
Executive Doctor of Education in Higher Education Leadership and Director with 20+ years of experience across higher education and K–12 leadership and teaching, including large-scale workshops, seminars, webinars, and professional-development initiatives reaching 100+ participants, university leaders, program directors, administrators, faculty, educators, and graduate students, and graduate courses serving 20+ students per course.
Designs and leads evidence-based, interactive in-person, virtual, and hybrid learning experiences (using Canvas, Blackboard, Zoom, Power Automate and Microsoft 365/Google Workspace) that move diverse stakeholders from research and theory to applied practice and completed professional projects, often within a few hours, while meeting defined learning objectives and advancing productivity, innovation, digital capabilities, and technology adoption. Uses needs analysis and pre-assessments to identify stakeholder priorities, establish measurable objectives, differentiate learning, and tailor curriculum, technologies, activities, and outcomes.
Skilled in translating complex research, AI, emerging technologies, ethics, leadership, and higher education challenges into practical, scalable, real-world applications. Experienced in curriculum and program development, stakeholder alignment, instructor and subject-matter-expert coordination, pre-/post-assessment, learning-outcome measurement, program evaluation, digital platforms and websites (Microsoft Power Automate, SharePoint, Microsoft Lists, GradePro, Adobe Photoshop, and Adobe Dreamweaver), documentation and data collection, budgeting and resource allocation, and evidence-based continuous improvement. Leverages generative AI and emerging tools (ChatGPT, Claude, Gamma, NotebookLM, Sora, Adobe Firefly, Canva Magic Write, Flora, Visme, Pitch, and Otter) for AI-assisted instructional design, content creation, research support, and productivity.
Designed, published, and implemented Artificial Intelligence Ethics and Literacy for Under-Resourced Higher Education Institutions (University of Miami, 2026), a doctoral dissertation focused on aligning university stakeholders around ethical AI frameworks and advancing digital and technological literacy in support of equitable student success. The research produced an open-access AI Literacy and Ethics Toolkit featuring 10 training modules, pre- and post-assessments, ethical AI rubrics, case studies, reflective activities, faculty and student guidance, administrative planning resources, and scalable implementation strategies to strengthen institutional readiness, stakeholder alignment, responsible AI adoption, and continuous improvement. The toolkit was developed and disseminated in web-based and LMS-compatible formats using digital learning and AI-supported tools such as Gamma, NotebookLM, Claude, Canva Magic Write, Visme, and Adobe Firefly.
University of Miami / School of Education and Human Development, Miami, Florida / July 2026 / GPA: 3.91 | Degree Honor: Award of Academic Merit
Doctoral Dissertation: Artificial Intelligence Ethics and Literacy for Under-Resourced Higher Education Institutions. Conducted applied doctoral research examining AI literacy, ethics, governance, institutional readiness, policy, access, and responsible AI implementation in higher education. Designed, developed, implemented, and evaluated an open-access Artificial Intelligence Literacy and Ethics Toolkit for students, faculty, administrators, advisors, librarians, career professionals, and institutional leaders. Curriculum modules, case studies, assessments, and resources were developed and delivered with digital and AI-supported tools including Gamma, NotebookLM, Claude, Canva Magic Write, Visme, Pitch, and Adobe Firefly, with web-based content built in Adobe Dreamweaver and Adobe Photoshop and distributed in LMS-compatible formats (Canvas, Blackboard). Data and assessments were organized with Microsoft Excel, Microsoft Lists, and SharePoint. Applied research evidence, assessment findings, and stakeholder feedback to evaluate outcomes, strengthen readiness and alignment, and guide continuous improvement.
