The difference between generative AI and human academic writing
There has been lots and lots of information and debate about the capability of generative AI tools such as ChatGPT in academic writing. I have published several youtube videos on how to use such tools. Many has been publishing posts and videos advocating the use of such tools in academic writing and publications. In this blog post, I just thought of sharing the text of the Discussion and Conclusion sections of a paper that I am working generated by ChatGPT and written by me. I consider myself well versed with high quality prompts for generative AI. The lest side of the following table we have the text generated by chatGPT and on the rightside text written by me (will be available after the paper is published).
| ChatGPT 4o | Text written by me |
| 5. Discussion The findings of this study offer valuable insights into the barriers hindering the adoption of Maritime Autonomous Surface Ships (MASS) by systematically identifying and prioritizing key challenges. The Importance-Improvement (A-B) analysis provided a structured approach to distinguish between critical, enabling, non-critical, and trivial barriers. In this section, we discuss the contributions of our findings to the literature and their implications for industry and policy. 5.1 Contribution to Literature This study makes several contributions to the growing body of literature on autonomous shipping. By systematically reviewing prior studies, we identified 60 barriers to MASS adoption and classified them into four main themes: (1) human factors, (2) data and risk management, (3) technology and connectivity, and (4) operations and policy. The A-B analysis provided a unique perspective by quantifying both the perceived importance and the need for improvement of these barriers, thus offering a novel framework for future research in the field. One of the key contributions of this study is the identification of trust-related issues as the most critical human factor barrier. Prior research has discussed the importance of trust in automation (Gu & Wallace, 2021; Wang et al., 2022), but our findings empirically highlight that trustworthiness of autonomous technology (SOC2) and building and maintaining trust in autonomous systems (SOC4) are among the most critical barriers. Additionally, the discrepancy between academic and industry perspectives on these factors, as revealed in Appendix B, suggests that academics might underestimate the industry’s reluctance to fully embrace automation. From a technological perspective, our findings align with previous studies that emphasize cybersecurity risks (Bou-Harb et al., 2017; Kennard et al., 2022) and the need for reliable connectivity (Longo et al., 2023) as critical challenges. Cyberattacks and cybersecurity risks (CCN1) were identified as the top concerns, reinforcing the argument that MASS must be developed with robust security measures to gain industry acceptance. Regulatory barriers emerged as one of the major roadblocks to MASS adoption, corroborating prior work that highlights the inadequacy of existing maritime regulations (Hannaford et al., 2022; Choi & Lee, 2022). Uncertainty regarding the effectiveness of the existing regulatory framework for MASS (REG1) and regulatory compliance in system development and deployment (REG4) were ranked as critical barriers. These findings support calls for regulatory harmonization to facilitate MASS operations in international waters. 5.2 Implications for Industry and Policy The findings have several implications for industry stakeholders and policymakers. First, given the significance of trust-related barriers, industry actors should actively engage in transparent communication with stakeholders to increase trust in MASS technologies. This includes clear communication of safety features, reliability measures, and contingency plans for system failures. Additionally, training programs for maritime professionals should incorporate automation-specific modules to bridge the skill gap. Second, cybersecurity and data management require immediate attention. The identification of cybersecurity risks as a critical barrier implies that industry players must allocate significant resources toward enhancing cybersecurity frameworks. Blockchain-based solutions, as suggested by Wang et al. (2022), could be explored to strengthen the security of autonomous systems. Third, regulatory agencies must accelerate the development of comprehensive MASS regulations. The upcoming IMO MASS Code (International Maritime Organization, 2025) is a step in the right direction, but more efforts are needed to establish clear legal responsibilities for remote operators, ensure liability frameworks, and facilitate cross-border collaborations. Finally, port infrastructure must be adapted to accommodate autonomous vessels. Our study identifies the need for MASS-facilitating port infrastructure (POF1) as an enabling barrier, suggesting that investment in smart port technologies will enhance the feasibility of autonomous shipping. Port authorities should work closely with technology developers to integrate MASS-friendly docking and communication systems. 6. Conclusion This study explored the barriers to the commercial adoption of Maritime Autonomous Surface Ships (MASS) by conducting a systematic literature review and employing the Importance-Improvement (A-B) analysis. We identified 60 barriers categorized into four themes: human factors, data and risk management, technology and connectivity, and operations and policy. Our findings provide valuable insights for academia, industry practitioners, and policymakers. The research addressed two key questions: (RQ1) What are the barriers to the commercial adoption of MASS? and (RQ2) Which of these barriers are most critical to the commercial adoption of MASS? In response to RQ1, we identified and classified a comprehensive list of barriers that hinder the transition to autonomous shipping. In response to RQ2, our A-B analysis revealed that critical barriers include trustworthiness of autonomous technology, cybersecurity risks, loss of autonomous control, and regulatory compliance challenges. Despite its contributions, this study has certain limitations. First, the sample size of 37 respondents may limit the generalizability of the findings. Future research could expand the sample to include a more diverse representation of maritime stakeholders across different regions. Second, the study focused on perceived importance and improvement needs but did not assess actual implementation efforts to overcome these barriers. Further research could examine how industry actors are actively addressing these challenges. Future research should explore the economic feasibility of MASS in greater detail, particularly the cost-benefit analysis of transitioning from conventional to autonomous shipping. Additionally, studies on real-world pilot projects and their outcomes would provide empirical evidence of how MASS technologies perform in operational settings. Finally, interdisciplinary research integrating maritime law, artificial intelligence, and behavioral sciences could offer deeper insights into the regulatory and human-technology interaction aspects of MASS adoption. By systematically analyzing the barriers to autonomous shipping and their industry implications, this study provides a solid foundation for future research and policy development aimed at facilitating the adoption of autonomous vessels in commercial shipping. |
Do you notice any difference? What is your opinion?