利用人工智能将经典学术课程转化为自适应学习流程的架构

IF 2.6 4区 经济学 Q3 BUSINESS
Andrei Bobocea, Razvan Bologa, Lorena Batagan, Bogdan-Stefan Posedaru
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引用次数: 0

摘要

有关自适应学习的文献表明,自适应学习可以极大地改善教育过程,许多研究也发现了个性化学习的必要性,而这正是自适应学习的强项之一。自适应学习平台需要有效的内容,而内容的缺乏阻碍了平台的大规模应用,因为内容创建成本会增加前期实施成本,并产生 "临界质量 "类型的问题,即没有内容的平台既无效又缺乏吸引力,从而导致用户缺乏兴趣,也缺乏开发新内容的资金。人工智能(AI)技术有可能通过承担大部分工作量来帮助内容创作。本文旨在探索这种可能性,并提出一种基于当前人工智能技术的架构,帮助教师和专家将经典课程材料转化为自适应学习流。该系统不是自主的,不会取代人类专家,而是会承担一些更直接但更耗时的工作。所建议的方法会产生一个独立于自适应学习平台本身的独特系统,它可以帮助重新措辞、重组和丰富内容,从而产生一个自动化的数字叙事或其片段,该数字叙事或其片段可以基于开放标准的格式导出,并在所选择的自适应学习平台中使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Architecture to Transform Classic Academic Courses into Adaptive Learning Flows with Artificial Intelligence
The literature on adaptive learning suggests that it can provide significant improvements to the educational process and numerous studies have found a necessity for personalised learning, which is one of the strong suits of adaptive learning. Adaptive learning platforms require that content be effective, and lack thereof has hindered large-scale adoption by adding the cost of content creation to the upfront implementation cost and creating a 'critical mass' type problem where a platform without content is ineffective and unattractive, leading to lack of interest from users and lack of funding for developing new content. Artificial intelligence (AI) technology has the potential to aid in content creation by taking on a significant part of the workload. This paper aims to explore this possibility and propose an architecture based on current artificial intelligence technologies that will help teachers and experts transform classic course materials into adaptive learning flows. The system is not autonomous and will not replace a human expert but rather will take on some of the more straightforward, but time-consuming, work. The proposed approach results in a distinct system, independent of the adaptive learning platform itself, that can help rephrase, restructure and enrich the content, resulting in an automated digital narrative, or fragment thereof, that can be exported in a format based on open standards and used within an adaptive learning platform of choice.
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来源期刊
CiteScore
4.20
自引率
3.80%
发文量
55
审稿时长
12 weeks
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