Ethical Accountability and Stakeholder Engagement in Higher Education Teaching and Learning: Applying Adaptive Leadership Theory at Columbia University
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866901584601939968 |
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| author | Fatile, Mopelola |
| author_facet | Fatile, Mopelola |
| contents | <p><strong><span>Just Released:</span></strong></p> <p><strong><span>Ethical Accountability and Stakeholder Engagement in Higher Education Teaching and Learning</span></strong></p> <p><strong><span>Applying Adaptive Leadership Theory at Columbia University</span></strong></p> <p><span>Ethical standards in higher education are often written with clarity, specificity, and formal structure into institutional documents. Yet the way those standards are interpreted and applied can differ depending on the roles and responsibilities of faculty members, students, administrators, and staff. This study examines Columbia University’s generative AI policy using Adaptive Leadership Theory to explore how leadership responsibilities relate to institutional ethics and shared governance.</span></p> <p><strong><span>Research Focus</span></strong><span> Through a qualitative archival approach, the study examines how faculty, students, and administrators interpret and apply Columbia’s generative AI policy. While the policy outlines expectations for academic integrity and AI use, its implementation reveals persistent gaps—particularly in faculty preparation, student participation, and uneven enforcement.</span></p> <p><strong><span>Key Findings</span></strong></p> <ul> <li><span>Written standards alone do not ensure ethical accountability; leadership must engage stakeholders and respond to emerging challenges.</span></li> <li><span>Faculty preparation and student participation remain inconsistent, creating friction in how the policy is applied.</span></li> <li><span>Feedback structures and flexible leadership practices are essential for maintaining trust and ethical clarity.</span></li> </ul> <p><strong><span>Recommendations</span></strong><span> To support ethical responsibility and build trust, the study proposes:</span></p> <ul> <li><span>Integrating adaptive leadership principles into faculty development</span></li> <li><span>Establishing formal faculty–student advisory bodies</span></li> <li><span>Creating transparent review and feedback processes</span></li> </ul> <p><strong><span>Conclusion</span></strong><span> Generative AI in higher education requires leadership that is responsive, inclusive, and ethically precise. Columbia’s case affirms that adaptive leadership offers a practical structure for guiding ethical standards in AI-supported teaching and learning.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17534854 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Ethical Accountability and Stakeholder Engagement in Higher Education Teaching and Learning: Applying Adaptive Leadership Theory at Columbia University Fatile, Mopelola leadership higher education ethics accountability stakeholder engagement teaching and learning <p><strong><span>Just Released:</span></strong></p> <p><strong><span>Ethical Accountability and Stakeholder Engagement in Higher Education Teaching and Learning</span></strong></p> <p><strong><span>Applying Adaptive Leadership Theory at Columbia University</span></strong></p> <p><span>Ethical standards in higher education are often written with clarity, specificity, and formal structure into institutional documents. Yet the way those standards are interpreted and applied can differ depending on the roles and responsibilities of faculty members, students, administrators, and staff. This study examines Columbia University’s generative AI policy using Adaptive Leadership Theory to explore how leadership responsibilities relate to institutional ethics and shared governance.</span></p> <p><strong><span>Research Focus</span></strong><span> Through a qualitative archival approach, the study examines how faculty, students, and administrators interpret and apply Columbia’s generative AI policy. While the policy outlines expectations for academic integrity and AI use, its implementation reveals persistent gaps—particularly in faculty preparation, student participation, and uneven enforcement.</span></p> <p><strong><span>Key Findings</span></strong></p> <ul> <li><span>Written standards alone do not ensure ethical accountability; leadership must engage stakeholders and respond to emerging challenges.</span></li> <li><span>Faculty preparation and student participation remain inconsistent, creating friction in how the policy is applied.</span></li> <li><span>Feedback structures and flexible leadership practices are essential for maintaining trust and ethical clarity.</span></li> </ul> <p><strong><span>Recommendations</span></strong><span> To support ethical responsibility and build trust, the study proposes:</span></p> <ul> <li><span>Integrating adaptive leadership principles into faculty development</span></li> <li><span>Establishing formal faculty–student advisory bodies</span></li> <li><span>Creating transparent review and feedback processes</span></li> </ul> <p><strong><span>Conclusion</span></strong><span> Generative AI in higher education requires leadership that is responsive, inclusive, and ethically precise. Columbia’s case affirms that adaptive leadership offers a practical structure for guiding ethical standards in AI-supported teaching and learning.</span></p> |
| title | Ethical Accountability and Stakeholder Engagement in Higher Education Teaching and Learning: Applying Adaptive Leadership Theory at Columbia University |
| topic | leadership higher education ethics accountability stakeholder engagement teaching and learning |
| url | https://doi.org/10.5281/zenodo.17534854 |