Status, Applications, and Prospects of AI in Maritime Education: Basis for Curriculum Planning

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Reyes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7827795/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigated the integration of Artificial Intelligence (AI) in maritime higher education institutions (MHEIs) in Region III, Philippines. Guided by five research questions, it examined the demographic profile of instructors, the status of AI integration, its applications in teaching, the challenges encountered, and the future prospects envisioned by faculty. A descriptive research design was employed, using a validated survey instrument administered to 50 maritime faculty members. Descriptive statistics, frequency distributions, weighted means, and thematic coding of open-ended responses were used in data analysis. Findings revealed that while simulation-based AI tools were the most widely used, adoption remained uneven across instructional domains. AI was strongly applied in simulation and skill development, followed by content delivery and assessment. The major challenges identified included a lack of training, infrastructure limitations, and ethical concerns. Faculty members were optimistic about AI’s prospects but emphasized the importance of continuous training, simulator investment, and clear institutional policies. The study concluded with recommendations for faculty development, curriculum alignment, and institutional support to enable responsible AI integration in maritime education. Educational Philosophy and Theory Artificial Intelligence and Machine Learning Artificial Intelligence (AI) AI Integration Curriculum Planning Maritime Education Simulation-Based Learning Introduction Artificial Intelligence (AI) has rapidly transformed higher education by reshaping instructional delivery, assessment, and curriculum development (Lyu et al., 2025 ). In maritime education and training (MET), AI holds particular significance due to its capacity to simulate complex, safety-critical environments and support adaptive learning (Karimi et al., 2024 ; Sharma et al., 2022 ). Simulation-based AI systems now play a key role in enhancing cadet readiness for real-world scenarios, integrating virtual and augmented reality for risk-free skill practice (Bačnar et al., 2025 ; Kim et al., 2021 ). These technological advances align with the broader shift toward digital transformation in maritime education, where instructors are expected to adopt data-driven and learner-centered pedagogies (Scanlan et al., 2022 ; Simmons & McLean, 2020 ). In the Philippines, maritime higher education institutions (MHEIs) are increasingly urged to integrate AI to remain aligned with the standards set by the International Maritime Organization (IMO) and the STCW Convention, as well as to meet the evolving digital competency requirements of the global shipping industry (Mohd et al., 2024; Mohamed & Elnoury, 2023 ). However, challenges persist, including limited faculty training, insufficient technological infrastructure, and ethical concerns over data privacy and algorithmic bias (Ofosu-Ampong, 2024 ; Gealone, 2024 ). These issues mirror international findings that the pace of AI adoption often outstrips institutional readiness and pedagogical support (Martyniuk et al., 2025 ). Given this context, the present study examined the integration of AI in maritime education within Region III, Philippines. Specifically, it sought to determine the current status and applications of AI in instruction, the challenges faced by faculty, and the prospects envisioned for future AI-driven curriculum development. By addressing these dimensions, the study aimed to generate evidence-based insights that can inform institutional strategies, faculty development programs, and curriculum alignment efforts to foster responsible and effective AI integration in maritime higher education. These research questions guided the study’s methodological approach, described in the following section. Methods This study employed a descriptive research design integrating both quantitative and qualitative approaches to examine the status, application, challenges, and future prospects of Artificial Intelligence (AI) integration in maritime higher education institutions (MHEIs) in Region III, Philippines. The descriptive design was deemed appropriate as it allowed the systematic collection and analysis of data reflecting faculty experiences and perceptions regarding AI use in instruction. Participants The study involved 50 maritime faculty members purposively selected from nine maritime higher education institutions across Region III. Purposive sampling was employed to ensure inclusion of instructors with relevant exposure to AI tools and applications in teaching and assessment. Participation was voluntary, and informed consent was secured before data collection. The sample, while limited in size, adequately represented faculty diversity in terms of teaching rank, experience, and subjects handled, consistent with the study’s objective of capturing varied instructional contexts. Research Instrument Data were gathered using a validated researcher-developed survey instrument aligned with the study’s five research questions. The instrument consisted of four major parts: (1) demographic profile of respondents; (2) status of AI integration, including types of tools used, courses where AI is applied, and frequency of use; (3) application of AI in instruction, covering content delivery, student assessment, and simulation or skill development; and (4) challenges encountered and future prospects for AI in maritime education. Most items employed a four-point Likert scale to measure the extent of agreement or application, while open-ended questions elicited qualitative insights on perceived opportunities, recommendations, and expectations for AI’s future role. Data Gathering Procedure The survey was disseminated both electronically and in person to accommodate institutional constraints and faculty availability. Respondents were assured of confidentiality, and responses were recorded anonymously. Data collection spanned two months during the academic year 2024–2025. Data Analysis Quantitative data were analyzed using descriptive statistics, including frequency counts, percentages, and weighted means, to summarize demographic information and assess the current status and application of AI integration. These analyses addressed Research Questions 1 to 4. For Research Question 5, qualitative thematic analysis was conducted on open-ended responses to identify recurring themes, patterns, and faculty recommendations regarding AI’s future prospects in maritime education. Triangulation of quantitative and qualitative findings enabled a comprehensive interpretation of AI integration trends, consistent with the study’s descriptive mixed-method approach. Limitations It is acknowledged that the relatively small sample size and purposive sampling approach may limit the generalizability of the results beyond the study’s regional context. However, the findings provide a meaningful snapshot of current practices and institutional readiness for AI integration in maritime education, offering valuable groundwork for future large-scale investigations. AI Assistance Declaration: Portions of the manuscript, including literature synthesis and language editing, were prepared with the assistance of AI tools (OpenAI’s ChatGPT). All analyses, interpretations, and final decisions were made solely by the authors. Results 1. Profile of Respondents Table 1 Demographic Profile of Respondents (N = 50) Variable