This study examined the integration of artificial intelligence (AI) with conventional teaching methods to develop effective hybrid instructional strategies for enhancing student learning outcomes in secondary schools in Ekiti State, Nigeria. This study adopted a quantitative research design to examine the integration of AI-enhanced learning tools in secondary schools in Ekiti State, Nigeria. The population comprised secondary school teachers and students, from which 400 teachers and 150 students were randomly selected. Teachers completed structured questionnaires assessing AI awareness, preparedness, challenges, and views on blending AI with traditional methods. A quasi-experimental design was used for students, divided into three groups: AI-enhanced, conventional, and hybrid learning. The Students’ Performance Test (SPT) was used to conduct pre- and post-tests to measure instructional effectiveness. Expert reviews confirmed the validity of both instruments, while reliability was established using Cronbach’s alpha and correlation analysis yielding a reliability coefficient of 0.89 and 0.82 respectively. Trained research assistants facilitated data collection. Data were analyzed using descriptive statistics and ANOVA. Ethical standards were observed, with informed consent, confidentiality, and anonymity ensured. The findings revealed that while AI adoption is growing, many teachers lack the training and technical skills required for effective use, with key challenges including insufficient training, poor infrastructure, and high costs. Despite these barriers, most teachers supported blending AI with traditional methods, and the hybrid model emerged as the most effective in improving student performance. The study recommended continuous professional development, investment in digital infrastructure, the introduction of pilot AI programs, and the formulation of clear policies to guide AI integration in education.
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