<?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%">Özgün Arda Kuş</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ARTIFICIAL INTELLIGENCE TOOLS, EDUCATIONAL APPROACHES, AND LEARNING OUTCOMES IN ENVIRONMENTAL EDUCATION</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">ChatGPT</style></keyword><keyword><style  face="normal" font="default" size="100%">environmental education</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge-action gap</style></keyword><keyword><style  face="normal" font="default" size="100%">pedagogical approaches</style></keyword><keyword><style  face="normal" font="default" size="100%">scoping review</style></keyword><keyword><style  face="normal" font="default" size="100%">sustainability 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%">April/2026</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://journals.indexcopernicus.com/search/article?articleId=4863023</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) technologies are increasingly being used in environmental education, but the empirical evidence base in this field remains fragmented. The aim of this study was to systematically map empirical research on the use of AI in environmental education between 2022 and 2025. The study employed a scoping review design based on the PRISMA-ScR protocol. A total of 419 records retrieved from the Scopus and Web of Science databases were screened, and 16 studies met the inclusion criteria. Data were extracted using a structured coding framework based on four research questions and synthesized through thematic analysis. The findings indicated that large language models, particularly ChatGPT, are the most frequently used AI tools. Nine studies aimed to change attitudes or behaviors. Pedagogical approaches included content creation, personalized learning, narrative-based empathy development, and gamification. Only one study did not specify a clear theoretical framework. Standardized effect sizes were reported in only four studies. The geographical distribution was concentrated in East Asia. One study included a delayed post-test, leaving the long-term sustainability of the reported effects largely uncertain. Longitudinal designs, comparative tool evaluations, and culturally embedded AI designs from underrepresented regions have been identified as priority research needs for the field's advancement.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Research article </style></work-type><section><style face="normal" font="default" size="100%">298-317</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%">Branko Anđić</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ARTIFICIAL INTELLIGENCE AND DEEPFAKE LEARNING IN HIGHER EDUCATION</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">deepfake learning</style></keyword><keyword><style  face="normal" font="default" size="100%">higher education</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">February/2025</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://oaji.net/articles/2023/987-1740920869.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">24</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%">Higher education appears to be undergoing the most significant transformations due to the integration of artificial intelligence into teaching, learning, feedback provision, assessment, writing, and the growing role of AI in student research. Several aspects of AI in education have already been studied, such as institutional adoption policies and guidelines (Jin et al., 2025), Intelligent Tutoring Systems, and Automated Assessment and Feedback (Ramadhan et al., 2024). On the other hand, certain topics, such as deepfakes and their consequences for learning, remain underexplored. Deepfakes refer to the use of AI to create media—such as photos, audio, and video content—that appears authentic but is, in reality, artificially generated. </style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Editorial</style></work-type><section><style face="normal" font="default" size="100%">4-6</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%">Yusuf Zorlu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">THE ROLE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED QUESTION GENERATION ON PRESERVICE SCIENCE TEACHERS' 21ST-CENTURY TEACHING COMPETENCIES</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%">21st-century skills</style></keyword><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">critical thinking dispositions</style></keyword><keyword><style  face="normal" font="default" size="100%">Preservice Science Teachers</style></keyword><keyword><style  face="normal" font="default" size="100%">science education</style></keyword><keyword><style  face="normal" font="default" size="100%">social intelligence</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October/2025</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://journals.indexcopernicus.com/search/article?articleId=4659934</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">24</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%">The increasing integration of artificial intelligence (AI) in education highlights the need to explore its impact on preservice science teachers' 21st-century teaching competencies. This study aimed to explore the role of AI-assisted question generation on preservice science teachers' 21st-century teaching competencies. Mixed-methods research design was utilized. This study was participated by 32 preservice science teachers. The “21st Century Skills Teaching Scale”, “Tromsø Social Intelligence Scale”, “Critical Thinking Disposition Scale”, structured interview, and an evaluation rubric were administered as data collection tools. The integration of the generation process of AI-assisted questions was completed in a period of 12 weeks. In findings, the AI-assisted question generation process contributed to the enhancement of preservice science teachers’ 21st century skills, social intelligence, and critical thinking disposition. The enhancement of 21st century skills, social intelligence, and critical thinking was concluded that the process of creating questions by utilizing the opportunities offered by artificial intelligence technology was thanks to interaction based on communication with artificial intelligence, cooperation established through group work, and access to up-to-date information through technology. In light of these findings, it could be stated that it will be beneficial to increase the integration of AI into teaching practices in science 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%">984-1005</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%">Uyanik Aktulun, O.