Category Frequency (f) Percentage (%) Age Below 30 16 32% 31–40 14 28% 41–50 10 20% 51 and above 10 20% Teaching Rank Instructor 37 74% Associate Professor 4 8% Program Head - BSMT 3 6% Professor 2 4% Course Developer 2 4% Assessor 1 2% Program Head - BSMarE 1 2% Teaching Experience Less than 5 years 27 74% 6–10 years 13 26% 11–15 years 6 12% More than 15 years 4 8% Subjects Taught Marine Engineering courses 17 34% Marine Transportation courses 15 30% General Education courses 12 24% Maritime Safety & compliance 8 16% Simulator-related courses 6 12% AI-related Training Attended 34 68% Not Attended 16 32% The profile indicates that maritime faculty in Region III are mostly in early to mid-career stages, with instructors comprising the largest share of the teaching force (74%). A majority have less than 10 years of experience, suggesting a generational mix of newer and seasoned educators. Notably, only 68% have attended any AI-related training, revealing a considerable skills gap in emerging technologies. This reflects the broader pattern observed in higher education, where faculty preparedness for AI integration often trails technological innovation. Fundi et al. ( 2024 ) found that even among in-service teachers exposed to competence-based curricula, readiness for AI adoption was limited by inadequate training opportunities and institutional support. Likewise, Petjärv et al. ( 2025 ) highlighted that despite increasing awareness of generative AI, faculty continue to report low confidence and lack of formal training as key barriers to responsible use. In maritime education, this aligns with Aly Salem and Hassan ( 2025 ), who noted that AI-related competence gaps among maritime trainers affect the performance and adaptability of trainees in simulation environments. Collectively, these findings suggest that while enthusiasm for digital transformation exists, structured faculty development remains an essential foundation for meaningful AI integration. 2. Status of AI Integration in Maritime Education Table 2 Top AI tools used (N = 50) AI tool Frequency (f) Percentage (%) Simulation-based AI tools 30 61% Generative AI (e.g., ChatGPT) 20 41% Adaptive learning platforms 19 39% Automated grading systems 17 35% Predictive analytics 9 18% Note : Percentages exceed 100% because respondents could select multiple AI tools. The results shown in Table 2 that simulation-based AI tools are the most widely used (61%), highlighting the importance of simulators in maritime training. Generative AI (41%) and adaptive learning platforms (39%) are also gaining ground, though to a lesser extent. Automated grading systems (35%) and predictive analytics (18%) remain less common, indicating that while simulation dominates, other AI applications are still in the early stages of adoption. These findings are consistent with Karimi et al. ( 2024 ), who confirmed that AI-enhanced simulation remains the cornerstone of maritime training, improving decision-making and engagement. Similarly, Sharma et al. ( 2022 ) demonstrated that AI chatbots enhance learning retention by providing interactive guidance in collision regulations (COLREGs) courses. The data also affirm Scanlan et al. ( 2022 ), who found that digital simulators and AI-driven systems have become the leading tools driving MET modernization. Overall, while maritime faculty have begun integrating AI, institutional readiness still dictates adoption levels. 3. Application of AI in Maritime Instruction Table 3 Application of AI (Mean Scores, 4-point scale) Application Area Mean (1–4 scale) Interpretation Content delivery 3.15 Agree Student assessment 2.97 Agree Simulation and skill development 3.32 Strongly Agree It can be gleaned in Table 3 that AI is most strongly applied in simulation and skill development (M = 3.32, interpreted as Strongly Agree ), which highlights its central role in maritime instruction. This result underscores how simulation-based AI technologies provide realistic, interactive training environments where cadets can develop decision-making and technical competencies in risk-free contexts. Content delivery also scored high (M = 3.15, Agree ), indicating that AI is increasingly supporting instructional delivery through adaptive learning platforms and generative content tools. Student assessment scores are slightly lower (M = 2.97, Agree ), suggesting more cautious adoption of AI in evaluating student performance compared to its use in simulations and teaching delivery. These results are consistent with the findings of Karimi et al. ( 2024 ), who reported that AI-based adaptive instructional systems in maritime safety training enhance engagement and decision-making under simulated conditions. Similarly, Cristina Campos Toresano et al. ( 2022 ) emphasized that simulation-centered learning aligns maritime competencies with automation and the evolving needs of the global shipping industry. In higher education contexts, Almasri ( 2024 ) noted that AI applications in science and technical education enhance experiential learning through interactive simulations, but their assessment applications remain limited due to validity concerns. The moderate use of AI for student assessment mirrors ethical and accuracy concerns raised by Khlaif et al. ( 2024 ), who observed that educators often hesitate to rely on AI for grading because of transparency and fairness issues. Vera ( 2023 ) likewise found that faculty members perceive AI tools as effective for formative feedback but are reluctant to delegate high-stakes evaluation tasks to automated systems. Together, these findings illustrate that while simulation represents the most mature form of AI integration in maritime education, applications in assessment and content delivery are progressing more cautiously due to ethical and pedagogical considerations. 4. Challenges Encountered in AI Integration Table 4 Challenges in AI Integration (Mean Scores, 4-point scale ) Challenge Mean SD Lack of training 3.45 0.617 Limited infrastructure 3.27 0.786 Ethical/data privacy concerns 3.15 0.772 High cost of AI tools 3.08 0.778 Resistance to adoption 2.05 0.849 The findings in Table 4 show that the most pressing challenge encountered by faculty members in integrating AI into maritime instruction is the lack of training (M = 3.45), indicating that most instructors feel underprepared to effectively utilize AI tools in their teaching. This is followed by limited infrastructure (M = 3.27), such as insufficient access to updated hardware, software, and reliable internet. Ethical and data privacy concerns (M = 3.15) also emerged as a notable issue, while high cost of AI tools (M = 3.08) and resistance to adoption (M = 2.85) were reported as relatively less pressing but still relevant. The trend suggests that while faculty acknowledge the value of AI, their ability to integrate it is constrained primarily by lack of institutional support in training and resources. These findings are consistent with recent studies. Fundi, Sanusi, Oyelere & Ayere ( 2024 ) found that in-service teachers in Kenya reported preparedness and training gaps as primary barriers to integrating AI in competence-based curricula. Khlaif et al. ( 2024 ) report that educators are cautious about adopting AI in assessment due to concerns about fairness, transparency, and validity of outputs. Almasri ( 2024 ) in her systematic review noted that while AI has potential in science education, lack of infrastructure and training still impede its full adoption. Collectively, these results reinforce the idea that faculty readiness is not solely a matter of individual competence but depends on systemic institutional support. Addressing these challenges will therefore require sustained investment in structured professional development and infrastructural upgrading, ensuring that AI integration in maritime education is both effective and ethically responsible. 