| Item | Information |
|---|---|
| Course Title | AI in Life: Social, Ethical & Cultural |
| Credit Hours | 3 credits |
| Term | Summer, 10-week format |
| Instructor | To be posted on Canvas |
| Department | Computer Science, Ball State University |
| Course Modality | Online asynchronous |
| Canvas Course Page | Canvas course page |
| Posted on Canvas | |
| Student Support Hours | Posted on Canvas |
This course explores the ethical, social, cultural and professional dimensions of artificial intelligence across diverse fields. The students will study algorithmic bias, data privacy, intellectual property, human–AI collaboration, and the societal effects of automation and generative AI. Multidisciplinary insights are provided through readings, case studies, discussions, and applied analyses. The course emphasizes critical understanding of how AI technologies influence, and are influenced by, human values, institutions, and decisions in business, healthcare, education, media, and the arts.
None.
Artificial intelligence is increasingly embedded in daily life, professional practice, education, healthcare, media, creative work, public institutions, and organizational decision-making. Understanding AI solely as a technical tool is no longer sufficient. Responsible AI practice requires attention to ethics, law, culture, governance, human rights, accessibility, social justice, professional responsibility, and environmental sustainability.
In this 10-week summer course, students should expect a steady weekly workload that includes readings, instructional content, a quiz, a discussion board, and a reflection essay. Several weeks also include applied case analyses or final project milestones.
By the end of this course, students will be able to:
Identify and analyze ethical and societal challenges arising from the design, deployment, and use of AI systems.
Evaluate AI technologies through frameworks of fairness, accountability, transparency, and human rights.
Explain professional responsibilities and codes of ethics relevant to AI and computing (e.g., ACM, IEEE).
Examine the impact of AI across disciplines such as healthcare, education, media, and creative industries.
Discuss implications of data privacy, intellectual property, and misinformation in the era of generative AI.
Engage with cross-disciplinary perspectives to understand human–AI interaction and decision-making.
Develop actionable guidelines for responsible and human-centered AI practice within organizational contexts.
This course is organized into 10 weekly modules. Each week includes readings, lecture or instructional content, a quiz, a discussion board, and a short reflection essay. Selected weeks also include applied case analyses. The course concludes with a final responsible-AI project in which students evaluate a real-world AI system and propose practical responsible-use guidelines.
Because this is a compressed summer course, each week may cover material that would normally be distributed across more than one week in a full semester. Students should check Canvas regularly for readings, announcements, due dates, assignment instructions, rubrics, and any updates to the schedule.
There is no required textbook. Course materials will consist of instructor-selected readings, official policy and standards documents, scholarly articles, professional guidelines, case databases, and accessible web resources.
Whenever possible, readings will be provided as accessible HTML pages rather than PDF files. If a required reading is available only as a PDF, the instructor will provide an accessible version, an accessible summary, or an equivalent alternative when needed.
Course materials will be prepared using an HTML-first workflow. Student-facing materials such as module pages, assignment instructions, reading lists, discussion prompts, and rubrics will be converted to accessible HTML. Materials will be designed to support screen-reader access, keyboard navigation, readable headings, descriptive links, sufficient color contrast, and mobile-friendly reading.
Students should contact the instructor as soon as possible if any course material, reading, video, database, or interactive resource is inaccessible. Alternative accessible formats will be provided when required.
| Week | Module Topic |
|---|---|
| 1 | Introduction to Responsible and Human-Centered AI; Ethical Frameworks for AI |
| 2 | Professional Codes, Responsible Computing Practice, and AI Governance |
| 3 | Algorithmic Bias, Fairness, Privacy, Surveillance, and Data Governance |
| 4 | Transparency, Explainability, Trustworthy AI, Generative AI, Misinformation, and Hallucination |
| 5 | AI, Intellectual Property, Authorship, Creativity, and Human–AI Collaboration at Work |
| 6 | AI in Education; AI in Healthcare, Psychology, and Human Services |
| 7 | AI in Media, Communication, Business, and the Arts |
| 8 | Sustainability, Global Equity, and the Social Cost of AI |
| 9 | Responsible AI design, transparency, and explainability in AI systems, Final Project Workshop, and Applied Synthesis |
| 10 | Final Responsible-AI Project Presentations and Course Synthesis |
Weekly Quizzes/Concept Checks: Short assessments of module concepts, readings, definitions, and applied scenarios.
Weekly Discussion Boards: Structured discussions that require students to apply readings, analyze AI-related dilemmas, and respond constructively to peers.
Weekly Reflection Essays: Short written reflections connected to the topic of each module. These essays emphasize synthesis, ethical reasoning, stakeholder analysis, and personal or professional application.
Applied Case Analyses: Formal analyses of real or realistic AI systems using responsible-AI frameworks. These assignments emphasize stakeholder analysis, risk identification, governance, accountability, and practical safeguards.
Final Responsible-AI Project: A larger end-of-course project in which students evaluate a real-world AI application from multiple ethical and societal perspectives and propose responsible-use guidelines.
