The integration of Artificial Intelligence (AI) in education holds immense potential to enhance teaching and learning. However, its effective adoption depends on teachers' readiness, which is influenced by factors such as demographic characteristics, AI familiarity, self-efficacy, perceived benefits, challenges, and institutional support. This study assesses the AI readiness of selected teachers in the Northern Region of Ghana using a quantitative research approach. A structured survey was administered to 300 teachers across various educational levels and subject specializations. Findings reveal a balanced gender representation (51.3% female, 48.7% male) and diverse teaching experience, with 36.7% having 1–5 years of experience. While STEM teachers form the largest group (47.7%), Senior High School teachers constitute the majority (39.3%). AI familiarity varies, with 37% reporting high or very high familiarity, whereas 39.6% have low or very low familiarity. Statistical analysis indicates no significant relationship between AI familiarity and teaching experience (p = .096) or gender (p = .506). Teachers demonstrate moderate self-efficacy in AI use, with confidence levels averaging between 2.89 and 2.97 on a 5-point scale. Perceived benefits include workload reduction (M = 3.00) and personalized learning (M = 2.87), while key challenges encompass inadequate training (M = 3.08), limited infrastructure (M = 3.00), and ethical concerns (M = 2.92). Institutional support is moderate, with school encouragement (M = 3.07) ranking highest. To enhance AI readiness, the study recommends AI-focused professional development, investment in infrastructure, and dedicated technical support. Strengthening institutional policies, addressing ethical concerns, and fostering AI awareness through engagement initiatives will be critical for successful integration. AI adoption can be effectively leveraged to transform education in Ghana when these gaps are addressed.
Building similarity graph...
Analyzing shared references across papers
Loading...
Hassan Mubarik Iddrisu
National Board of Examinations
Simon Alhassan Iddrisu
International Journal of Research in Education and Science
University for Development Studies
Building similarity graph...
Analyzing shared references across papers
Loading...
Iddrisu et al. (Mon,) studied this question.
synapsesocial.com/papers/68bb4d206d6d5674bcd00e29 — DOI: https://doi.org/10.46328/ijres.1314
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: