<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">İsmail Satmaz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ARTIFICIAL INTELLIGENCE AWARENESS AND AI-TPACK AMONG SCIENCE TEACHERS IN TÜRKİYE</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Baltic Science Education</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">AI awareness</style></keyword><keyword><style  face="normal" font="default" size="100%">AI-TPACK</style></keyword><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">science education</style></keyword><keyword><style  face="normal" font="default" size="100%">SCIENCE teachers</style></keyword><keyword><style  face="normal" font="default" size="100%">teacher professional development</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October / 2026</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://oaji.net/articles/2026/987-1793131855.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">Continuous</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Artificial intelligence (AI) is changing how science is taught, and teachers need both an awareness of AI and the pedagogical knowledge to use it in class. This study examined the AI awareness and AI technological pedagogical content knowledge (AI-TPACK) of science teachers in Türkiye, compared these scores across demographic and professional groups, and analysed how the two constructs are related. In a cross-sectional correlational survey, data were gathered from 434 science teachers in state and private schools with the AI Awareness Scale for Science Teachers and the AI-TPACK Scale. Groups were compared with t-tests and Welch's ANOVA followed by Games–Howell tests, and relationships were examined with Pearson correlations. Teachers' AI awareness was at a moderate level, whereas their AI-TPACK was high. Scores varied by school type, AI training, years of experience, and degree level, while gender differences appeared in only a few subdimensions. Private-school teachers and teachers with AI training scored markedly higher on both scales. AI awareness correlated strongly with AI-TPACK, most clearly with its AI-related technological domains. As the data are cross-sectional, these patterns cannot be read causally, but they point to AI-specific professional learning and fair access to AI resources as priorities for science teacher education.</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><work-type><style face="normal" font="default" size="100%">Original article</style></work-type><section><style face="normal" font="default" size="100%">884-898</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Derya Girgin</style></author><author><style face="normal" font="default" size="100%">Fuat Elkonca</style></author><author><style face="normal" font="default" size="100%">İsmail Satmaz</style></author><author><style face="normal" font="default" size="100%">Eylem Yalçinkaya-Önder</style></author><author><style face="normal" font="default" size="100%">Serkan Demir</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DIAGNOSTIC ASSESSMENT OF LOWER-SECONDARY SCHOOL STUDENTS’ LEARNING NEEDS RELATED TO POLAR TOPICS: SCALE DEVELOPMENT AND VALIDATION</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Baltic Science Education</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">climate literacy</style></keyword><keyword><style  face="normal" font="default" size="100%">learning needs assessment</style></keyword><keyword><style  face="normal" font="default" size="100%">lower-secondary school students</style></keyword><keyword><style  face="normal" font="default" size="100%">polar education</style></keyword><keyword><style  face="normal" font="default" size="100%">scale development</style></keyword><keyword><style  face="normal" font="default" size="100%">science education</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October / 2026</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://oaji.net/articles/2026/987-1793131672.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">Continuous</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Rapid polar change makes it important for science teachers to know what students need when they study these regions. This study reports the development and validation of the Polar Topics Learning Needs Scale (PPLS), a self-report instrument for lower-secondary school students. The scale covers five dimensions: Content Learning Needs, Perceived Conceptual Difficulties, Teaching Support Needs, Technology and Activity-Based Learning Preferences, and Interest and Curiosity in Polar Topics. An initial pool of 35 items was reviewed by nine experts (CVR ≥ .78) and piloted with 25 students. Exploratory factor analysis with 316 students produced five factors that accounted for 53.81% of the variance, with loadings between .46 and .88. Confirmatory factor analysis with a separate group of 204 students supported this structure, χ²(265) = 420.98, p &lt; .001, χ²/df = 1.59, RMSEA = .054, SRMR = .06, CFI = .98, NFI = .95, NNFI = .98, GFI = .84. Subscale α and ω coefficients ranged from .82 to .88. The 25-item PPLS yields subscale scores that can be used descriptively with similar samples; predictive validity, temporal stability, and cross-cultural generalisability have not yet been established.</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><work-type><style face="normal" font="default" size="100%">Original article</style></work-type><section><style face="normal" font="default" size="100%">835-854</style></section></record></records></xml>