5. Future Prospects for AI in Maritime Education Table 5 Faculty Prospects for AI Integration Prospect Statement Mean SD Interpretation AI will play a central role in future maritime education 3.44 0.71 Agree I feel optimistic about AI’s long-term role 3.35 0.70 Agree Faculty will require ongoing training 3.25 0.60 Agree AI will improve personalized learning 3.17 0.66 Agree Open-ended themes Training (6 mentions), Simulation/Digital Twins (6), Ethics/Policy (4), Curriculum Change (1), Assessment (1) Table 5 presents the mean ratings of respondents regarding their envisioned future role of AI in maritime education. The results indicate that faculty members largely agree that AI will play a central role in maritime instruction (M = 3.44, SD = 0.71) and express optimism about its long-term role in higher education (M = 3.35, SD = 0.70). Respondents also emphasized the importance of ongoing training (M = 3.25, SD = 0.60) and noted AI’s potential to enhance personalized learning opportunities (M = 3.17, SD = 0.66). These findings suggest that instructors generally perceive AI as a positive force in the future of maritime education, though they stress the need for sustainable faculty upskilling. Beyond the scaled responses, the open-ended question revealed recurring themes in how faculty envision AI’s future. The most frequently mentioned were the need for training (6 mentions), greater use of simulation and digital twins (6 mentions), and the importance of ethics and policy guidelines (4 mentions). Other areas noted include curriculum change and AI-driven assessment. Sample faculty responses include: “Real-time situation adjustments from AI should be integrated on simulator-based systems,” , “Teachers have to have enough training first and then specific guidelines or policies should be drafted , and “Maximizing AI in streamlining instructional content, assessment, and feedback (at least for general education ) ”. These highlight the dual emphasis on technological innovation and policy preparedness. The combined findings illustrate that while faculty members are optimistic about AI’s transformative role, they consistently call for institutional investments in professional development, simulation infrastructure, and ethical frameworks to ensure responsible adoption. This finding is consistent with Funa and Gabay (2024), who emphasized that policy coherence and faculty readiness are vital for the sustainable use of AI in higher education. Similarly, Karimi et al. ( 2024 ) underscored that simulation-based AI systems, especially those using adaptive algorithms, will define the next generation of maritime training, a vision echoed by the respondents’ references to digital twins and real-time adaptive scenarios. Faculty optimism regarding AI’s potential aligns with Ofosu-Ampong ( 2024 ), who reported that higher education instructors view AI as a long-term enabler of personalized, efficient, and competency-based learning. The emphasis on continuous training and curriculum renewal also parallels Upadhyay and Sah (2025), who found that faculty readiness and professional development are indispensable for effective AI integration. Additionally, Aly Salem and Hassan ( 2025 ) demonstrated that AI-driven training tools significantly enhance maritime trainees’ performance, supporting the participants’ belief that simulator-centered instruction will remain foundational in future maritime education. The attention to ethical frameworks reflects growing consensus in higher education scholarship. Castelló-Sirvent et al. ( 2024 ) argued that integrating AI ethics into curriculum design is necessary to maintain trust, transparency, and accountability in teaching and assessment, concerns likewise raised by faculty respondents in this study. Furthermore, Matos et al. ( 2025 ) noted that future AI integration must be accompanied by explicit strategies for digital ethics and sustainable pedagogy to balance innovation with institutional responsibility. Overall, the findings reveal that AI integration in maritime higher education is still at a formative stage but developing in promising directions. Faculty rely most on AI-driven simulators for skill development, while applications in content delivery and assessment remain limited. Consistent with Karimi et al. ( 2024 ) and Aly Salem and Hassan ( 2025 ), simulation-based AI tools are recognized for enhancing decision-making and engagement, yet infrastructural and ethical constraints persist. The lack of structured, continuous training, highlighted by Fundi et al. ( 2024 ) and Funa and Gabay (2024), continues to hinder widespread adoption. Despite these barriers, faculty express strong optimism toward AI’s long-term prospects in maritime instruction, aligning with Upadhyay and Sah (2025) and Matos et al. ( 2025 ), who emphasize curriculum modernization, faculty capacity building, and institutional support as the cornerstones of effective AI integration. In sum, AI integration in maritime education is marked by innovation tempered by challenges but driven by a strong commitment to pedagogical advancement. The outlook remains positive: maritime faculty envision a future where AI enables personalized, simulation-rich, and ethically grounded learning environments that advance both maritime competence and educational transformation. Conclusion and Implications This study examined the integration of artificial intelligence (AI) in maritime higher education institutions (MHEIs) in Region III, Philippines, focusing on the status, applications, challenges, and future prospects of AI in instruction. The results revealed that AI adoption is emerging but remains uneven across instructional domains. Simulation-based tools are most widely used, underscoring their effectiveness in enhancing situational awareness, decision-making, and practical competence among cadets. However, the use of AI for content delivery, assessment, and predictive analytics remains moderate, reflecting constraints in infrastructure, policy support, and faculty readiness. The findings highlight that while most maritime instructors express optimism toward AI’s potential, gaps in structured training and institutional preparedness persist. Faculty members recognize AI’s capacity to modernize maritime training and align education with global industry standards but emphasize the need for sustained capacity-building, ethical governance, and curriculum alignment. These conclusions are consistent with recent scholarship (Karimi et al., 2024 ) underscoring that technological adoption in specialized disciplines such as maritime education must be guided by human-centered and pedagogically grounded approaches. Recommendations To ensure systematic and responsible AI integration in maritime education, the following actions are recommended: Faculty Training and Professional Development. Institutions should institutionalize continuous AI literacy and pedagogical training programs to strengthen instructors’ digital competence and instructional confidence. Infrastructure and Resource Enhancement. MHEIs should invest in robust digital infrastructure and simulation technologies to support effective and equitable AI deployment across maritime programs. Curriculum Alignment. AI integration should be embedded into curricular design, particularly within simulation, navigation, and safety modules, ensuring alignment with international maritime standards and accreditation