</style></author><author><style face="normal" font="default" size="100%">Koray Kasapoglu</style></author><author><style face="normal" font="default" size="100%">Bulent Aydogdu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">COMPARING TURKISH PRE-SERVICE STEM AND NON-STEM TEACHERS' ATTITUDES AND ANXIETY TOWARD ARTIFICIAL INTELLIGENCE</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%">anxiety toward artificial intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">attitude toward artificial intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">pre-service non-STEM teacher</style></keyword><keyword><style  face="normal" font="default" size="100%">pre-service STEM teacher</style></keyword><keyword><style  face="normal" font="default" size="100%">pre-service teacher</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October/2024</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://oaji.net/articles/2023/987-1729495932.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">23</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%">Identifying student teachers’ attitudes and anxiety toward artificial intelligence (AI) in regard to their field of study might be helpful in determining whether and how AI will be employed in their future classrooms. Hence, this study aims to compare pre-service STEM and non-STEM teachers’ attitudes and anxiety toward AI. In this quantitative research, the causal-comparative research design was adopted. The study involved 520 pre-service teachers from a faculty of education at a public university in Türkiye. Among all, 51.5% were pre-service non-STEM teachers while 48.5% were pre-service STEM teachers. Data were collected through the Turkish versions of “the General Attitudes toward Artificial Intelligence Scale” and “the Artificial Intelligence Anxiety Scale”. Diagnostic analytics were performed, and descriptive statistics and MANOVA were performed to analyse the data. As a result, pre-service teachers, in general, were mostly positive about AI, but undecided to be anxious about AI. STEM student teachers had more positive attitudes toward AI than non-STEM student teachers, and non-STEM student teachers were more anxious toward AI than STEM student teachers. The results imply that non-STEM teacher education curricula should be redesigned to be AI-integrated to better prepare teachers of the future as teachers with TPACK integrated with AI. </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%">950-963</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%">Pei-yu Chen</style></author><author><style face="normal" font="default" size="100%">Yuan-Chen Liu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">IMPACT OF AI ROBOT IMAGE RECOGNITION TECHNOLOGY ON IMPROVING STUDENTS’ CONCEPTUAL UNDERSTANDING OF CELL DIVISION AND SCIENCE LEARNING MOTIVATION</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">cell division</style></keyword><keyword><style  face="normal" font="default" size="100%">image recognition technology</style></keyword><keyword><style  face="normal" font="default" size="100%">learning by teaching</style></keyword><keyword><style  face="normal" font="default" size="100%">science learning motivation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">April/2024</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://oaji.net/articles/2023/987-1713272661.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">23</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%">This study explored the integration of neural networks and artificial intelligence in image recognition for object identification. The aim was to enhance students’ learning experiences through a &quot;Learning by Teaching&quot; approach, in which students act as instructors to train AI robots in recognizing objects. This research specifically focused on the cell division unit in the first grade of lower-secondary school. This study employed a quasi-experimental research design involving four seventh-grade classes in a rural lower-secondary school. The experimental group (41 students) were taught via an AI robot image recognition technology, whereas the control group (40 students) were taught via a more conventional textbook-centered approach. The research followed a pre-test design, with three classes lasting 45 min each, totaling 135 min of teaching time over two weeks. Evaluation tools include the &quot;Cell Division Two Stage Diagnostic Test&quot; and the &quot;Science Learning Motivation Scale.&quot; The results indicate that learning through teaching AI robot image recognition technology is more effective than textbook learning in enhancing students’ comprehension of the &quot;cell division&quot; concept and boosting motivation to learn science.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Original article</style></work-type><section><style face="normal" font="default" size="100%">208-220</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%">Sameera Alshorman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">THE READINESS TO USE AI IN TEACHING SCIENCE: SCIENCE TEACHERS' PERSPECTIVE</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 readiness</style></keyword><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">quantitative survey</style></keyword><keyword><style  face="normal" font="default" size="100%">science education</style></keyword><keyword><style  face="normal" font="default" size="100%">science teachers’ perspectives</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June/2024</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://oaji.net/articles/2023/987-1719377387.