AI & Its Applications: A Student Study Guide (2026 Edition)
Created by Herve ONANGA KINGBO (2026)
Table of Contents
1. Introduction to Artificial Intelligence (AI)
2. The Turing Test Era
3. Prompt Engineering Effects
4. Algorithmic Bias
5. AI Processing Models
6. AI for Student Performance
7. Data Salience
8. Knowledge Accessibility
9. Ethical AI Use in Academia and Work
10. AI Heuristics
11. AI Lexicon: Forms and Types
12. Processing Biases
13. Counterfactuals in AI
14. Themes in AI Processing
15. Case Study – The Automated Architect Dilemma
16. The Ethical Zone
17. Certification and Professional Readiness
18. Study Guide Summary
19. Quick Reference Checklist
20. References & Citation Guidance
,Introduction to Artificial Intelligence (AI)
Study Guide, Definitions and Notes
Intelligence: The ability to acquire and apply knowledge and skills
Artificial: Made or produced by human beings rather than occurring naturally
Artificial Intelligence (AI): The simulation of human intelligence processes by
computer systems, especially the ability to perceive, reason, learn, and solve
problems Influenced by data quality, algorithmic bias, and human programming.
Active processing of massive datasets to identify patterns
, The Turing Test Era
o Focus on whether a machine can "think" like a human
o Evaluator decides whether a response is from A (Human) or B (Machine)
o Modern AI often operates through "Black Box" processes (not always
transparent)
o Also influenced by the specific parameters set by developers
Prompt Engineering Effects
o The quality of an AI output is based on how the instruction (prompt) is
presented. Example: Steak is 80% lean (Analogous to a clear "Specific
Prompt") Steak is 20% fat (Analogous to a "Vague or Biased Prompt") More
likely to want steak when it says 80% lean (Input quality determines utility)
Algorithmic Bias
o AI tends to reflect the biases present in its training data and may neglect
marginalized perspectives
o It may interpret ambiguous info as confirmatory info (Confirmation Bias)
Created by Herve ONANGA KINGBO (2026)
Table of Contents
1. Introduction to Artificial Intelligence (AI)
2. The Turing Test Era
3. Prompt Engineering Effects
4. Algorithmic Bias
5. AI Processing Models
6. AI for Student Performance
7. Data Salience
8. Knowledge Accessibility
9. Ethical AI Use in Academia and Work
10. AI Heuristics
11. AI Lexicon: Forms and Types
12. Processing Biases
13. Counterfactuals in AI
14. Themes in AI Processing
15. Case Study – The Automated Architect Dilemma
16. The Ethical Zone
17. Certification and Professional Readiness
18. Study Guide Summary
19. Quick Reference Checklist
20. References & Citation Guidance
,Introduction to Artificial Intelligence (AI)
Study Guide, Definitions and Notes
Intelligence: The ability to acquire and apply knowledge and skills
Artificial: Made or produced by human beings rather than occurring naturally
Artificial Intelligence (AI): The simulation of human intelligence processes by
computer systems, especially the ability to perceive, reason, learn, and solve
problems Influenced by data quality, algorithmic bias, and human programming.
Active processing of massive datasets to identify patterns
, The Turing Test Era
o Focus on whether a machine can "think" like a human
o Evaluator decides whether a response is from A (Human) or B (Machine)
o Modern AI often operates through "Black Box" processes (not always
transparent)
o Also influenced by the specific parameters set by developers
Prompt Engineering Effects
o The quality of an AI output is based on how the instruction (prompt) is
presented. Example: Steak is 80% lean (Analogous to a clear "Specific
Prompt") Steak is 20% fat (Analogous to a "Vague or Biased Prompt") More
likely to want steak when it says 80% lean (Input quality determines utility)
Algorithmic Bias
o AI tends to reflect the biases present in its training data and may neglect
marginalized perspectives
o It may interpret ambiguous info as confirmatory info (Confirmation Bias)