frameworks. Policy and Ethical Frameworks. Institutional policies must address issues of data privacy, algorithmic fairness, and intellectual property to guide ethical AI use in instruction and assessment. Collaborative Research and Industry Partnerships. Strengthening linkages with maritime industries, technology providers, and international MET institutions will foster collaborative innovation, research, and the exchange of best practices. By pursuing these recommendations, maritime education institutions can foster a balanced and future-ready ecosystem—one that leverages the potential of AI while safeguarding pedagogical integrity, ethical responsibility, and the professional growth of educators. Declarations Funding Statement: This research received no specific grant from any funding agency, commercial, or not-for-profit sectors. The study was self-funded by the author as part of her doctoral dissertation requirements at Bataan Peninsula State University. Declaration of Conflict of Interest: The author declares no conflict of interest related to the conduct, authorship, or publication of this study. Author Contribution: The author solely conceptualized, designed, gathered data, analyzed results, and wrote the entire manuscript. Acknowledgments The author expresses sincere gratitude to the participating maritime higher education institutions and faculty members in Region III, Philippines, for their invaluable cooperation and time. Appreciation is also extended to the research advisers and academic mentors from Bataan Peninsula State University for their guidance and constructive feedback throughout the conduct of this study. This study was approved by the Ethics Review Committee of Bataan Peninsula State University. Participation was voluntary, with informed consent obtained from all respondents. No identifying data were collected, and all procedures complied with the Data Privacy Act of 2012 and relevant ethical research standards. References Almasri, F. (2024). Exploring the Impact of Artificial Intelligence in Teaching and Learning of Science: A Systematic Review of Empirical Research. Research in Science Education , 54 (1). https://doi.org/10.1007/s11165-024-10176-3 Aly Salem, A. M., & M. Hassan, M. H. 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Reyes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBADOQMIzQzEPMRpMSZdS+IGorXITzv78DFPRV36dun2ZxIMFdaJDdK9B/BqMbidbmzMc+Zw7s45Z8wkGM6kJzbInEvAr0U6jU2at+1A7oYbOWwSjG2HExskcgzwO2x2Gvtv3n916QY30p9JMP4jQgvD7TQ2Zt4G5gSDGwlmEowNRGgxuJ3GLDnn2GFDoF+MLRKOpRu3yZwh6DDGD29q6uTNpdsf3vhQYy3bL91DwGFAwASOCAkgTgBiNgmCGhgYGH/AtDCgMEbBKBgFo2AUQAAACdRDLY21GKEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0003-2314-7929","institution":"Bataan Peninsula State University","correspondingAuthor":true,"prefix":"","firstName":"Sheryl","middleName":"M.","lastName":"Reyes","suffix":""}],"badges":[],"createdAt":"2025-10-10 14:02:14","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7827795/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7827795/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93520773,"identity":"9928d106-0e47-48a2-95a4-13361330d6c4","added_by":"auto","created_at":"2025-10-14 17:47:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40842,"visible":true,"origin":"","legend":"","description":"","filename":"AIinMaritimeEducation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7827795/v1/3ca362893623d4b5f4944986.docx"},{"id":93520772,"identity":"fa58ae59-3110-4177-afc8-2442087e16ae","added_by":"auto","created_at":"2025-10-14 17:47:19","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs7827795.json","url":"https://assets-eu.researchsquare.com/files/rs-7827795/v1/3002f778bc0232f99ece88cd.json"},{"id":93520776,"identity":"7398f3fa-e11d-4000-a541-06aa231ef9da","added_by":"auto","created_at":"2025-10-14 17:47:19","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79713,"visible":true,"origin":"","legend":"","description":"","filename":"rs78277950enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7827795/v1/4c098293075fb6fc5ee1a315.xml"},{"id":93521457,"identity":"118fc628-457d-4790-a298-feaea0093ce3","added_by":"auto","created_at":"2025-10-14 17:55:19","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78612,"visible":true,"origin":"","legend":"","description":"","filename":"rs78277950structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7827795/v1/2cd337e86ee9e459310b4382.xml"},{"id":93520774,"identity":"dbdb5248-25eb-4bec-b52d-7b4ecba03659","added_by":"auto","created_at":"2025-10-14 17:47:19","extension":"html","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83390,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7827795/v1/6cc881e180bc674db0b3bc6e.html"},{"id":93521731,"identity":"fd7399d7-5478-4ccf-8c17-57f842d88d9f","added_by":"auto","created_at":"2025-10-14 18:03:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":711840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7827795/v1/26a195fe-f138-42d3-9315-07154bb3575f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eStatus, Applications, and Prospects of AI in Maritime Education: Basis for Curriculum Planning\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial Intelligence (AI) has rapidly transformed higher education by reshaping instructional delivery, assessment, and curriculum development (Lyu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In maritime education and training (MET), AI holds particular significance due to its capacity to simulate complex, safety-critical environments and support adaptive learning (Karimi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Simulation-based AI systems now play a key role in enhancing cadet readiness for real-world scenarios, integrating virtual and augmented reality for risk-free skill practice (Bačnar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These technological advances align with the broader shift toward digital transformation in maritime education, where instructors are expected to adopt data-driven and learner-centered pedagogies (Scanlan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Simmons \u0026amp; McLean, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the Philippines, maritime higher education institutions (MHEIs) are increasingly urged to integrate AI to remain aligned with the standards set by the International Maritime Organization (IMO) and the STCW Convention, as well as to meet the evolving digital competency requirements of the global shipping industry (Mohd et al., 2024; Mohamed \u0026amp; Elnoury, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, challenges persist, including limited faculty training, insufficient technological infrastructure, and ethical concerns over data privacy and algorithmic bias (Ofosu-Ampong, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gealone, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These issues mirror international findings that the pace of AI adoption often outstrips institutional readiness and pedagogical support (Martyniuk et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven this context, the present study examined the integration of AI in maritime education within Region III, Philippines. Specifically, it sought to determine the current status and applications of AI in instruction, the challenges faced by faculty, and the prospects envisioned for future AI-driven curriculum development. By addressing these dimensions, the study aimed to generate evidence-based insights that can inform institutional strategies, faculty development programs, and curriculum alignment efforts to foster responsible and effective AI integration in maritime higher education.