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">23</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%">This study aimed to assess the readiness of Jordanian science teachers for integrating Artificial Intelligence (AI) in science education, focusing on their perceptions, challenges, and training needs. A quantitative survey methodology was employed, using a 35-item questionnaire distributed to 136 science teachers in urban Jordan. The questionnaire, developed and refined through expert peer review and a pilot study, encompassed demographics, attitudes towards AI, perceived benefits and challenges of AI in science teaching, self-efficacy in using AI, and behavioural intentions regarding its use. The results indicated varying teacher readiness levels, influenced by gender, educational background, and concerns over data privacy and security. While there was a general sense of optimism about the potential of AI, significant challenges related to limited resource access and insufficient professional development were identified. These results emphasized the need for targeted policy initiatives and training programs to enhance teachers' readiness for AI adoption. The study's insights contributed to understanding the facilitators and barriers to AI integration in science education, highlighting the critical role of teacher readiness in the effective utilization of AI in educational contexts. </style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Original article</style></work-type><section><style face="normal" font="default" size="100%">432-448</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%">Hunkoog Jho</style></author><author><style face="normal" font="default" size="100%">Minsu Ha</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TOWARDS EFFECTIVE ARGUMENTATION: DESIGN AND IMPLEMENTATION OF A GENERATIVE AI-BASED EVALUATION AND FEEDBACK SYSTEM</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%">argumentative writing</style></keyword><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">automated assessment</style></keyword><keyword><style  face="normal" font="default" size="100%">natural language processing</style></keyword><keyword><style  face="normal" font="default" size="100%">web architecture</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">April/2024</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://oaji.net/articles/2023/987-1713273011.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">23</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%">This study aimed at examining the performance of generative artificial intelligence to extract argumentation elements from text. Thus, the researchers developed a web-based framework to provide automated assessment and feedback relying on a large language model, ChatGPT. The results produced by ChatGPT were compared to human experts across scientific and non-scientific contexts. The findings revealed marked discrepancies in the performance of AI for extracting argument components, with a significant variance between issues of a scientific nature and those that are not. Higher accuracy was noted in identifying claims, data, and qualifiers, as opposed to rebuttals, backing, and warrants. The study illuminated AI's promise for educational applications but also its shortcomings, such as the increased frequency of erroneous element identification when accuracy was low. This highlights the essential need for more in-depth comparative research on models and the further development of AI to enhance its role in supporting argumentation training.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Original article</style></work-type><section><style face="normal" font="default" size="100%">280-291</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%">Maja Kerneža</style></author><author><style face="normal" font="default" size="100%">Dejan Zemljak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SCIENCE TEACHERS' APPROACH TO CONTEMPORARY ASSESSMENT WITH A READING LITERACY EMPHASIS</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">assessment</style></keyword><keyword><style  face="normal" font="default" size="100%">reading literacy</style></keyword><keyword><style  face="normal" font="default" size="100%">SCIENCE teachers</style></keyword><keyword><style  face="normal" font="default" size="100%">teacher training</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October/2023</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://oaji.net/articles/2023/987-1697653714.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">22</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%">In a sample of 1215 teachers, this study examined the readiness of science educators for assessment in the rapidly evolving landscape of artificial intelligence in education. Participants responded to an online questionnaire during the emergency remote teaching phase, offering insights into the frequency and nature of assessment methods utilized. The research draws a connection between assessment techniques during remote teaching and the emergence of AI in education. The results show that the selected assessment methods vary across teachers, with some specific differences observed in the assessment practices of science teachers. The study underscores the critical role of reading literacy in enhancing student engagement in contemporary learning environments. Moreover, the findings suggest that continuous professional development significantly improves the readiness of (science) teachers for AI-enhanced assessment. Drawing from these insights, recommendations for subsequent research are delineated. </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%">851-864</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%">King-Dow Su</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">IMPLEMENTATION OF INNOVATIVE ARTIFICIAL INTELLIGENCE COGNITIONS WITH PROBLEM-BASED LEARNING GUIDED TASKS TO ENHANCE STUDENTS’ PERFORMANCE IN SCIENCE</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">cross-disciplinary</style></keyword><keyword><style  face="normal" font="default" size="100%">Problem-Based Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">situational issues</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">April/2022</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://oaji.net/articles/2022/987-1652122552.