Exploring Ethical AI Scenarios
After reviewing ethical scenarios on the following two slides, you will return to Canvas for a discussion post.
Start
Lesson 1-C
Examples of Ethical AI Dilemmas in Higher Education:
AI-Assisted Writing & Academic Integrity
Accessibility vs. Cost of Technology
Algorithmic Bias in Admissions or Grading
Lesson 1-C
More Examples of Ethical AI Dilemmas in Higher Education:
Faculty Use of AI in Teaching & Assessment
Inclusion in AI Curriculum Design
Intellectual Property & AI-Generated Work
Lesson 1-C
Return to Canvas Course Lesson 1-C to Complete a Discussion Post on Applying Your Understanding to a Real-Life Example
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Lesson 1-C
From an ethical perspective, institutions must consider the benefits of adopting advanced AI tools against the risk of creating exclusion. If access to AI becomes a privilege rather than a shared resource, it undermines the principles of fairness, inclusivity, and equal opportunity that higher education aims to promote.
From an ethical perspective, using AI-generated work without proper acknowledgment raises concerns about honesty, transparency, and attribution. Presenting AI-created material as one’s own original work misstates authorship and damages the integrity of scholarly and creative efforts.
AI-assisted writing raises important questions about academic integrity, which is grounded in honesty, fairness, and originality. When students use AI tools, they must ensure that the ideas, words, and structures generated by these systems accurately represent their own understanding and authorship.
An algorithm might favor applicants from better-funded schools or penalize certain language patterns linked to specific cultural or socioeconomic backgrounds. In grading, AI-assisted assessment tools can also exhibit bias.
Ethically, faculty members are responsible for ensuring that AI is used to improve learning rather than replace meaningful human judgment. Automated grading systems, for example, can boost efficiency but might also introduce bias or misinterpret student work, especially for nontraditional writing styles or diverse cultural expressions.
From an ethical perspective, educators must create AI curricula that foster equity, representation, and accessibility. This involves using diverse datasets, featuring scholars and practitioners from underrepresented groups, and involving students in discussions about fairness, accountability, and social impact.
Exploring Ethical AI Scenarios
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Created on October 17, 2025
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Transcript
Exploring Ethical AI Scenarios
After reviewing ethical scenarios on the following two slides, you will return to Canvas for a discussion post.
Start
Lesson 1-C
Examples of Ethical AI Dilemmas in Higher Education:
AI-Assisted Writing & Academic Integrity
Accessibility vs. Cost of Technology
Algorithmic Bias in Admissions or Grading
Lesson 1-C
More Examples of Ethical AI Dilemmas in Higher Education:
Faculty Use of AI in Teaching & Assessment
Inclusion in AI Curriculum Design
Intellectual Property & AI-Generated Work
Lesson 1-C
Return to Canvas Course Lesson 1-C to Complete a Discussion Post on Applying Your Understanding to a Real-Life Example
Review the discussion post assignment in Canvas
Click Canvas Icon
Lesson 1-C
From an ethical perspective, institutions must consider the benefits of adopting advanced AI tools against the risk of creating exclusion. If access to AI becomes a privilege rather than a shared resource, it undermines the principles of fairness, inclusivity, and equal opportunity that higher education aims to promote.
From an ethical perspective, using AI-generated work without proper acknowledgment raises concerns about honesty, transparency, and attribution. Presenting AI-created material as one’s own original work misstates authorship and damages the integrity of scholarly and creative efforts.
AI-assisted writing raises important questions about academic integrity, which is grounded in honesty, fairness, and originality. When students use AI tools, they must ensure that the ideas, words, and structures generated by these systems accurately represent their own understanding and authorship.
An algorithm might favor applicants from better-funded schools or penalize certain language patterns linked to specific cultural or socioeconomic backgrounds. In grading, AI-assisted assessment tools can also exhibit bias.
Ethically, faculty members are responsible for ensuring that AI is used to improve learning rather than replace meaningful human judgment. Automated grading systems, for example, can boost efficiency but might also introduce bias or misinterpret student work, especially for nontraditional writing styles or diverse cultural expressions.
From an ethical perspective, educators must create AI curricula that foster equity, representation, and accessibility. This involves using diverse datasets, featuring scholars and practitioners from underrepresented groups, and involving students in discussions about fairness, accountability, and social impact.