\u003c/p\u003e\u003cp\u003eThese research questions guided the study\u0026rsquo;s methodological approach, described in the following section.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study employed a descriptive research design integrating both quantitative and qualitative approaches to examine the status, application, challenges, and future prospects of Artificial Intelligence (AI) integration in maritime higher education institutions (MHEIs) in Region III, Philippines. The descriptive design was deemed appropriate as it allowed the systematic collection and analysis of data reflecting faculty experiences and perceptions regarding AI use in instruction.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThe study involved 50 maritime faculty members purposively selected from nine maritime higher education institutions across Region III. Purposive sampling was employed to ensure inclusion of instructors with relevant exposure to AI tools and applications in teaching and assessment. Participation was voluntary, and informed consent was secured before data collection. The sample, while limited in size, adequately represented faculty diversity in terms of teaching rank, experience, and subjects handled, consistent with the study\u0026rsquo;s objective of capturing varied instructional contexts.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResearch Instrument\u003c/h3\u003e\n\u003cp\u003eData were gathered using a validated researcher-developed survey instrument aligned with the study\u0026rsquo;s five research questions. The instrument consisted of four major parts: (1) demographic profile of respondents; (2) status of AI integration, including types of tools used, courses where AI is applied, and frequency of use; (3) application of AI in instruction, covering content delivery, student assessment, and simulation or skill development; and (4) challenges encountered and future prospects for AI in maritime education. Most items employed a four-point Likert scale to measure the extent of agreement or application, while open-ended questions elicited qualitative insights on perceived opportunities, recommendations, and expectations for AI\u0026rsquo;s future role.\u003c/p\u003e\n\u003ch3\u003eData Gathering Procedure\u003c/h3\u003e\n\u003cp\u003eThe survey was disseminated both electronically and in person to accommodate institutional constraints and faculty availability. Respondents were assured of confidentiality, and responses were recorded anonymously. Data collection spanned two months during the academic year 2024\u0026ndash;2025.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eQuantitative data were analyzed using descriptive statistics, including frequency counts, percentages, and weighted means, to summarize demographic information and assess the current status and application of AI integration. These analyses addressed Research Questions 1 to 4.\u003c/p\u003e\u003cp\u003eFor Research Question 5, qualitative thematic analysis was conducted on open-ended responses to identify recurring themes, patterns, and faculty recommendations regarding AI\u0026rsquo;s future prospects in maritime education. Triangulation of quantitative and qualitative findings enabled a comprehensive interpretation of AI integration trends, consistent with the study\u0026rsquo;s descriptive mixed-method approach.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eIt is acknowledged that the relatively small sample size and purposive sampling approach may limit the generalizability of the results beyond the study\u0026rsquo;s regional context. However, the findings provide a meaningful snapshot of current practices and institutional readiness for AI integration in maritime education, offering valuable groundwork for future large-scale investigations.\u003c/p\u003e\u003cp\u003eAI Assistance Declaration: Portions of the manuscript, including literature synthesis and language editing, were prepared with the assistance of AI tools (OpenAI\u0026rsquo;s ChatGPT). All analyses, interpretations, and final decisions were made solely by the authors.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003e1. Profile of Respondents\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDemographic Profile of Respondents (N = 50)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency (f)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelow 30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31–40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41–50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeaching Rank\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInstructor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssociate Professor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProgram Head - BSMT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCourse Developer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssessor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProgram Head - BSMarE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeaching Experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than 5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6–10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11–15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMore than 15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubjects Taught\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarine Engineering courses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarine Transportation courses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral Education courses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaritime Safety \u0026amp; compliance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSimulator-related courses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI-related Training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAttended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Attended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe profile indicates that maritime faculty in Region III are mostly in early to mid-career stages, with instructors comprising the largest share of the teaching force (74%). A majority have less than 10 years of experience, suggesting a generational mix of newer and seasoned educators. Notably, only 68% have attended any AI-related training, revealing a considerable skills gap in emerging technologies.\u003c/p\u003e\u003cp\u003eThis reflects the broader pattern observed in higher education, where faculty preparedness for AI integration often trails technological innovation. Fundi et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that even among in-service teachers exposed to competence-based curricula, readiness for AI adoption was limited by inadequate training opportunities and institutional support. Likewise, Petjärv et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlighted that despite increasing awareness of generative AI, faculty continue to report low confidence and lack of formal training as key barriers to responsible use. In maritime education, this aligns with Aly Salem and Hassan (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who noted that AI-related competence gaps among maritime trainers affect the performance and adaptability of trainees in simulation environments. Collectively, these findings suggest that while enthusiasm for digital transformation exists, structured faculty development remains an essential foundation for meaningful AI integration.