LinkedIn Resources - Technology in Higher Education

 

Asynchonous AI-POWERED Professional Learning Programs

An asynchronous online class is a form of online education that does not require students to log in and attend lectures or participate in discussions at a specific, real-time schedule. Students can access course materials like pre-recorded lectures, readings, and assignments at their own pace and on their own schedule, as long as they meet deadlines. This flexibility is ideal for those balancing education with work or other commitments, or those in different time zones. 

Key characteristics

Flexible schedule: Students work on the course content on their own time, which could be in the morning, at night, or whenever it is most convenient for them. 

Self-paced learning: Students can adjust their learning speed, rewatch recorded lectures, or spend more time on challenging topics before moving on. 

Delayed interaction: Unlike synchronous classes, there is no real-time interaction. Communication and feedback occur through methods like discussion boards, which can lead to a delay in responses. 

Asynchronous materials: Content is delivered through pre-recorded videos, readings, online modules, and assignments that are accessible at any time. 

How it differs from synchronous learning

Synchronous classes require both students and instructors to be online at the same time for live sessions, such as live video lectures and real-time discussions. 

Asynchronous classes lack this real-time component, offering a more independent and self-directed learning experience. 

Benefits

Convenience: Allows students to fit their education into their busy lives. 

Flexibility: Offers more control over when and where you study. 

Time zone friendly: Works well for students who are not in the same time zone as the institution. 

Potential challenges

Requires self-discipline: Students need to be self-motivated to stay on track. 

Isolation: The lack of live interaction can lead to a feeling of isolation for some learners. 

Delayed feedback: Responses to questions or assignments are not immediate. 

AI Safety Use and Policy

 

🤖 Geotech University Adopted AI Model: The 4-P Framework (Adjusted)

Geotech University adopts the "4-P Hybrid Model" for integrating Artificial Intelligence into its learning, research, and administrative functions.

P-Factor, Definition, Geotech University Application, Focus & Goal

1. Pedagogy, AI as a Teaching/Learning Aid,"AI tools integrated into modular assignments enhance critical thinking, data analysis, and technical writing as a ""Co-Pilot"".","Enhance Learning Outcomes: Use AI to accelerate research and synthesis, but mandate citation and verification of all output."

2. Policy,Governance and Ethical Use,"Creation of clear guidelines for submission integrity, data privacy, and intellectual property (IP) rights concerning AI-generated content.",Ensure Academic Integrity: Define permissible AI limits in submissions and establish clear consequences for misuse (see Safety Policy).

3. Practical Research, AI as a Research Accelerator,"Faculty and doctoral students utilize AI for high-level tasks like hypothesis generation, large-scale data modeling, and literature gap analysis.","Drive Innovation: Leverage AI to increase research output and efficiency, requiring mandatory transparency regarding the specific models used."

4. Privacy & Protection,Data Security and Safety,"Strict controls on input data to protect PII, proprietary research, and university data from interaction with external LLMs.",Maintain Data Integrity: Prohibit the input of confidential University or research data into non-vetted public AI platforms.

🚨 Geotech University AI Responsible Use and Safety Policy (Continued)

1. Academic Integrity and Citation Policy

Students must maintain intellectual honesty when using AI tools.

• Transparency is Mandatory: Any use of an AI tool (e.g., ChatGPT, Copilot, Midjourney) in generating text, code, images, or data must be explicitly acknowledged and cited in the bibliography or footnotes.

• Verification Requirement: Students are ultimately responsible for the accuracy and originality of all submitted work. AI-generated content must be fact-checked and verified by the student. Submitting unedited, AI-generated output as original work is considered plagiarism.

• Non-Permitted Use (Default): Unless explicitly permitted by the faculty member in the assignment prompt or course syllabus, the use of AI tools for generating final answers, essays, or code solutions is prohibited.

• Assessment Misuse: Using AI to complete take-home exams, quizzes, or high-stakes assessments is classified as academic misconduct.

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2. Data Safety and Privacy Policy

Protecting institutional and personal data is paramount when interacting with AI systems.

