Pub Date : 2024-07-05DOI: 10.1108/ils-10-2023-0160
Kong Chen, April C. Tallant, Ian Selig
Purpose Current knowledge and research on students’ utilization and interaction with generative artificial intelligence (AI) tools in their academic work is limited. This study aims to investigate students’ engagement with these tools. Design/methodology/approach This research used survey-based research to investigate generative AI literacy (utilization, interaction, evaluation of output and ethics) among students enrolled in a four-year public university in the southeastern USA. This article focuses on the respondents who have used generative AI (218; 47.2%). Findings Most respondents used generative AI to generate ideas for papers, projects or assignments, and they also used AI to assist with their original ideas. Despite their use of AI assistance, most students were critical of generative AI output, and this mindset was reflected in their reported interactions with ChatGPT. Respondents expressed a need for explicit guidance from course syllabi and university policies regarding generative AI’s ethical and appropriate use. Originality/value Literature related to generative AI use in higher education specific to ChatGPT is predominantly from educators’ viewpoints. This study provides empirical evidence about how university students report using generative AI in the context of generative AI literacy.
{"title":"Exploring generative AI literacy in higher education: student adoption, interaction, evaluation and ethical perceptions","authors":"Kong Chen, April C. Tallant, Ian Selig","doi":"10.1108/ils-10-2023-0160","DOIUrl":"https://doi.org/10.1108/ils-10-2023-0160","url":null,"abstract":"Purpose\u0000Current knowledge and research on students’ utilization and interaction with generative artificial intelligence (AI) tools in their academic work is limited. This study aims to investigate students’ engagement with these tools.\u0000\u0000Design/methodology/approach\u0000This research used survey-based research to investigate generative AI literacy (utilization, interaction, evaluation of output and ethics) among students enrolled in a four-year public university in the southeastern USA. This article focuses on the respondents who have used generative AI (218; 47.2%).\u0000\u0000Findings\u0000Most respondents used generative AI to generate ideas for papers, projects or assignments, and they also used AI to assist with their original ideas. Despite their use of AI assistance, most students were critical of generative AI output, and this mindset was reflected in their reported interactions with ChatGPT. Respondents expressed a need for explicit guidance from course syllabi and university policies regarding generative AI’s ethical and appropriate use.\u0000\u0000Originality/value\u0000Literature related to generative AI use in higher education specific to ChatGPT is predominantly from educators’ viewpoints. This study provides empirical evidence about how university students report using generative AI in the context of generative AI literacy.\u0000","PeriodicalId":504986,"journal":{"name":"Information and Learning Sciences","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-05DOI: 10.1108/ils-10-2023-0135
Priya Sharma, Jose Sandoval-Llanos, Daniel Foster, Melanie J Miller Foster
Purpose This study aims to examine the role of key network actors in relation to the discourse structure of a microblogging hashtag stream within a global agricultural educators’ conference over two years. Prior work in online networks suggests that participation is dominated by highly active members, and in this study, the authors focus on examining what types of discourse are shared and reshared by key actors. Design/methodology/approach The authors used a combination of social network analyses and qualitative discourse coding to examine approximately 1,390 posts associated with the conference hashtag over two consecutive years. Findings The study analyses uncovered a set of common key participants over both years and common types of discourse used by those key participants. Key participants took on roles of resharing messages and contributed to discourse by retweeting posts that highlighted participants’ thoughts and feelings related to the conference and the discipline. Research limitations/implications This research has implications for encouraging diverse participants and diverse discourses related to key community goals. Design suggestions include identifying and inviting key actors as collaborators to reshare discourse that clearly aligns with community goals and using smaller hashtag spaces to encourage broader participation. Originality/value Prior work on microblogging has highlighted either the types of discourse and information sharing or the structures of the network interactions within conference hashtag streams. This study builds on this prior work and combines discourse and structure to understand the ways in which key network figures reshare discourse within the community, a facet that has been underreported in the literature.
