Utilisation of Generative Artificial Intelligence Tools as Correlates of Physics Student-Teachers' Pedagogical Content Knowledge and Perceived Teaching Practice Performance During Secondary School Teaching Practice

@article{1722132,
      author = {Abdulazeez Aliyu Umar},
      title = {Utilisation of Generative Artificial Intelligence Tools as Correlates of Physics Student-Teachers' Pedagogical Content Knowledge and Perceived Teaching Practice Performance During Secondary School Teaching Practice},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {768-782},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722132.pdf},
      abstract = {This study investigated the relationships among the use of Generative Artificial Intelligence (AI) tools, physics student-teachers' pedagogical content knowledge (PCK), and their perceived teaching performance during teaching practice in secondary schools. Specifically, the study examined the interrelationships among these variables, explored the relationship between pedagogical content knowledge and perceived teaching practice performance, and determined the predictive roles of Generative AI tool usage and pedagogical content knowledge on perceived teaching practice performance. The study adopted a correlational survey research design. The population comprised physics student-teachers in the Department of Science and Technology Education, Lagos State University, who were undertaking their teaching practice. A sample of 50 student-teachers was selected using a simple random sampling technique. Data were collected using a researcher-developed questionnaire containing 30 items on the use of Generative AI tools, pedagogical content knowledge, and perceived teaching practice performance. The instrument was validated by experts, and its reliability was established using Cronbach's alpha coefficient. Data were analysed using mean, standard deviation, Pearson Product-Moment Correlation, and multiple linear regression at the 0.05 level of significance. The findings revealed significant positive relationships among the study variables. The use of Generative AI tools was positively correlated with pedagogical content knowledge (r = .367, p = .010) and perceived teaching practice performance (r = .314, p = .028). A strong positive relationship was also found between pedagogical content knowledge and perceived teaching practice performance (r = .783, p < .001). Furthermore, the multiple regression analysis showed that 61.4% of the variance in perceived teaching practice performance was jointly explained by the use of Generative AI tools and pedagogical content knowledge (R² = .614, F(2, 46) = 36.509, p < .001). The study concluded that although the use of Generative AI tools was positively associated with both pedagogical content knowledge and perceived teaching practice performance, pedagogical content knowledge emerged as the stronger predictor of teaching practice performance. The study recommends the integration of Generative AI tools into teacher education programmes, particularly in physics teacher training, and the continuous enhancement of student-teachers' pedagogical content knowledge to improve teaching effectiveness.},
      keywords = {Generative Artificial Intelligence, Pedagogical Content Knowledge, Perceived Teaching Practice Performance, Physics Student-Teachers, Teacher Education.},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722132}
  }