• No Confidential Data Input: Users must not input or paste any sensitive, confidential, or proprietary information into public, third-party AI tools. This includes PII, proprietary research, and internal administrative documents.

• Vetted Platforms Only: Faculty and staff handling sensitive information are restricted to using only University-vetted and licensed AI environments.

• IP Rights: When using AI for creative or research output, users must be aware that the intellectual property (IP) rights may be complex. Geotech University asserts that any AI-generated output resulting from University resources or research falls under the existing G.U. IP policy.

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3. Faculty and Researcher Guidelines

Faculty are expected to lead by example in the responsible integration of AI.

• Syllabus Disclosure: Faculty must clearly outline the permissible and non-permissible use of AI for every course and assignment, specifically within the Modular Syllabus Template.

• Tool Selection: Faculty should prioritize and recommend AI tools that adhere to high privacy standards.

• Curriculum Adaptation: Faculty are encouraged to design assignments that require critical interaction with AI (e.g., "Analyze this AI-generated answer and identify its flaws.").

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4. Enforcement and Review

• Detection: Geotech University reserves the right to use AI detection software and internal analytics to check for undisclosed AI generation.

• Consequences: Violations of this policy will be processed under the standard Geotech University Academic Misconduct and Employee Disciplinary policies.

• Policy Review: This policy will be reviewed and updated annually by the G.U. Academic Affairs and Technology Safety Committee.

Geotech University AI Adopted Model

 

P-Factor	Definition	Geotech University Application	Focus & Goal
1. Pedagogy	AI as a Teaching/Learning Aid	AI tools are integrated into modular assignments to enhance critical thinking, data analysis, and technical writing practice, acting as a "Co-Pilot".	Enhance Learning Outcomes: Students use AI to accelerate research, synthesize dense material, and debug code, but must cite and verify all output.
2. Policy	Governance and Ethical Use	The creation of clear guidelines governing submission integrity, data privacy, and intellectual property (IP) rights related to AI-generated content.	Ensure Academic Integrity: Define the permissible limits of AI use in academic submissions and establish clear consequences for misuse (see Safety Policy below).
3. Practical Research	AI as a Research Accelerator	Faculty and doctoral students utilize AI for high-level tasks like hypothesis generation, large-scale data modeling, and literature gap analysis, particularly in technical fields.	Drive Innovation: Leverage AI tools to increase research output and efficiency, with mandatory transparency regarding the specific models used.
4. Privacy & Protection	Data Security and Safety	Strict controls on input data to protect PII (Personally Identifiable Information), proprietary research, and university data when interacting with external LLMs (Large Language Models).	Maintain Data Integrity: Prohibit the input of confidential University or research data into non-vetted public AI platforms.

Doctoral Syllabus Template

Doctoral Seminar: [Course Number & Title]

Course Administration

Faculty: [Professor's Name, Title]

Email: [Professor's Email] FACULTY: Use Geotech University/official email.

Office Hours: [Days, Times, Location (Zoom/In-person)]

Class Time/Location: [Day, Time, Room #/Virtual Link]

Program Area: [e.g., Ph.D. in Data Science, D.Eng. in Sustainable Systems]

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▶️ Faculty Instruction: Login and Syllabus Processing

Quick Start Guide: Watch this video for a demonstration of how to log in, access the data management panel, and complete the modular syllabus sections efficiently.

Watch Video: G.U. Syllabus Workflow (3 min)

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Course Overview & Objectives

[A concise paragraph describing the course, focusing on its role in the doctoral program. **Ivey-style emphasis on real-world application and decision-making**.]

FACULTY: Keep this section *highly focused*. What is the key research domain and why is this course essential for a **Geotech University** Ph.D. to master it?

Learning Objectives

• Analyze complex, ambiguous data sets to formulate defensible research hypotheses.
• Critically evaluate seminal and contemporary literature relevant to [Specific Field].
• Develop and articulate a viable research methodology for a dissertation proposal (Core Objective).

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Assessment and Grading

FACULTY: Align grade weights directly with the modular structure below.