{"title":"The role of key actors in relation to discourse in a microblogging hashtag stream","authors":"Priya Sharma, Jose Sandoval-Llanos, Daniel Foster, Melanie J Miller Foster","doi":"10.1108/ils-10-2023-0135","DOIUrl":"https://doi.org/10.1108/ils-10-2023-0135","url":null,"abstract":"\u0000Purpose\u0000This study aims to examine the role of key network actors in relation to the discourse structure of a microblogging hashtag stream within a global agricultural educators’ conference over two years. Prior work in online networks suggests that participation is dominated by highly active members, and in this study, the authors focus on examining what types of discourse are shared and reshared by key actors.\u0000\u0000\u0000Design/methodology/approach\u0000The authors used a combination of social network analyses and qualitative discourse coding to examine approximately 1,390 posts associated with the conference hashtag over two consecutive years.\u0000\u0000\u0000Findings\u0000The study analyses uncovered a set of common key participants over both years and common types of discourse used by those key participants. Key participants took on roles of resharing messages and contributed to discourse by retweeting posts that highlighted participants’ thoughts and feelings related to the conference and the discipline.\u0000\u0000\u0000Research limitations/implications\u0000This research has implications for encouraging diverse participants and diverse discourses related to key community goals. Design suggestions include identifying and inviting key actors as collaborators to reshare discourse that clearly aligns with community goals and using smaller hashtag spaces to encourage broader participation.\u0000\u0000\u0000Originality/value\u0000Prior work on microblogging has highlighted either the types of discourse and information sharing or the structures of the network interactions within conference hashtag streams. This study builds on this prior work and combines discourse and structure to understand the ways in which key network figures reshare discourse within the community, a facet that has been underreported in the literature.\u0000","PeriodicalId":504986,"journal":{"name":"Information and Learning Sciences","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-04DOI: 10.1108/ils-11-2023-0170
Jung Won Hur
Purpose This study aims to investigate how preservice teachers’ stages of concern, beliefs, confidence and interest in AI literacy education evolve as they deepen their understanding of AI concepts and AI literacy education. Design/methodology/approach AI literacy lessons were integrated into a technology integration course for preservice teachers, and the impacts of the lessons were evaluated through a mixed-methods study. The Concerns-Based Adoption Model was employed as the analytical framework to explore participants’ specific concerns related to AI. Findings Findings revealed that participants initially lacked AI knowledge and awareness. However, targeted AI literacy education enhanced preservice teachers’ awareness and confidence in teaching AI. While acknowledging AI’s educational benefits, participants expressed ongoing concerns after AI literacy lessons, such as fears of teacher displacement and the potential adverse effects of incorporating generative AI on students’ critical learning skills development. Originality/value Despite the importance of providing preservice teachers with AI literacy skills and knowledge, research in this domain remains scarce. This study fills this gap by enhancing the AI-related knowledge and skills of future educators, while also identifying their specific concerns regarding the integration of AI into their future classrooms. The findings of this study offer valuable insights and guidelines for teacher educators to incorporate AI literacy education into teacher training programs.