Pepperdine University / Graduate School of Education and Psychology, Los Angeles, California / July 2020 / GPA: 3.94
Central University of Venezuela / Caracas, Venezuela / December 2004 / GPA: 3.00
Award of Academic Merit, University of Miami, 2026
University of Miami, 2025
University of Miami, 2026
Pepperdine University, 2020
Harper College, 2015
Central University of Venezuela, 2004
Clinical Associate Professor of Education & Program Director, Master's in Teaching English as a Foreign Language / Universidad Andrés Bello / szapata@unab.cl / Program Director and Direct Supervisor
University of Miami / Abotero@miami.edu / Former Professor
Senior Research Associate, Behavioral Science Research Institute (BSRI) / University of Miami / jvertovec@bsrinstitute.org / Dissertation Committee Member
Lecturer and Researcher, Intensive English Program / University of Miami / tap172@miami.edu / Co-instructor and Research Collaborator
A DISSERTATION
Coral Gables, Florida | July 2026
©2026 Laura Wise | All Rights Reserved
By Laura Wise
___________________________
Carol-Anne Phekoo, Ph.D.
Clinical Associate Professor of Education
___________________________
Todd Warner, Ph.D.
Senior Lecturer Professor of Education
___________________________
Jack Vertovec, Ph.D.
Senior Research Associate, Behavioral Science Research Institute (BSRI)
___________________________
Scotney D. Evans, Ph.D.
Associate Professor of Education
___________________________
Dean of the Graduate School
University of Miami
Abstract of a dissertation at the University of Miami
Dissertation supervised by: Dr. Carol-Anne Phekoo
Degree: Ed.D., Higher Education Leadership
No. of pages: 65 Date: July 2026
The rapid expansion of AI in higher education creates major opportunities, yet under-resourced institutions face heightened risks of inequitable and unethical adoption. A comprehensive meta review of studies conducted in higher education settings revealed that 40.9% of the studies reported ethical gaps, 29% cited lack of ethics as a primary barrier, and 65% indicated weak foundations without frameworks; misuse persisted, with 17% of academic papers containing ≥20% AI-generated content and 5% exceeding 80% (Bond et al., 2024; Bennett et al., 2025; Rafiq & Qurat-ul-Ain, 2025). Readiness gaps are substantial: 92% of students use AI, but only 36% receive training, about 10% of institutions provide guidance, 78.5% of faculty need training, and 85.8% report their institutions do not promote ethical AI (Alnsour et al., 2025; Mazaheriyan & Nourbakhsh, 2025; UNESCO, 2023). Grounded in major developmental, sociocultural, engagement, and equity theories, this dissertation shows structured AI ethics instruction is a high-impact practice linked to stronger engagement, ethical reasoning, and learning outcomes, with 80% of faculty supporting ethics instruction, 51% of studies reporting positive effects, and 32.3% improved outcomes (Alnsour et al., 2025; Bond et al., 2024). The project therefore introduces an open-access, cross-platform AI Literacy and Ethics Toolkit to strengthen institutional capacity, support responsible AI integration, and promote equitable student success.
Studies reporting ethical gaps in AI adoption
Cited lack of ethics as a primary barrier
Institutions with weak AI frameworks
Students using AI tools
Students receiving AI training
Institutions providing AI guidance
Faculty needing AI training
Institutions not promoting ethical AI
Faculty supporting ethics instruction
Studies reporting positive effects of AI ethics instruction
Improved learning outcomes with structured AI ethics
Sources: Bond et al., 2024; Alnsour et al., 2025; UNESCO, 2023; Bennett et al., 2025
This dissertation introduces a cross-platform, open-access AI Literacy and Ethics Toolkit designed to:
Equip students, faculty, administrators, and leaders with ethical AI frameworks
Address governance, academic integrity, privacy, bias, and transparency
Ensure digital equity and accessible AI education for all learners
Train-the-trainer programs, consortium models, and grant-funding strategies for HBCUs, MSIs, and community colleges
University of Miami | Ed.D. in Higher Education Leadership | July 2026 | Laura Wise
The toolkit serves four primary stakeholder groups, each with a dedicated learning pathway:
Build AI literacy, avoid plagiarism, develop ethical reasoning.
Pathway: Pre-Assessment → Modules 1, 2, 3, 6 → Ethical AI Rubric → Post-Assessment
Integrate ethical AI into course design, assignments, and grading.