Component	Weight	Ivey Workflow Adaption
Participation & Discussion Leadership	20%	Case preparation and defense of position.
**Modular Assignment 1: Case Analysis Report**	25%	Structured decision memo addressing a complex issue.
**Modular Assignment 2: Literature Synthesis & Gap Analysis**	30%	A rigorous foundation for the subsequent research proposal.
Final Project: Research Proposal (Cumulative)	25%	A polished, publishable proposal blueprint.

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Weekly Schedule & Modular Assignments (Completed Sample)

FACULTY: Note how the fields have been completed below. The focus is on **Deliverables and Required Actions**, not just reading lists.

MODULE 1: Foundational Concepts & Decision Framing (Weeks 1-3) - SAMPLE

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Topics Covered:

• Week 1: Introduction to **Sustainable Systems Engineering's** Grand Challenges.
• Week 2: Ethical/Societal Implications and Stakeholder Analysis in Urban Planning.
• Week 3: **Case Study Focus:** The "Smart Grid Implementation" in Austin, TX.

Required Actions:

Prepare the **Austin Smart Grid** brief, focusing on the core decision-maker's dilemma regarding initial investment vs. long-term ecological impact.

Deliverable (Assignment 1):

**DUE:** September 22, 2025 | **Weight:** 25%

Submit a 1,500-word **Case Analysis Report (Decision Memo)** recommending a specific course of action, supported by evidence from the case and required readings. (Focus: Economic Feasibility and Public Trust).

MODULE 2: Critical Literature & Theory Application (Weeks 4-7) - SAMPLE

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Topics Covered:

• Weeks 4-5: Review of Seminal Theories (System Dynamics, Resilience Theory, and Complex Adaptive Systems).
• Weeks 6-7: Identification of Methodological Limitations in current climate modeling research.

Required Actions:

Select a minimum of 20 high-impact papers on **Infrastructure Resilience** and create an annotated bibliography by Week 5.

Deliverable (Assignment 2):

**DUE:** October 20, 2025 | **Weight:** 30%

Submit a **Literature Synthesis and Gap Analysis** (3,000 words) that clearly defines an unaddressed or poorly addressed research question suitable for doctoral study. **(Example Gap:** Measuring the social cost of intermittent power in micro-grids).

MODULE 3: Proposal Development & Defense (Weeks 8-12) - TEMPLATE

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Topics Covered:

• Week 8: Quantitative vs. Qualitative Methodologies in [Field].
• Weeks 9-10: Research Design, Ethics, and Data Collection Planning.
• Weeks 11-12: Peer Review & Proposal Defense Workshops.

Required Actions:

Present a 10-minute outline of your final research proposal for peer feedback (Week 11).

Final Project: Research Proposal

**DUE:** [Date, Time of Final Exam Period] | **Weight:** 25%

A complete, formal **Doctoral Research Proposal** (5,000-7,000 words) based on the Gap Analysis from Module 2, suitable for submission to your advisory committee.

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🧑‍💻 Staff & Faculty Data Management

This section is exclusively for **Geotech University** Staff and Faculty to manage and update course-specific field data, case outcomes, or modular assignment feedback. Access requires **Geotech University** credentials.

Faculty/Staff Data Access Login

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Geotech University User ID:

Password:

Note: Login redirects to the official **Geotech University** Single Sign-On (SSO) page.

Field Data & Assignment Grade Entry

Once logged in, the following interface appears (backend requirement):

**Secure Backend Integration Required**

Option 1: Field Data Upload

(Upload CSV/Excel file for bulk field data updates related to case studies.)

Option 2: Direct Data Entry (E.g., for Assignment Grades)

-- Select Module Assignment -- Modular Assignment 1: Case Analysis Report Modular Assignment 2: Literature Synthesis

FACULTY NOTE: This section requires robust **backend development** to ensure data security, validation, and integration with the gradebook/LMS database. The current HTML serves as the user-facing container.

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Geotech University Policies

A full list of academic honesty, disability services, and classroom conduct policies can be found at: [Link to University Policy Handbook].

Accessibility: Students requiring academic accommodations should contact the **Geotech University** Student Services office immediately.

