever more popular (Morris et al, 2020 ), research has been focusing on identifying barriers and improving approaches to online education (McCarthy et al, 2022 ). This includes an increase in the demand for AI/NLP technologies such as TS that can be utilized within computer-assisted language learning (CALL) applications (Tseng and Yeh, 2019 ; Rets and Rogaten, 2020 ).…”