Artificial Intelligence in Literacy Pedagogy: Enhancing Critical Thinking or Undermining Creative Engagement Among English Language Students in FCT College of Education, Zuba, Abuja

Abstract

This study examined artificial intelligence in literacy pedagogy: enhancing critical thinking or undermining creative engagement among English language students in FCT College of Education, Zuba, Abuja. Three specific purposes and three research questions guided the study. The study employed a descriptive survey design. The population is made up of all the 519 English language students in FCT College of Education, Zuba Abuja consisting of 198 year one students; 191 year two students and 130 year three students. The sample size is 226 determined using Taro Yamane formula. Stratified proportionate random sampling was used to select respondents in each stratum. The instrument for data collection was a questionnaire titled “Artificial Intelligence in Literacy Pedagogy: Enhancing Critical Thinking or Undermining Creative Engagement Questionnaire (AILPECTUCEQ)” constructed by the researcher based on the research questions. The instrument was face validated by three experts in Faculty of Education, Nnamdi Azikiwe University, Awka. The instrument was pilot tested to ensure its reliability and the data collected was analyzed using Cronbachs’ Alpha. The scores gave coefficient reliability of 0.73, 0.80 and 0.74, for the three clusters respectively and an overall reliability coefficient of 0.76. The researcher administered the instrument to the respondents with the help of two research assistants. Data were analyzed using mean and standard deviation for answering research questions. The findings revealed among others that AI enhanced critical thinking skill and creative engagement. Based on the findings, the study recommended among others that to capitalize on AI’s positive influence on critical thinking, curriculum designers should integrate structured AI-assisted activities—such as text-analysis algorithms and reflective-feedback platforms—that explicitly target evaluation of evidence, identification of bias, and argument construction.