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2. Status of AI Integration in Maritime Education\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eTop AI tools used (N = 50)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI tool\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (f)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimulation-based AI tools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenerative AI (e.g., ChatGPT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdaptive learning platforms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutomated grading systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictive analytics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote\u003c/em\u003e: Percentages exceed 100% because respondents could select multiple AI tools.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e that simulation-based AI tools are the most widely used (61%), highlighting the importance of simulators in maritime training. Generative AI (41%) and adaptive learning platforms (39%) are also gaining ground, though to a lesser extent. Automated grading systems (35%) and predictive analytics (18%) remain less common, indicating that while simulation dominates, other AI applications are still in the early stages of adoption.\u003c/p\u003e\u003cp\u003eThese findings are consistent with Karimi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who confirmed that AI-enhanced simulation remains the cornerstone of maritime training, improving decision-making and engagement. Similarly, Sharma et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that AI chatbots enhance learning retention by providing interactive guidance in collision regulations (COLREGs) courses.\u003c/p\u003e\u003cp\u003eThe data also affirm Scanlan et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found that digital simulators and AI-driven systems have become the leading tools driving MET modernization. Overall, while maritime faculty have begun integrating AI, institutional readiness still dictates adoption levels.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3. Application of AI in Maritime Instruction\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eApplication of AI (Mean Scores, 4-point scale)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApplication Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (1–4 scale)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContent delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAgree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAgree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimulation and skill development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStrongly Agree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIt can be gleaned in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e that AI is most strongly applied in simulation and skill development (M = 3.32, interpreted as \u003cem\u003eStrongly Agree\u003c/em\u003e), which highlights its central role in maritime instruction. This result underscores how simulation-based AI technologies provide realistic, interactive training environments where cadets can develop decision-making and technical competencies in risk-free contexts. Content delivery also scored high (M = 3.15, \u003cem\u003eAgree\u003c/em\u003e), indicating that AI is increasingly supporting instructional delivery through adaptive learning platforms and generative content tools. Student assessment scores are slightly lower (M = 2.97, \u003cem\u003eAgree\u003c/em\u003e), suggesting more cautious adoption of AI in evaluating student performance compared to its use in simulations and teaching delivery.\u003c/p\u003e\u003cp\u003eThese results are consistent with the findings of Karimi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who reported that AI-based adaptive instructional systems in maritime safety training enhance engagement and decision-making under simulated conditions. Similarly, Cristina Campos Toresano et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) emphasized that simulation-centered learning aligns maritime competencies with automation and the evolving needs of the global shipping industry. In higher education contexts, Almasri (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) noted that AI applications in science and technical education enhance experiential learning through interactive simulations, but their assessment applications remain limited due to validity concerns.\u003c/p\u003e\u003cp\u003eThe moderate use of AI for student assessment mirrors ethical and accuracy concerns raised by Khlaif et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who observed that educators often hesitate to rely on AI for grading because of transparency and fairness issues. Vera (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) likewise found that faculty members perceive AI tools as effective for formative feedback but are reluctant to delegate high-stakes evaluation tasks to automated systems. Together, these findings illustrate that while simulation represents the most mature form of AI integration in maritime education, applications in assessment and content delivery are progressing more cautiously due to ethical and pedagogical considerations.\u003c/p\u003e\u003cp\u003e\u003cb\u003e4. Challenges Encountered in AI Integration\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eChallenges in AI Integration (Mean Scores, 4-point scale\u003c/em\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChallenge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimited infrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthical/data privacy concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh cost of AI tools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResistance to adoption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe findings in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that the most pressing challenge encountered by faculty members in integrating AI into maritime instruction is the lack of training (M = 3.45), indicating that most instructors feel underprepared to effectively utilize AI tools in their teaching. This is followed by limited infrastructure (M = 3.27), such as insufficient access to updated hardware, software, and reliable internet. Ethical and data privacy concerns (M = 3.15) also emerged as a notable issue, while high cost of AI tools (M = 3.08) and resistance to adoption (M = 2.85) were reported as relatively less pressing but still relevant. The trend suggests that while faculty acknowledge the value of AI, their ability to integrate it is constrained primarily by lack of institutional support in training and resources.\u003c/p\u003e\u003cp\u003eThese findings are consistent with recent studies. Fundi, Sanusi, Oyelere \u0026amp; Ayere (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that in-service teachers in Kenya reported preparedness and training gaps as primary barriers to integrating AI in competence-based curricula. Khlaif et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) report that educators are cautious about adopting AI in assessment due to concerns about fairness, transparency, and validity of outputs. Almasri (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in her systematic review noted that while AI has potential in science education, lack of infrastructure and training still impede its full adoption. Collectively, these results reinforce the idea that faculty readiness is not solely a matter of individual competence but depends on systemic institutional support. Addressing these challenges will therefore require sustained investment in structured professional development and infrastructural upgrading, ensuring that AI integration in maritime education is both effective and ethically responsible.