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</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%">This research conceived multivariate learning concepts and aimed at the implementation of integrating artificial intelligence (AI) life learning of face recognition, self-driving, and robots’ situational issues into problem-based learning (PBL) in natural science. This research put forward problem-solving and group cooperation in which students worked together to complete learning tasks from encountering problems to planning problem-solving. All 127 participants engaged in this research from the Hungkuo Delin University of Technology in Taiwan. 7 experts helped construct the content validity, construct validity, and reliability through instrument developments of 5 open-ended test questions and 31 learning attitude items. The statistical analyses revealed that those students improved their scientific cognitive skills of problem-solving by quantitative analyzes and presented their logical reasoning and activation ability by qualitative ones. In addition, the descriptive statistical analysis of learning attitude revealed that students had positive thinking attributes for the application of cross-disciplinary PBL and AI new knowledge. These findings had implications for the AI cognitions with PBL educational environment to increase innovation in the classroom. Pedagogical suggestions presented that students’ learning growth in the environment requires long-term cultivation to exhibit significant learning outcomes in the future.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Original article</style></work-type><section><style face="normal" font="default" size="100%">245-257</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%">Andrej Šorgo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">THE TEACHER’S ROLE IN THE BATTLE OF THE INTELLIGENT MACHINES</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">intelligent systems</style></keyword><keyword><style  face="normal" font="default" size="100%">interactive tools</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">February/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://oaji.net/articles/2020/987-1581269250.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">19</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%">For most educators, grading and marking assignments are not high on their wish lists for their teaching routines. In the context of learning, the same can be said for their students, where examinations are associated with feelings such as insecurity, anxiety, fear, and stress. For diverse reasons, such as allowing students to self-assess their knowledge or providing homework, many teachers who are more advanced in the application of ICT, are already putting quizzes and tests online. Nowadays, such tests are in most cases delivered and assessed automatically, with an automatic system. Often, such basic systems provide feedback to the students if an answer is correct and a summary of the outcome. So far, so good ˗ after testing, students know what they still have to learn and what they have already learned. Equally, teachers get the same information about the students. In this way, a teacher’s time can be spent on better purpose. However, the drawback is that tests are uniform in the format of the templates provided by the system, the philosophy of test construction and the grading by their developers. Additionally, if not controlled, such tests can even be answered by people other than the target students.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Editorial</style></work-type><section><style face="normal" font="default" size="100%">4-5</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%">Bulent Cavas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">INDUSTRY 4.0 AND SCIENCE EDUCATION</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">historical paradigm</style></keyword><keyword><style  face="normal" font="default" size="100%">industry 4.0</style></keyword><keyword><style  face="normal" font="default" size="100%">science education</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://oaji.net/articles/2019/987-1571123849.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">18</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%">Industry 4.0 and its applications are one of the most important issues of countries that want to keep their competitiveness in the field of scientific, technological and innovation in today's world. The Countries that are not able to adapt to Industry 4.0 processes will not be able to develop scientifically and technologically or keep up with current innovation processes. One of the aims of this paper is to explain the concept of industry 4.0 to our readers in a comprehensible manner and to show how much science education is actually related to industry 4.0.
In a general sense, Industry 4.0 or in other words, the 4th Industrial Revolution is an expression that includes many modern automation processes, large databases that communicate with each other, robotic devices and dark factories, in short, quality production technologies. In addition, Industry 4.0 includes modern technologies such as additive manufacturing, autonomous robots, big data, artificial intelligence, augmented reality, system integration, internet of things, cybersecurity, cloud computing, machine learning, deep learning etc. These technologies play an important role especially in the formation of new smart factories.
</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><work-type><style face="normal" font="default" size="100%">Editorial</style></work-type><section><style face="normal" font="default" size="100%">652-653</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%">Tereza Horáková</style></author><author><style face="normal" font="default" size="100%">Milan Houška</style></author><author><style face="normal" font="default" size="100%">Ludmila Dömeová</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CLASSIFICATION OF THE EDUCATIONAL TEXTS STYLES WITH THE METHODS OF ARTIFICIAL INTELLIGENCE</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">classification and regression trees</style></keyword><keyword><style  face="normal" font="default" size="100%">educational texts</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge unit</style></keyword><keyword><style  face="normal" font="default" size="100%">production rules</style></keyword><keyword><style  face="normal" font="default" size="100%">stylometric analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June/2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://oaji.net/articles/2017/987-1497963889.