{"title":"通过基于语言的生成技术推进整体教育目标的实现","authors":"Miguel Nussbaum , Zvi Bekerman","doi":"10.1016/j.lindif.2024.102600","DOIUrl":null,"url":null,"abstract":"<div><div>We explore the transformative potential of generative language-based technologies in educational reform, moving beyond traditional cognitive transmission towards a more holistic and relational learning paradigm. Through Humberto Maturana's theoretical lens, we examine how generative AI can facilitate dynamic, learner-centred environments that emphasize relational understanding and structural coupling. We critique the prevailing focus on cognitive training and advocate for integrating the embodied, interactive nature of learning—encompassing both verbal and non-verbal communication—into educational practices. Generative language-based technologies are positioned as key tools for reshaping educational practices, enabling learners to transcend the constraints of present educational paradigms and foster a more integrated understanding of knowledge. By emphasizing social interactions and environmental engagement, generative language-based technologies promote more meaningful communication and connections. We also address significant challenges these technologies present, including risks to educational equity, ethical concerns, and the potential erosion of cognitive autonomy through over-reliance on AI tools.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"117 ","pages":"Article 102600"},"PeriodicalIF":3.8000,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advancing holistic educational goals through generative language-based technologies\",\"authors\":\"Miguel Nussbaum , Zvi Bekerman\",\"doi\":\"10.1016/j.lindif.2024.102600\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>We explore the transformative potential of generative language-based technologies in educational reform, moving beyond traditional cognitive transmission towards a more holistic and relational learning paradigm. Through Humberto Maturana's theoretical lens, we examine how generative AI can facilitate dynamic, learner-centred environments that emphasize relational understanding and structural coupling. We critique the prevailing focus on cognitive training and advocate for integrating the embodied, interactive nature of learning—encompassing both verbal and non-verbal communication—into educational practices. Generative language-based technologies are positioned as key tools for reshaping educational practices, enabling learners to transcend the constraints of present educational paradigms and foster a more integrated understanding of knowledge. By emphasizing social interactions and environmental engagement, generative language-based technologies promote more meaningful communication and connections. We also address significant challenges these technologies present, including risks to educational equity, ethical concerns, and the potential erosion of cognitive autonomy through over-reliance on AI tools.</div></div>\",\"PeriodicalId\":48336,\"journal\":{\"name\":\"Learning and Individual Differences\",\"volume\":\"117 \",\"pages\":\"Article 102600\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-11-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Learning and Individual Differences\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1041608024001936\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, EDUCATIONAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Learning and Individual Differences","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1041608024001936","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EDUCATIONAL","Score":null,"Total":0}
Advancing holistic educational goals through generative language-based technologies
We explore the transformative potential of generative language-based technologies in educational reform, moving beyond traditional cognitive transmission towards a more holistic and relational learning paradigm. Through Humberto Maturana's theoretical lens, we examine how generative AI can facilitate dynamic, learner-centred environments that emphasize relational understanding and structural coupling. We critique the prevailing focus on cognitive training and advocate for integrating the embodied, interactive nature of learning—encompassing both verbal and non-verbal communication—into educational practices. Generative language-based technologies are positioned as key tools for reshaping educational practices, enabling learners to transcend the constraints of present educational paradigms and foster a more integrated understanding of knowledge. By emphasizing social interactions and environmental engagement, generative language-based technologies promote more meaningful communication and connections. We also address significant challenges these technologies present, including risks to educational equity, ethical concerns, and the potential erosion of cognitive autonomy through over-reliance on AI tools.
期刊介绍:
Learning and Individual Differences is a research journal devoted to publishing articles of individual differences as they relate to learning within an educational context. The Journal focuses on original empirical studies of high theoretical and methodological rigor that that make a substantial scientific contribution. Learning and Individual Differences publishes original research. Manuscripts should be no longer than 7500 words of primary text (not including tables, figures, references).