Research Article
Nadeyah J. Alreiahi, Noha Alrwaished
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep617
ABSTRACT
This study examined the participation of preservice teachers (PSTs) in artificial intelligence (AI)-generated lesson-plan training sessions, primarily within a mathematics instruction course in a teacher training program. Although earlier research has explored AI in education, few studies have specifically looked at how AI-generated lesson plans affect PSTs’ lesson-planning skills. This study addresses this gap by exploring math PSTs’ views on the use of AI-generated tools in lesson planning. Data were collected from focus group interviews with fifty students and were analyzed thematically using a coding framework after targeted training sessions. The findings revealed key themes, including the time-saving benefits of AI, its ability to produce innovative activities, and the support these tools provide to PSTs. While AI tools served as scaffolds, the importance of teacher agency, pedagogical knowledge, and content alignment remained vital. These results highlight the potential of AI support in teacher training programs, while also recognizing that it does not replace essential skills such as critical thinking and professional judgment. The study offers implications and recommendations for integrating AI while maintaining pedagogical rigor and curriculum integrity.
Keywords: artificial intelligence, preservice teachers, lesson planning, mathematics education, teacher training, pedagogical knowledge, higher education
Research Article
Kemal Özgen, Serkan Narlı
CONT ED TECHNOLOGY, Volume 11, Issue 1, pp. 77-98
ABSTRACT
This study focuses on the relationship among Content Knowledge (CK), Pedagogic Knowledge (PK), and Technological Knowledge (TK) using Technological Pedagogical Content Knowledge (TPACK). The aim of the study is to use the determined relationship to provide mathematical clarity using the Rough Set Theory, which is commonly used in areas such as Artificial Intelligence, Data Reduction, Determination of Dependencies, Estimation of Data Importance and the establishment of Decision (control) Algorithms. Accordingly, TPACK scale was applied to 340 preservice teachers who, at the time of conducting this study, were continuing their teaching at elementary (grade 5-8) and secondary (grade 9-12) Mathematics Teaching Department. The gathered data was broken into three different groups - low, medium and high. The data grouping allowed for applying of the Rough Set Analysis. This will enable TPACK constructs to assign prospective teachers to any of the three identified groups. Analysis has put forth that the CK, PK and TK components explain TPACK with a dependency degree of 0.105 and that even though the levels of significance of each component is low by itself, it cannot be removed from the data set. Lastly, decision rules have been established between CK, PK and TK with TPACK.
Keywords: content knowledge, pedagogical knowledge, rough sets, technological knowledge, technological pedagogical content knowledge