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">Continuous</style></pages><isbn><style face="normal" font="default" size="100%">E-ISSN 2538-7138</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Modern educational methods emphasize the necessity to transfer knowledge instead of data or information within the educational process. Thus it is important to the educational texts supporting the educational process contain knowledge in a particular textual representation. But it is not trivial to decide whether the particular piece of text contain knowledge or not. The solution is to measure the similarity between the particular text structure and a typical structure of a knowledge-designed text. This research aims at analysing the classification ability of three commonly-used classification techniques: artificial neural networks (ANNs), classification and regression trees (CARTs) and decision trees (bigMLs) to separate texts or text fragments into two groups. The texts in the first group contain mainly data and information (common texts), the texts in the other group contain knowledge in one of the particular knowledge representations (knowledge texts). The sample of 120 text fragments was used for the analysis. The results show that the ANN techniques are significantly more able to make the right classification of the text than the CART or bigML ones, and evidence good classification abilities. Thus the ANN approach could broaden the set of methods used for evaluation of difficulty of educational texts or textbooks.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Original article</style></work-type><section><style face="normal" font="default" size="100%">324-336</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%">Andrej Flogie</style></author><author><style face="normal" font="default" size="100%">Boris Aberšek</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TRANSDISCIPLINARY APPROACH OF SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS EDUCATION</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">cognitive neuroeducation model</style></keyword><keyword><style  face="normal" font="default" size="100%">cognitive science</style></keyword><keyword><style  face="normal" font="default" size="100%">multidisciplinarity</style></keyword><keyword><style  face="normal" font="default" size="100%">neuroscience</style></keyword><keyword><style  face="normal" font="default" size="100%">transdisciplinary model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">December/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://oaji.net/articles/2016/987-1479544205.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</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%">At the end of 20th century and especially in this century the education field is undergoing a significant change not only as a result of technological innovations but also pedagogical innovations on the bases of artificial intelligence (AI), cognitive science and neuroscience. What interested us was the attitude of students and teachers towards these changes. In the research the participating students were arranged in two groups, the control group (CG), where conventional lessons were carried out and the experimental group (EG), in which teachers used a transdisciplinary cognitive neuroeducation model. The performance data for the both groups was acquired via questionnaire adopted from TIMSS research. The teachers’ attitude towards these changes was mostly monitored via qualitative research.
As is apparent from the results, a positive shift can be seen in the students’ attitude towards school. And this positive attitude towards school can create in students the suitable motivation, which is the first and most important step towards quality knowledge. A positive shift was also made in the minds of the teachers.</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><work-type><style face="normal" font="default" size="100%">Original article</style></work-type><section><style face="normal" font="default" size="100%">779–790</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%">Boris Aberšek</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">COGITO ERGO SUM HOMOMACHINE?</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%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">human processing power</style></keyword><keyword><style  face="normal" font="default" size="100%">school system</style></keyword><keyword><style  face="normal" font="default" size="100%">teaching process</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September/2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://oaji.net/articles/2015/987-1425807869.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">When European Union (EU) heads of state and government met at a summit in Lisbon in 2000 (Lisbon declaration, 2000), they set the goal of making Europe 'the most competitive and dynamic knowledge-based economy in the world'. In a knowledge economy, the 'most effective modern economies will be those that produce the most information and knowledge, and make that information and knowledge easily accessible to the greatest number of individuals and enterprises'. This policy brief suggests that individuals and companies can easily collaborate and compete globally, and that the solution for Europe in meeting the Lisbon goals is to invest heavily in education and skills. Statistical evidence briefly demonstrates the high return on investment in education. It makes recommendations for ensuring that Europe' school systems become more flexible and effective in improving learning outcomes, and argues that Europeans capacity to compete in the global knowledge economy will depend on whether its higher education institutions can meet the rapidly growing demand for high-level skills. International comparisons demonstrate the challenges confronting Europe but also illustrate the success of efforts to meet such challenges.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Editorial</style></work-type><section><style face="normal" font="default" size="100%">268-270 </style></section></record></records></xml>