<?xml version="1.0" encoding="UTF-8"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-10-30T06:37:41Z</responseDate><request verb="GetRecord" identifier="oai:bayburt-rdlopen.rawdatalibrary.net:bayburt/11292" metadataPrefix="oai_dc">https://bayburt-rdlopen.rawdatalibrary.net/oai/request</request><GetRecord><record><header><identifier>oai:bayburt-rdlopen.rawdatalibrary.net:bayburt/11292</identifier><datestamp>2026-10-29T19:41:30Z</datestamp><setSpec>openaire</setSpec><setSpec>driver</setSpec><setSpec>com_bayburt_10000</setSpec><setSpec>col_bayburt_10001</setSpec><setSpec>com_bayburt_10010</setSpec><setSpec>col_bayburt_10224</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:title>Examining the relationships between artificial intelligence literacy, AI-TPACK, job satisfaction, and well-being among teachers through structural equation modeling</dc:title><dc:creator>Kayalı, Bünyami</dc:creator><dc:creator>Balat, Şener</dc:creator><dc:creator>Yavuz, Mehmet</dc:creator><dc:subject>AI-TPACK</dc:subject><dc:subject>Artificial intelligence literacy</dc:subject><dc:subject>structural equation modeling</dc:subject><dc:subject>teacher satisfaction</dc:subject><dc:subject>teacher well-being</dc:subject><dc:description>This study explores the relationships between teachers' artificial intelligence literacy (AIL), AI-TPACK competencies, job satisfaction, and well-being. Survey data from 350 in-service teachers were analyzed using PLS-SEM to examine how AI-related competencies are associated with professional and psychological outcomes. The findings reveal that AIL strongly predicts AI-specific knowledge, especially AI-TCK, AI-TK, AI-TPCK, and AI-TPK, with a moderate influence on traditional pedagogical knowledge. At the job satisfaction level, AI-TCK and general pedagogical knowledge are positively associated with satisfaction, while AI-TK has a negative relationship. Mediation analyses show that AI competencies influence well-being through job satisfaction. AI-TCK and pedagogical knowledge are positively associated with well-being indirectly through job satisfaction, whereas AI-TK shows a negative indirect association along the same pathway. The study suggests that AI-related competencies are linked to teachers' work-life outcomes, and highlights job satisfaction as the key mediator between AI competence and well-being. Because the design is cross-sectional and self-reported, these relationships are interpreted as associations rather than causal effects. The study advocates for integrating AI in ways that support pedagogical development rather than just technical skills.</dc:description><dc:publisher>Elsevier B.V.</dc:publisher><dc:date>2026</dc:date><dc:type>Article</dc:type><dc:identifier>https://rdlopen.rawdatalibrary.net/r/bayburt/11292</dc:identifier><dc:identifier>https://doi.org/10.1016/j.actpsy.2026.107297</dc:identifier><dc:identifier>0001-6918</dc:identifier><dc:language>en</dc:language><dc:relation>Acta Psychologica</dc:relation><dc:rights>info:eu-repo/semantics/openAccess</dc:rights><dc:type>info:eu-repo/semantics/article</dc:type><dc:identifier>https://bayburt-rdlopen.rawdatalibrary.net/tr/items/bayburt/11292</dc:identifier></oai_dc:dc></metadata></record></GetRecord></OAI-PMH>