ITPro 1: 1/2026

Overview

• Working collaboratively

• Collaborative Software: MS vs Google

• Studying online:

  • Creating documents/assignments
  • Online Grading
  • Checking Grades

• Online Search Tools:

  • Search engines: Google/Bing
  • GenAI: LLM / AI
  • Wikipedia

The Basic Structure of Papers

Independence (Private Victory)

• Habit 1: Be Proactive

  • Take responsibility for your life and choices.
  • Focus time and energy on things they can control or influence.
  • Minimize waste time and energy on complaining about external factors.

• Habit 2: Begin with the End in Mind

  • Define a clear vision of your personal values, long-term goals, and destination before taking action.
  • Use this vision as a framework to design your daily decisions and actions.

• Habit 3: Put First Things First

  • Focus on prioritizing tasks based on urgency and importance.
  • Manage your time and life according to your highest priorities.
  • Give priority to activities that are important but not necessarily urgent.

Interdependence (Public Victory)

• Habit 4: Think Win-Win

  • Seeks mutually beneficial solutions in relationships and agreements.
  • Success for one person does not have to come at the expense or exclusion of another’s success.

• Habit 5: Seek First to Understand, Then to Be Understood

  • Practice empathetic listening
  • Genuinely comprehend another person’s perspective
  • Listen before attempting to share your own point of view.

• Habit 6: Synergize

  • Combine the strengths of people
  • Use teamwork and open-mindedness to achieve goals that no single individual could accomplish alone.

Dangers of genAI

• AI Hallucinations: LLMs were not designed to be fact retrieval engines. They work by predicting the probability of the next word in a sequence. LLMs may produce outputs that are factually incorrect, nonsensical, or entirely fabricated.

• Model Bias: LLMs are built using large bodies of text, often scraped from the Internet. This data contains bias that LLMs can learn and propagate. LLMs can give responses that are biased or disparaging or provide responses of worse quality for certain subgroups.

• AI Privacy Concerns: LLMs can leak or inadvertently disclose personally identifiable information or other sensitive or confidential details.

• Toxic, Harmful, or Inappropriate Content: LLMs are capable of creating toxic, harmful, violent, obscene, harassing, and otherwise inappropriate content.

• AI Copyright Infringement And IP Risks: LLMs are often trained with copyrighted data and thus can generate content that is identical to or similar to copyrighted material. They can also leverage materials online such as a person’s tone or voice to create highly similar content to what that person might have generated.

• Security Vulnerabilities: LLMs have increased the surface area for security risk. Feedback cycles create an identity and profile.

• Dependance and reduction of discretion: The speed and general accuracy cloud judgement and reduce long term retention.

Stages of this Project

Phase 1: Initial Research Task: The team selects a hypothetical industry (e.g., healthcare, fintech, or education) and establishes the core ethical considerations for adopting genAI policy into the workplace that minimizes the risks and maximizes skill development in the workers. The goal is to develop incentives that would develop workers that perform well regardless of access to genAI. The intent is to encourage workers to use genAI to develop genuine skills in workers without developing a permenant dependance on genAI.

Team members must prompt 2 different genAI tools to generate a draft of a broad list and outline of incentives that would address the risks and security vulnerabilities of using genAI, and provide operational guidelines that makes the workers to take full responsibility for submitted work and discourage over-dependance on the technology.

• Phase 2: Critical analysis Teamwork are to compare the 2 sets of ai-generated outline. By debate, the team is to identify items are irrelevant or overstated, as well as filter out AI hallucinations, unrealistic suggestions, or generic advice. The goal is to summarize and record the comparison and establish the finalized outline of the proposal.

• Phase 3: Deep-Dive Drafting The team will divide up the outline into specific functional modules which are assigned to members of the team. Each team member takes ownership of a section and generates a full description of the recommendations by using targeted prompts to draft preliminary content for each section, generate case studies, and format policy.

• Phase 4: Communication & Collaboration: The team holds peer-review sessions to share drafts, ensures consistent tone across modules, resolve overlapping policies, and verify that all claims are backed by source facts. Members input their drafted document into a genAI model, prompting it to act as an auditor or adversary seeking to exploit policy loopholes. The team evaluates the AI-generated audit reports, decides which criticisms are valid, and rewrites weak sections to fortify the document.

• Phase 5: Document Finalization: The team will synthesize the individual modules into a polished, professional document using the format discussed earlier and draft a high-level executive summary for stakeholders. Teams will use genAI to assist in converting complex legalistic text into clear, concise executive summaries and key takeaway points. The group performs a final line-by-line review to align language, verify technical accuracy, and finalize formatting. A final table must be added that displays the amount of time and effort spent in each phase of this project.