Digital Competencies for Effective GenAI Use in Secondary Schools: A Longitudinal Exploration of Teachers' Perspectives and Classroom Practices.
Background: The rapid rise of Generative Artificial Intelligence (GenAI) is transforming education. At this stage, there is not yet a well‐established and tested theoretical framework clarifying which competencies learners must possess to use GenAI effectively and safely. This study draws on three t...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 5; pp. 1 - 19 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188234220&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188234220 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Oct2025 vid: 41 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 188234220 188234220 188234220 10.1111/jcal.70123 188234220 ppf: 1 ppct: 18 formats: tig: atl: Digital Competencies for Effective GenAI Use in Secondary Schools: A Longitudinal Exploration of Teachers' Perspectives and Classroom Practices. aug: au: Levy‐Nadav, Liron Shamir‐Inbal, Tamar Blau, Ina affil: Department of Education and Psychology, The Open University of Israel, Ra'anana, Israel sug: subj: Digital Technology Utilization Artificial Intelligence, Generative Utilization Schools, Secondary Faculty Attitudes Evaluation Teachers Psychosocial Factors Teaching Methods Learning Methods Human Male Female Prospective Studies Semi-Structured Interview Multimethod Studies Thematic Analysis Comparative Studies Pretest-Posttest Design Computer Literacy User-Computer Interface Critical Thinking Interrater Reliability Conceptual Framework Male Female ab: Background: The rapid rise of Generative Artificial Intelligence (GenAI) is transforming education. At this stage, there is not yet a well‐established and tested theoretical framework clarifying which competencies learners must possess to use GenAI effectively and safely. This study draws on three theoretical frameworks: Eshet‐Alkalai's Digital Literacy Framework (2012), Long and Magerko's AI Literacy (2020) and DigComp 2.2. Objectives: The study aims to explore teachers' perspectives regarding the digital competencies that students need for effective GenAI tool use, as well as the classroom learning activities that facilitate the development and practice of these competencies and how they evolve over time. Methods: We conducted 34 semi‐structured interviews at an interval of 6–8 months with 17 teachers who had begun utilising a variety of GenAI tools for teaching purposes. This mixed‐methods study combines qualitative top‐down and bottom‐up thematic analysis with quantitative comparisons between the pre and post measurements. Additionally, for data triangulation, a large qualitative sample of 97 GenAI‐enhanced learning activities conducted in classrooms was analysed. Results and Conclusions: Findings revealed seven key competencies categorised into general digital literacies, emerging technology literacies and GenAI‐specific competencies. Three categories of classroom activities that practiced these competencies emerged: proper use of tools, fostering personal expression and encouraging critical discussion. As expected in the beginning of new technology integration, declines were observed in most competencies in both interviews and learning activities, except for a significant increase in critical‐thinking competencies and stable levels of managing‐ongoing dialogue. The findings suggest expanding the digital literacy framework by incorporating 'Learning New Technology', which covers general competencies for integrating emerging technologies, and specific GenAI‐related competencies. The study offers valuable insights into theory and practice AI‐enhanced learning. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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