Staff LogIn

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🧑‍💻 Staff & Faculty Data Management

This section is exclusively for **Geotech University Staff and Faculty** to manage and update course-specific field data, case outcomes, or modular assignment feedback. Access requires Geotech University credentials.

Faculty/Staff Data Access Login

►

Geotech User ID:

Password:

Note: Login redirects to the official Geotech University Single Sign-On (SSO) page.

Field Data & Assignment Grade Entry

Once logged in, the following interface appears (backend requirement):

**Secure Backend Integration Required**

Option 1: Field Data Upload

(Upload CSV/Excel file for bulk field data updates related to case studies.)

Option 2: Direct Data Entry (E.g., for Assignment Grades)

-- Select Module Assignment -- Modular Assignment 1: Case Analysis Report Modular Assignment 2: Literature Synthesis

FACULTY NOTE: This section requires robust **backend development** to ensure data security, validation, and integration with the gradebook/LMS database. The current HTML serves as the user-facing container.

Elevating Doctoral Program

Elevating Doctoral Programs: Why Academic Rigor Demands Practitioner Partnership

Introducing the Global Executive Fellowship (GEF) at Geotech University.

At Geotech University, our Global Ed.D. is built on a foundation of rigorous academic standards, verified by institutional accreditation and mandates like CAEP and PSEL. Our primary challenge, therefore, isn't about *what* we teach, but how we ensure that our research-intensive doctoral students are solving the most complex, current, and global problems in education.

The solution is not to lower the bar for executives, but to raise the level of real-world engagement for our students. This is the philosophy behind our **Global Executive Fellowship (GEF)**.

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The Credibility Tightrope: Keeping the Degree Inviolate

Doctoral degrees, especially professional ones like the Ed.D., must be earned through demonstrated competency and a minimum standard of academic work. Any path that grants academic credit or the degree itself without completing the full curriculum would violate our accreditation and instantly **devalue the degree** for every student who earned it.

The integrity of the Ed.D. depends on our adherence to these mandates:

• **Academic Gatekeeping:** Every student, regardless of professional seniority, must successfully complete the required coursework, including our **Mixed Methods Research Core**.
• **Global Standardization:** Our admissions process uses third-party evaluation mandates, such as **NACES-approved agencies** for international transcripts, guaranteeing that our cohort starts with a globally verified academic foundation.

The GEF model allows us to partner with influential leaders without compromising these fundamental academic requirements.

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The Global Executive Fellowship (GEF) Model

The GEF is not a degree-granting program. It is a highly selective, non-remunerated appointment for distinguished leaders who are committed to advancing the practice of education through research.

Role: Practitioner-in-Residence

Fellows are designated as **Distinguished Practitioners-in-Residence** or **Global Policy Fellows**. Their value is not measured in credit hours, but in the depth of their expertise and the quality of the organizational problems they share with our students.

Key Contributions of GEF Fellows:

• **Sourcing DiP Problems:** Fellows provide our doctoral candidates with access to authentic, high-priority organizational challenges that serve as the subject of their **Dissertation-in-Practice (DiP)** projects. This ensures every DiP yields an immediate, strategic solution.
• **Executive Mentorship:** Fellows offer one-on-one mentorship, advising students on navigating the political and operational complexities of implementing their research findings within a large organization.
• **Content Infusion:** They lead mandatory, non-credit workshops and guest lectures, providing context that enriches our specialized concentrations and links theory directly to executive decision-making.

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The Strategic Partnership Advantage

By adopting the GEF, Geotech University achieves three crucial strategic objectives:

• **Elevated Program Distinction:** We can advertise a network of high-profile, non-academic leaders that is directly tied to the student experience.
• **Real-World Relevance:** The Ed.D. becomes an active, embedded consultancy that generates evidence-based policy and organizational improvements.
• **Global Advocacy and Networking:** Fellows become influential advocates for the program, opening doors for fundraising, student placement, and international collaboration with our IIEP and NLPN partners.

The Global Executive Fellowship is our commitment to the future of doctoral education: a model that demands **uncompromising academic rigor** while ensuring **unparalleled professional relevance**.

Learn More About the Global Ed.D.

Apply or Inquire Today →