{"title":"Fostering AI literacy: overcoming concerns and nurturing confidence among preservice teachers","authors":"Jung Won Hur","doi":"10.1108/ils-11-2023-0170","DOIUrl":"https://doi.org/10.1108/ils-11-2023-0170","url":null,"abstract":"\u0000Purpose\u0000This study aims to investigate how preservice teachers’ stages of concern, beliefs, confidence and interest in AI literacy education evolve as they deepen their understanding of AI concepts and AI literacy education.\u0000\u0000\u0000Design/methodology/approach\u0000AI literacy lessons were integrated into a technology integration course for preservice teachers, and the impacts of the lessons were evaluated through a mixed-methods study. The Concerns-Based Adoption Model was employed as the analytical framework to explore participants’ specific concerns related to AI.\u0000\u0000\u0000Findings\u0000Findings revealed that participants initially lacked AI knowledge and awareness. However, targeted AI literacy education enhanced preservice teachers’ awareness and confidence in teaching AI. While acknowledging AI’s educational benefits, participants expressed ongoing concerns after AI literacy lessons, such as fears of teacher displacement and the potential adverse effects of incorporating generative AI on students’ critical learning skills development.\u0000\u0000\u0000Originality/value\u0000Despite the importance of providing preservice teachers with AI literacy skills and knowledge, research in this domain remains scarce. This study fills this gap by enhancing the AI-related knowledge and skills of future educators, while also identifying their specific concerns regarding the integration of AI into their future classrooms. The findings of this study offer valuable insights and guidelines for teacher educators to incorporate AI literacy education into teacher training programs.\u0000","PeriodicalId":504986,"journal":{"name":"Information and Learning Sciences","volume":" 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-02DOI: 10.1108/ils-10-2023-0165
Julia Jochim, Vera Lenz-Kesekamp
Purpose Large language models such as ChatGPT are a challenge to academic principles, calling into question well-established practices, teaching and exam formats. This study aims to explore the adaptation process regarding text-generative artificial intelligence (AI) of students and teachers in higher education and to identify needs for change. Design/methodology/approach The issue is explored in a mixed-methods approach based on Domestication Theory (Silverstone et al., 1992; Silverstone, 1994), incorporating views of both teaching staff and students. Both statistical and content analyses were carried out. Findings The results show that both students and teachers are conflicted about generative AI and its usage. Trepidation and fear stand against a general feeling that AI is an integral part of the future and needs to be embraced. Both groups show marked needs for training and rules and offer a variety of ideas for new exam formats. Originality/value This study provides a unique insight by exploring the attitudes and usage intentions regarding generative AI of two stakeholder groups: students and teachers. Its results can be of significant use to institutions deciding on their strategy regarding AI. It illustrates attitudes and usage intentions as well as needs of both groups. In addition, ideas for new assessment and teaching formats were generated.
目的 像 ChatGPT 这样的大型语言模型是对学术原则的挑战，对既定的实践、教学和考试形式提出了质疑。本研究旨在探讨高等教育中学生和教师对文本生成人工智能（AI）的适应过程，并确定变革需求。设计/方法/途径本研究以驯化理论（Domestication Theory，Silverstone et al.研究结果表明，学生和教师对生成式人工智能及其使用都存在矛盾。人们普遍认为，人工智能是未来不可或缺的一部分，必须加以拥抱。两个群体都对培训和规则表现出明显的需求，并对新的考试形式提出了各种想法。 原创性/价值 本研究通过探讨学生和教师这两个利益相关群体对生成式人工智能的态度和使用意图，提供了独特的见解。研究结果对院校决定其人工智能战略具有重要意义。它说明了两个群体的态度和使用意向以及需求。此外，还提出了新的评估和教学形式的想法。
{"title":"Teaching and testing in the era of text-generative AI: exploring the needs of students and teachers","authors":"Julia Jochim, Vera Lenz-Kesekamp","doi":"10.1108/ils-10-2023-0165","DOIUrl":"https://doi.org/10.1108/ils-10-2023-0165","url":null,"abstract":"\u0000Purpose\u0000Large language models such as ChatGPT are a challenge to academic principles, calling into question well-established practices, teaching and exam formats. This study aims to explore the adaptation process regarding text-generative artificial intelligence (AI) of students and teachers in higher education and to identify needs for change.\u0000\u0000\u0000Design/methodology/approach\u0000The issue is explored in a mixed-methods approach based on Domestication Theory (Silverstone et al., 1992; Silverstone, 1994), incorporating views of both teaching staff and students. Both statistical and content analyses were carried out.\u0000\u0000\u0000Findings\u0000The results show that both students and teachers are conflicted about generative AI and its usage. Trepidation and fear stand against a general feeling that AI is an integral part of the future and needs to be embraced. Both groups show marked needs for training and rules and offer a variety of ideas for new exam formats.\u0000\u0000\u0000Originality/value\u0000This study provides a unique insight by exploring the attitudes and usage intentions regarding generative AI of two stakeholder groups: students and teachers. Its results can be of significant use to institutions deciding on their strategy regarding AI. It illustrates attitudes and usage intentions as well as needs of both groups. In addition, ideas for new assessment and teaching formats were generated.\u0000","PeriodicalId":504986,"journal":{"name":"Information and Learning Sciences","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1108/ils-06-2023-0072
Leo Van Audenhove, Lotte Vermeire, Wendy Van den Broeck, Andy Demeulenaere
Purpose The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens. Design/methodology/approach This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data. Findings Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness. Originality/value Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.
