{"title":"协同调节网络中的生成式人工智能学习","authors":"Jason Lodge, Paula de Barba, Jaclyn Broadbent","doi":"10.53761/1.20.7.02","DOIUrl":null,"url":null,"abstract":"The emergence of generative artificial intelligence (AI) has created legitimate concerns surrounding academic integrity and the ease with which such technologies might lead to cheating in assessment, in particular. However, fixating solely on potential misconduct is overshadowing a more profound, transformative interaction between learners and machines. This commentary article delves into the relationship between students and AI, aiming to highlight the need for revised pedagogical strategies in the AI age. We argue that the much-discussed approaches that prioritise AI literacy or augmented critical thinking might be inadequate. Instead, we contend that a more holistic approach emphasising self-regulated learning (SRL) and co-regulation of learning is needed. SRL promotes autonomy, adaptability, and a deeper understanding, qualities indispensable for navigating the intricacies of AI-enhanced learning environments. Furthermore, we introduce the notion of a network of co-regulation, which underscores the intertwined learning processes between humans and machines. By positioning the self at the core of this network, we emphasise the indispensable role of individual agency in steering productive human-AI educational interactions. Our contention is that by fostering SRL and understanding co-regulated dynamics, educators can better equip learners for an interconnected AI-driven world.","PeriodicalId":45764,"journal":{"name":"Journal of University Teaching and Learning Practice","volume":"7 4-5","pages":"0"},"PeriodicalIF":2.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning with Generative Artificial Intelligence Within a Network of Co-Regulation\",\"authors\":\"Jason Lodge, Paula de Barba, Jaclyn Broadbent\",\"doi\":\"10.53761/1.20.7.02\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The emergence of generative artificial intelligence (AI) has created legitimate concerns surrounding academic integrity and the ease with which such technologies might lead to cheating in assessment, in particular. However, fixating solely on potential misconduct is overshadowing a more profound, transformative interaction between learners and machines. This commentary article delves into the relationship between students and AI, aiming to highlight the need for revised pedagogical strategies in the AI age. We argue that the much-discussed approaches that prioritise AI literacy or augmented critical thinking might be inadequate. Instead, we contend that a more holistic approach emphasising self-regulated learning (SRL) and co-regulation of learning is needed. SRL promotes autonomy, adaptability, and a deeper understanding, qualities indispensable for navigating the intricacies of AI-enhanced learning environments. Furthermore, we introduce the notion of a network of co-regulation, which underscores the intertwined learning processes between humans and machines. By positioning the self at the core of this network, we emphasise the indispensable role of individual agency in steering productive human-AI educational interactions. Our contention is that by fostering SRL and understanding co-regulated dynamics, educators can better equip learners for an interconnected AI-driven world.\",\"PeriodicalId\":45764,\"journal\":{\"name\":\"Journal of University Teaching and Learning Practice\",\"volume\":\"7 4-5\",\"pages\":\"0\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of University Teaching and Learning Practice\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53761/1.20.7.02\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of University Teaching and Learning Practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53761/1.20.7.02","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Learning with Generative Artificial Intelligence Within a Network of Co-Regulation
The emergence of generative artificial intelligence (AI) has created legitimate concerns surrounding academic integrity and the ease with which such technologies might lead to cheating in assessment, in particular. However, fixating solely on potential misconduct is overshadowing a more profound, transformative interaction between learners and machines. This commentary article delves into the relationship between students and AI, aiming to highlight the need for revised pedagogical strategies in the AI age. We argue that the much-discussed approaches that prioritise AI literacy or augmented critical thinking might be inadequate. Instead, we contend that a more holistic approach emphasising self-regulated learning (SRL) and co-regulation of learning is needed. SRL promotes autonomy, adaptability, and a deeper understanding, qualities indispensable for navigating the intricacies of AI-enhanced learning environments. Furthermore, we introduce the notion of a network of co-regulation, which underscores the intertwined learning processes between humans and machines. By positioning the self at the core of this network, we emphasise the indispensable role of individual agency in steering productive human-AI educational interactions. Our contention is that by fostering SRL and understanding co-regulated dynamics, educators can better equip learners for an interconnected AI-driven world.
期刊介绍:
The Journal of University Teaching and Learning Practice aims to add significantly to the body of knowledge describing effective and innovative teaching and learning practice in higher education.The Journal is a forum for educational practitioners across a wide range of disciplines. Its purpose is to facilitate the communication of teaching and learning outcomes in a scholarly way, bridging the gap between journals covering purely academic research and articles and opinions published without peer review.