Pathway: Module 1 → Modules 2, 3, 4 → Assessment Examples → Rubric → Module 10
Develop governance policies and scalable AI literacy programs.
Pathway: Module 5 → Modules 4, 10 → Governance Framework → Task Force
Vet AI tools, ensure FERPA/GDPR compliance, manage data agreements.
Core principles of fairness, transparency, and accountability. Includes case studies (Amazon hiring bias), debates, and activities. For all stakeholders.
Faculty-focused extension covering ethical AI writing workflows, academic integrity protection, critical thinking preservation, and case study-based learning (COMPAS, deepfakes, student AI essays).
Develops ability to critically assess AI outputs for accuracy, bias, and hallucinations. Teaches the SIFT method (Stop, Investigate, Find better sources, Trace claims). For students and faculty.
Ethical frameworks for data handling: informed consent, privacy, data security, data minimization. Covers FERPA risks with tools like Otter.ai. For students, faculty, and IT staff.
Working knowledge of GDPR, EU AI Act, FERPA, UNESCO AI Ethics, and IEEE Ethically Aligned Design. For administrators, faculty, and IT staff.
Institutional AI oversight structures, task force roles and responsibilities, policy development, and annual review processes. For administrators (primary), faculty, and IT staff.
Bridges theory and practice through real-world case studies (Amazon, facial recognition, COMPAS), ethical dilemmas (privacy vs. security, efficiency vs. equity), and scenario-based activities. Also covers AI business models, corporate interests, and potential harms including cognitive offloading, unequal access, vendor dependency, and hidden labor.
Pre- and post-assessment tools using the AT-EAI Likert-scale instrument (17 items) to measure growth in AI literacy, ethical reasoning, and critical thinking. Deployable via LMS. For faculty and administrators.
Standardized rubric assessing four criteria (Accuracy & Critical Evaluation, Bias Awareness, Transparency & Citation, Responsible Use) across four performance levels (Emerging → Developing → Proficient → Advanced). Includes a full descriptor table and 10 ready-to-use faculty assessment examples (essays, bias analysis papers, policy briefs, AI audits, oral defenses, discussion boards, etc.).
Step-by-step ethical workflows for 10 widely used AI tools: ChatGPT, Google NotebookLM, OpenAI Sora, Adobe Firefly, Gamma, Otter.ai, Canva Magic Write, Flora AI, Visme, and Pitch. Ethics embedded throughout, not treated as an add-on.
Step-by-step faculty and administrator implementation guides, task force annual review process, and continuous improvement framework. Designed for plug-and-play use with free tools and LMS integration.
Completed before engaging with the toolkit. Includes a 6-item Likert self-rating scale, prior AI tool experience checklist, and 3 short-response questions. Establishes baseline AI literacy.
Completed after the toolkit. Mirrors the pre-assessment for growth comparison. Includes an applied scenario task (ChatGPT hallucination case), 5 reflection questions, and 4-item toolkit feedback rating scale.
Author-developed templates for students, faculty/professionals, and a "no AI used" statement. Designed as an educational and ethical practice, not a punishment tool. Includes ethical reflection questions and a toolkit purpose statement.
Faculty-focused guide covering how to teach ethical AI use, AI citation standards (APA 7th), AI-resistant assignment design, real-world case studies, and critical thinking strategies (Critique-First Method, Socratic AI Dialogue, Revision as Learning).
Concrete examples of how each group uses the toolkit in practice across higher education.
Extensive peer-reviewed bibliography spanning AI ethics & bias, AI in higher education, legal & policy frameworks, and assessment & literacy literature.
Start Here: Pre-Assessment → Modules 1, 2, 3, 6 → Ethical AI Rubric → Post-Assessment
Start Here: Module 1 → Modules 2, 3, 4 → Assessment Examples → Rubric → Module 10
Start Here: Module 5 → Modules 4, 10 → Governance Framework → Task Force
All modules are open-access, mobile-friendly, and require no prior AI expertise. © Laura Wise.
LAURA WISE, Ed.D.