\u003c/p\u003e\u003cp\u003e\u003cb\u003e5. Future Prospects for AI in Maritime Education\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eFaculty Prospects for AI Integration\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProspect Statement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI will play a central role in future maritime education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI feel optimistic about AI’s long-term role\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFaculty will require ongoing training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI will improve personalized learning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOpen-ended themes\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTraining (6 mentions), Simulation/Digital Twins (6), Ethics/Policy (4), Curriculum Change (1), Assessment (1)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the mean ratings of respondents regarding their envisioned future role of AI in maritime education. The results indicate that faculty members largely agree that AI will play a central role in maritime instruction (M = 3.44, SD = 0.71) and express optimism about its long-term role in higher education (M = 3.35, SD = 0.70). Respondents also emphasized the importance of ongoing training (M = 3.25, SD = 0.60) and noted AI’s potential to enhance personalized learning opportunities (M = 3.17, SD = 0.66). These findings suggest that instructors generally perceive AI as a positive force in the future of maritime education, though they stress the need for sustainable faculty upskilling.\u003c/p\u003e\u003cp\u003eBeyond the scaled responses, the open-ended question revealed recurring themes in how faculty envision AI’s future. The most frequently mentioned were the need for training (6 mentions), greater use of simulation and digital twins (6 mentions), and the importance of ethics and policy guidelines (4 mentions). Other areas noted include curriculum change and AI-driven assessment. Sample faculty responses include: \u003cem\u003e“Real-time situation adjustments from AI should be integrated on simulator-based systems,”\u003c/em\u003e, \u003cem\u003e“Teachers have to have enough training first and then specific guidelines or policies should be drafted\u003c/em\u003e, and \u003cem\u003e“Maximizing AI in streamlining instructional content, assessment, and feedback (at least for general education\u003c/em\u003e)\u003cem\u003e”.\u003c/em\u003e These highlight the dual emphasis on technological innovation and policy preparedness.\u003c/p\u003e\u003cp\u003eThe combined findings illustrate that while faculty members are optimistic about AI’s transformative role, they consistently call for institutional investments in professional development, simulation infrastructure, and ethical frameworks to ensure responsible adoption. This finding is consistent with Funa and Gabay (2024), who emphasized that policy coherence and faculty readiness are vital for the sustainable use of AI in higher education. Similarly, Karimi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) underscored that simulation-based AI systems, especially those using adaptive algorithms, will define the next generation of maritime training, a vision echoed by the respondents’ references to digital twins and real-time adaptive scenarios.\u003c/p\u003e\u003cp\u003eFaculty optimism regarding AI’s potential aligns with Ofosu-Ampong (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who reported that higher education instructors view AI as a long-term enabler of personalized, efficient, and competency-based learning. The emphasis on continuous training and curriculum renewal also parallels Upadhyay and Sah (2025), who found that faculty readiness and professional development are indispensable for effective AI integration. Additionally, Aly Salem and Hassan (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) demonstrated that AI-driven training tools significantly enhance maritime trainees’ performance, supporting the participants’ belief that simulator-centered instruction will remain foundational in future maritime education.\u003c/p\u003e\u003cp\u003eThe attention to ethical frameworks reflects growing consensus in higher education scholarship. Castelló-Sirvent et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) argued that integrating AI ethics into curriculum design is necessary to maintain trust, transparency, and accountability in teaching and assessment, concerns likewise raised by faculty respondents in this study. Furthermore, Matos et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) noted that future AI integration must be accompanied by explicit strategies for digital ethics and sustainable pedagogy to balance innovation with institutional responsibility.\u003c/p\u003e\u003cp\u003eOverall, the findings reveal that AI integration in maritime higher education is still at a formative stage but developing in promising directions. Faculty rely most on AI-driven simulators for skill development, while applications in content delivery and assessment remain limited. Consistent with Karimi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Aly Salem and Hassan (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), simulation-based AI tools are recognized for enhancing decision-making and engagement, yet infrastructural and ethical constraints persist. The lack of structured, continuous training, highlighted by Fundi et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Funa and Gabay (2024), continues to hinder widespread adoption. Despite these barriers, faculty express strong optimism toward AI’s long-term prospects in maritime instruction, aligning with Upadhyay and Sah (2025) and Matos et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who emphasize curriculum modernization, faculty capacity building, and institutional support as the cornerstones of effective AI integration.\u003c/p\u003e\u003cp\u003eIn sum, AI integration in maritime education is marked by innovation tempered by challenges but driven by a strong commitment to pedagogical advancement. The outlook remains positive: maritime faculty envision a future where AI enables personalized, simulation-rich, and ethically grounded learning environments that advance both maritime competence and educational transformation.\u003c/p\u003e"},{"header":"Conclusion and Implications","content":"\u003cp\u003eThis study examined the integration of artificial intelligence (AI) in maritime higher education institutions (MHEIs) in Region III, Philippines, focusing on the status, applications, challenges, and future prospects of AI in instruction. The results revealed that AI adoption is emerging but remains uneven across instructional domains. Simulation-based tools are most widely used, underscoring their effectiveness in enhancing situational awareness, decision-making, and practical competence among cadets. However, the use of AI for content delivery, assessment, and predictive analytics remains moderate, reflecting constraints in infrastructure, policy support, and faculty readiness.\u003c/p\u003e\u003cp\u003eThe findings highlight that while most maritime instructors express optimism toward AI’s potential, gaps in structured training and institutional preparedness persist. Faculty members recognize AI’s capacity to modernize maritime training and align education with global industry standards but emphasize the need for sustained capacity-building, ethical governance, and curriculum alignment. These conclusions are consistent with recent scholarship (Karimi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) underscoring that technological adoption in specialized disciplines such as maritime education must be guided by human-centered and pedagogically grounded approaches.