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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.