{"title":"Data literacy in the new EU DigComp 2.2 framework how DigComp defines competences on artificial intelligence, internet of things and data","authors":"Leo Van Audenhove, Lotte Vermeire, Wendy Van den Broeck, Andy Demeulenaere","doi":"10.1108/ils-06-2023-0072","DOIUrl":"https://doi.org/10.1108/ils-06-2023-0072","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens.\u0000\u0000\u0000Design/methodology/approach\u0000This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data.\u0000\u0000\u0000Findings\u0000Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness.\u0000\u0000\u0000Originality/value\u0000Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.\u0000","PeriodicalId":504986,"journal":{"name":"Information and Learning Sciences","volume":" 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139792471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1108/ils-06-2023-0072
Leo Van Audenhove, Lotte Vermeire, Wendy Van den Broeck, Andy Demeulenaere
Purpose The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens. Design/methodology/approach This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data. Findings Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness. Originality/value Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.
{"title":"Data literacy in the new EU DigComp 2.2 framework how DigComp defines competences on artificial intelligence, internet of things and data","authors":"Leo Van Audenhove, Lotte Vermeire, Wendy Van den Broeck, Andy Demeulenaere","doi":"10.1108/ils-06-2023-0072","DOIUrl":"https://doi.org/10.1108/ils-06-2023-0072","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens.\u0000\u0000\u0000Design/methodology/approach\u0000This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data.\u0000\u0000\u0000Findings\u0000Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness.\u0000\u0000\u0000Originality/value\u0000Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.\u0000","PeriodicalId":504986,"journal":{"name":"Information and Learning Sciences","volume":"29 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139852380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-12DOI: 10.1108/ils-06-2023-0078
Sein Oh, Lorri M. Mon
Purpose By examining types of literacies taught by public libraries and the modes through which these programs were offered, this study aims to explore how public libraries might integrate data literacy training for the general public into existing library educational programs. Design/methodology/approach This study examined programs offered in 30 US public libraries during 2019 and 2020 to better understand types of literacy education announced to the public through library website listings and Facebook Events pages. Findings While public libraries offered educational programs in literacy areas ranging from basic reading and writing to technology, vocational skills, health literacy and more, data literacy training was not widely offered. However, this study identified many already-existing programs highly compatible for integrating with data literacy training. Originality/value This study offered new insights into both the literacies taught in public library programs as well as ways for public libraries to integrate data literacy training into existing educational programming, in order to better provide data literacy education for the general public.
{"title":"Community-based learning and data literacy: the role of the public library","authors":"Sein Oh, Lorri M. Mon","doi":"10.1108/ils-06-2023-0078","DOIUrl":"https://doi.org/10.1108/ils-06-2023-0078","url":null,"abstract":"\u0000Purpose\u0000By examining types of literacies taught by public libraries and the modes through which these programs were offered, this study aims to explore how public libraries might integrate data literacy training for the general public into existing library educational programs.\u0000\u0000\u0000Design/methodology/approach\u0000This study examined programs offered in 30 US public libraries during 2019 and 2020 to better understand types of literacy education announced to the public through library website listings and Facebook Events pages.\u0000\u0000\u0000Findings\u0000While public libraries offered educational programs in literacy areas ranging from basic reading and writing to technology, vocational skills, health literacy and more, data literacy training was not widely offered. However, this study identified many already-existing programs highly compatible for integrating with data literacy training.\u0000\u0000\u0000Originality/value\u0000This study offered new insights into both the literacies taught in public library programs as well as ways for public libraries to integrate data literacy training into existing educational programming, in order to better provide data literacy education for the general public.\u0000","PeriodicalId":504986,"journal":{"name":"Information and Learning Sciences","volume":"10 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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