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<p>This study examined the integration of Artificial Intelligence (AI) in maritime higher education institutions (MHEIs) in Region III, Philippines, focusing on instructor profiles, the status of AI adoption, instructional applications, perceived challenges, and future prospects. Using a descriptive research design, a researcher-developed survey was administered to 50 maritime faculty members. The instrument underwent expert review for clarity and content alignment, and internal consistency reliability was assessed using Cronbach’s alpha (α = 0.756–0.972). Exploratory factor analysis was conducted on the AI Application scale as a preliminary structural examination. Data were analyzed using descriptive statistics, weighted means, frequency distributions, and thematic analysis of open-ended responses. Results indicated that simulation-related AI tools were most widely used, although overall adoption across instructional domains was uneven. AI was most strongly applied in simulation and skill development, followed by content delivery and student assessment. Key challenges included limited faculty training, infrastructure constraints, and ethical considerations. Faculty expressed cautious optimism regarding AI’s future role and emphasized the need for structured professional development and institutional policy support. The findings provide exploratory, context-specific insights that may inform similar maritime and technical education contexts facing comparable challenges in AI adoption.</p>

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Status applications and prospects of AI integration in maritime education implications for curriculum planning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Sheryl M. Reyes

摘要

This study examined the integration of Artificial Intelligence (AI) in maritime higher education institutions (MHEIs) in Region III, Philippines, focusing on instructor profiles, the status of AI adoption, instructional applications, perceived challenges, and future prospects. Using a descriptive research design, a researcher-developed survey was administered to 50 maritime faculty members. The instrument underwent expert review for clarity and content alignment, and internal consistency reliability was assessed using Cronbach’s alpha (α = 0.756–0.972). Exploratory factor analysis was conducted on the AI Application scale as a preliminary structural examination. Data were analyzed using descriptive statistics, weighted means, frequency distributions, and thematic analysis of open-ended responses. Results indicated that simulation-related AI tools were most widely used, although overall adoption across instructional domains was uneven. AI was most strongly applied in simulation and skill development, followed by content delivery and student assessment. Key challenges included limited faculty training, infrastructure constraints, and ethical considerations. Faculty expressed cautious optimism regarding AI’s future role and emphasized the need for structured professional development and institutional policy support. The findings provide exploratory, context-specific insights that may inform similar maritime and technical education contexts facing comparable challenges in AI adoption.