\u003c/p\u003e\u003ch2\u003eRecommendations\u003c/h2\u003e\u003cp\u003eTo ensure systematic and responsible AI integration in maritime education, the following actions are recommended:\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFaculty Training and Professional Development.\u003c/b\u003e Institutions should institutionalize continuous AI literacy and pedagogical training programs to strengthen instructors’ digital competence and instructional confidence.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInfrastructure and Resource Enhancement.\u003c/b\u003e MHEIs should invest in robust digital infrastructure and simulation technologies to support effective and equitable AI deployment across maritime programs.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCurriculum Alignment.\u003c/b\u003e AI integration should be embedded into curricular design, particularly within simulation, navigation, and safety modules, ensuring alignment with international maritime standards and accreditation frameworks.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePolicy and Ethical Frameworks.\u003c/b\u003e Institutional policies must address issues of data privacy, algorithmic fairness, and intellectual property to guide ethical AI use in instruction and assessment.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCollaborative Research and Industry Partnerships.\u003c/b\u003e Strengthening linkages with maritime industries, technology providers, and international MET institutions will foster collaborative innovation, research, and the exchange of best practices.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003eBy pursuing these recommendations, maritime education institutions can foster a balanced and future-ready ecosystem—one that leverages the potential of AI while safeguarding pedagogical integrity, ethical responsibility, and the professional growth of educators.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Statement:\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency, commercial, or not-for-profit sectors. The study was self-funded by the author as part of her doctoral dissertation requirements at Bataan Peninsula State University.\u003c/p\u003e\u003cp\u003eDeclaration of Conflict of Interest:\u003c/p\u003e\u003cp\u003eThe author declares no conflict of interest related to the conduct, authorship, or publication of this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution:\u003c/h2\u003e\u003cp\u003eThe author solely conceptualized, designed, gathered data, analyzed results, and wrote the entire manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThe author expresses sincere gratitude to the participating maritime higher education institutions and faculty members in Region III, Philippines, for their invaluable cooperation and time. Appreciation is also extended to the research advisers and academic mentors from Bataan Peninsula State University for their guidance and constructive feedback throughout the conduct of this study.\u003c/p\u003e\u003cp\u003eThis study was approved by the Ethics Review Committee of Bataan Peninsula State University. Participation was voluntary, with informed consent obtained from all respondents. No identifying data were collected, and all procedures complied with the Data Privacy Act of 2012 and relevant ethical research standards.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmasri, F. (2024). Exploring the Impact of Artificial Intelligence in Teaching and Learning of Science: A Systematic Review of Empirical Research. \u003cem\u003eResearch in Science Education\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(1). https://doi.org/10.1007/s11165-024-10176-3\u003c/li\u003e\n\u003cli\u003eAly Salem, A. M., \u0026amp; M. Hassan, M. H. (2025). 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Design and implementation of AI chatbot for COLREGs training. \u003cem\u003eWMU Journal of Maritime Affairs, 21\u003c/em\u003e(3), 415\u0026ndash;433. https://doi.org/10.1007/s13437-022-00284-0\u003c/li\u003e\n\u003cli\u003eSimmons, E., \u0026amp; McLean, G. (2020). Understanding the paradigm shift in maritime education. \u003cem\u003eWorldwide Hospitality and Tourism Themes, 12\u003c/em\u003e(1), 90\u0026ndash;97. https://doi.org/10.1108/WHATT-10-2019-0062\u003c/li\u003e\n\u003cli\u003eUpadhyay, A., \u0026amp; Abhilasha Sah. (2025). Faculty Perceptions of AI-Based Learning Tools and Their Integration in Higher Education Teaching Practices. \u003cem\u003eInternational Journal for Multidisciplinary Research\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3). https://doi.org/10.36948/ijfmr.2025.v07i03.48383\u003c/li\u003e\n\u003cli\u003eVera, F. (2023, September 15). \u003cem\u003eFaculty Members\u0026rsquo; Perceptions of Artificial Intelligence in Higher Education: A Comprehensive Study\u003c/em\u003e. Https://Revistatransformar.cl/Index.php/Transformar; Transformar Electronic Journal. file:///C:/Users/ACER/Downloads/148.pdf\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Bataan Peninsula State University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence (AI), AI Integration, Curriculum Planning, Maritime Education, Simulation-Based Learning","lastPublishedDoi":"10.21203/rs.3.rs-7827795/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7827795/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the integration of Artificial Intelligence (AI) in maritime higher education institutions (MHEIs) in Region III, Philippines. Guided by five research questions, it examined the demographic profile of instructors, the status of AI integration, its applications in teaching, the challenges encountered, and the future prospects envisioned by faculty. A descriptive research design was employed, using a validated survey instrument administered to 50 maritime faculty members. Descriptive statistics, frequency distributions, weighted means, and thematic coding of open-ended responses were used in data analysis. Findings revealed that while simulation-based AI tools were the most widely used, adoption remained uneven across instructional domains. AI was strongly applied in simulation and skill development, followed by content delivery and assessment. The major challenges identified included a lack of training, infrastructure limitations, and ethical concerns. Faculty members were optimistic about AI\u0026rsquo;s prospects but emphasized the importance of continuous training, simulator investment, and clear institutional policies. The study concluded with recommendations for faculty development, curriculum alignment, and institutional support to enable responsible AI integration in maritime education.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Status, Applications, and Prospects of AI in Maritime Education: Basis for Curriculum Planning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 17:47:15","doi":"10.21203/rs.3.rs-7827795/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3947b63d-d167-4d30-8660-2dcf98ef7ea0","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56096733,"name":"Educational Philosophy and Theory"},{"id":56096734,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2025-10-14T17:47:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 17:47:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7827795","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7827795","identity":"rs-7827795","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}