Impact of AI assistance on student agency

IF 8.9 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Education Pub Date : 2023-11-30 DOI:10.1016/j.compedu.2023.104967
Ali Darvishi , Hassan Khosravi , Shazia Sadiq , Dragan Gašević , George Siemens
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引用次数: 0

Abstract

AI-powered learning technologies are increasingly being used to automate and scaffold learning activities (e.g., personalised reminders for completing tasks, automated real-time feedback for improving writing, or recommendations for when and what to study). While the prevailing view is that these technologies generally have a positive effect on student learning, their impact on students’ agency and ability to self-regulate their learning is under-explored. Do students learn from the regular, detailed and personalised feedback provided by AI systems, and will they continue to exhibit similar behaviour in the absence of assistance? Or do they instead continue to rely on AI assistance without learning from it? To contribute to filling this research gap, we conducted a randomised controlled experiment that explored the impact of AI assistance on student agency in the context of peer feedback. With 1625 students across 10 courses, an experiment was conducted using peer review. During the initial four-week period, students were guided by AI features that utilised techniques such as rule-based suggestion detection, semantic similarity, and comparison with previous comments made by the reviewer to enhance their submissions if the feedback provided was deemed insufficiently detailed or general in nature. Over the following four weeks, students were divided into four different groups: control (AI) received prompts, (NR) received no prompts, (SR) received self-monitoring checklists in place of AI prompts, and (SAI) had access to both AI prompts and self-monitoring checklists. Results of the experiment suggest that students tended to rely on rather than learn from AI assistance. If AI assistance was removed, self-regulated strategies could help fill the gap but were not as effective as AI assistance. Results also showed that hybrid human-AI approaches that complement AI assistance with self-regulated strategies (SAI) were not more effective than AI assistance on its own. We conclude by discussing the broader benefits, challenges and implications of relying on AI assistance in relation to student agency in a world where we learn, live and work with AI.

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人工智能对学生代理的影响
人工智能学习技术越来越多地被用于自动化和支撑学习活动(例如,完成任务的个性化提醒,提高写作的自动实时反馈,或关于何时学习和学习内容的建议)。虽然普遍的观点是这些技术通常对学生的学习有积极的影响,但它们对学生的能动性和自我调节学习能力的影响尚未得到充分的探讨。学生是否能从人工智能系统提供的定期、详细和个性化的反馈中学习?在没有帮助的情况下,他们是否会继续表现出类似的行为?还是继续依赖人工智能的帮助而不从中学习?为了填补这一研究空白,我们进行了一项随机对照实验,探索人工智能在同伴反馈背景下对学生代理的影响。1625名学生参加了10门课程,采用同行评议的方式进行了一项实验。在最初的四周时间里,学生们受到人工智能功能的指导,这些功能利用了诸如基于规则的建议检测、语义相似性以及与审稿人之前的评论进行比较等技术,以便在提供的反馈被认为不够详细或不够笼统的情况下增强他们的提交。在接下来的四周里,学生们被分为四个不同的组:对照组(AI)收到提示,(NR)没有收到提示,(SR)收到代替AI提示的自我监控清单,(SAI)同时获得AI提示和自我监控清单。实验结果表明,学生倾向于依赖人工智能的帮助,而不是从人工智能中学习。如果人工智能援助被取消,自我调节的策略可以帮助填补空白,但不如人工智能援助有效。结果还表明,将人工智能辅助与自我调节策略(SAI)相结合的混合人类-人工智能方法并不比人工智能辅助本身更有效。最后,我们讨论了在一个我们与人工智能一起学习、生活和工作的世界中,依赖人工智能援助与学生代理相关的更广泛的好处、挑战和影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Education
Computers & Education 工程技术-计算机:跨学科应用
CiteScore
27.10
自引率
5.80%
发文量
204
审稿时长
42 days
期刊介绍: Computers & Education seeks to advance understanding of how digital technology can improve education by publishing high-quality research that expands both theory and practice. The journal welcomes research papers exploring the pedagogical applications of digital technology, with a focus broad enough to appeal to the wider education community.
期刊最新文献
Editorial Board From experience to empathy: An empathetic VR-based learning approach to improving EFL learners’ empathy and writing performance Bridging computer and education sciences: A systematic review of automated emotion recognition in online learning environments Navigating elementary EFL speaking skills with generative AI chatbots: Exploring individual and paired interactions Investigating behavioral and cognitive patterns among high-performers and low-performers in Co-viewing video lectures
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