The course uses a 5000-point grading system. One percent of the final course grade equals 50 points.
| Component | Weight | Points |
|---|---|---|
| Weekly Quizzes/Concept Checks | 15% | 750 |
| Weekly Discussion Boards | 15% | 750 |
| Weekly Reflection Essays | 30% | 1500 |
| Applied Case Analyses | 20% | 1000 |
| Final Responsible-AI Project | 20% | 1000 |
| Total | 100% | 5000 |
| Week | Assignment | Points | Group |
|---|---|---|---|
| 1 | Week 1 Quiz | 75 | Quizzes |
| 1 | Week 1 Discussion Board | 75 | Discussions |
| 1 | Week 1 Reflection Essay | 150 | Reflections |
| 2 | Week 2 Quiz | 75 | Quizzes |
| 2 | Week 2 Discussion Board | 75 | Discussions |
| 2 | Week 2 Reflection Essay | 150 | Reflections |
| 2 | Applied Case Analysis 1: Governance and Accountability | 250 | Case Analyses |
| 3 | Week 3 Quiz | 75 | Quizzes |
| 3 | Week 3 Discussion Board | 75 | Discussions |
| 3 | Week 3 Reflection Essay | 150 | Reflections |
| 3 | Applied Case Analysis 2: Bias, Privacy, and Data Governance | 250 | Case Analyses |
| 4 | Week 4 Quiz | 75 | Quizzes |
| 4 | Week 4 Discussion Board | 75 | Discussions |
| 4 | Week 4 Reflection Essay | 150 | Reflections |
| 4 | Applied Case Analysis 3: Generative AI and Misinformation | 250 | Case Analyses |
| 5 | Week 5 Quiz | 75 | Quizzes |
| 5 | Week 5 Discussion Board | 75 | Discussions |
| 5 | Week 5 Reflection Essay | 150 | Reflections |
| 5 | Final Project Topic Brief | 100 | Final Project |
| 6 | Week 6 Quiz | 75 | Quizzes |
| 6 | Week 6 Discussion Board | 75 | Discussions |
| 6 | Week 6 Reflection Essay | 150 | Reflections |
| 6 | Applied Case Analysis 4: Applied Domain Impact | 250 | Case Analyses |
| 7 | Week 7 Quiz | 75 | Quizzes |
| 7 | Week 7 Discussion Board | 75 | Discussions |
| 7 | Week 7 Reflection Essay | 150 | Reflections |
| 7 | Responsible-AI Framework and Source Plan | 150 | Final Project |
| 8 | Week 8 Quiz | 75 | Quizzes |
| 8 | Week 8 Discussion Board | 75 | Discussions |
| 8 | Week 8 Reflection Essay | 150 | Reflections |
| 9 | Week 9 Quiz | 75 | Quizzes |
| 9 | Week 9 Discussion Board | 75 | Discussions |
| 9 | Week 9 Reflection Essay | 150 | Reflections |
| 9 | Draft Responsible-AI Audit/Project Draft | 250 | Final Project |
| 10 | Week 10 Quiz | 75 | Quizzes |
| 10 | Week 10 Discussion Board | 75 | Discussions |
| 10 | Week 10 Reflection Essay | 150 | Reflections |
| 10 | Final Responsible-AI Project Report | 350 | Final Project |
| 10 | Final Project Presentation or Executive Summary | 150 | Final Project |
The final project requires students to examine a real-world AI application from multiple ethical, societal, professional, and organizational perspectives. Students will analyze the system’s purpose, stakeholders, benefits, risks, governance needs, and responsible-use safeguards.
| Week | Final Project Component | Points | Group |
|---|---|---|---|
| 5 | Final Project Topic Brief | 100 | Final Project |
| 7 | Responsible-AI Framework and Source Plan | 150 | Final Project |
| 9 | Draft Responsible-AI Audit/Project Draft | 250 | Final Project |
| 10 | Final Responsible-AI Project Report | 350 | Final Project |
| 10 | Final Project Presentation or Executive Summary | 150 | Final Project |
| Total | 1000 |
Students may organize the final project using the following structure:
AI system overview: What does the system do, and where is it used?
Purpose and context: What problem is it supposed to solve?
Stakeholders: Who benefits, who is affected, and who may be harmed?
Data and model concerns: What data, model, documentation, or evaluation issues matter?
Ethical and social risks: What fairness, privacy, safety, misinformation, labor, access, sustainability, or accountability risks arise?
Governance analysis: How should the system be governed, monitored, audited, and improved?
Comparable incident: What real-world AI incident reveals a similar risk?
Responsible-use guidelines: What practical rules should guide responsible adoption?
Human-centered recommendations: How should human agency, oversight, accessibility, and recourse be preserved?
Final judgment: Should the system be adopted, restricted, redesigned, or rejected?
Weekly quizzes assess students’ understanding of key module concepts, readings, definitions, frameworks, and applied examples. Quizzes may include multiple-choice questions, short-answer questions, matching questions, and scenario-based questions.
Discussion boards evaluate students’ ability to engage thoughtfully with ethical questions, apply course readings, analyze AI systems from multiple stakeholder perspectives, and respond constructively to classmates. Students are expected to write an original post and complete peer replies according to each module’s instructions.
Reflection essays are short written responses connected to the module topic. These essays ask students to synthesize readings, apply ethical and human-centered AI concepts, and reflect on how AI affects individuals, organizations, professions, communities, and society.
Reflection essays should demonstrate clear reasoning, specific use of course concepts, and thoughtful engagement with the module’s central questions. Unless otherwise specified, reflection essays should be written in complete paragraphs and submitted through Canvas.
Applied case analyses require students to evaluate real or realistic AI systems using responsible-AI frameworks. These assignments emphasize stakeholder analysis, risk identification, fairness, privacy, transparency, accountability, governance, human oversight, and practical safeguards.
Students may draw on case databases, news reports, policy documents, organizational materials, or instructor-provided case descriptions. Each case analysis should identify the system, affected stakeholders, potential harms, relevant course concepts, and recommendations for responsible practice.
The final responsible-AI project is the culminating assignment for the course. Students will select a real-world AI application and analyze it from multiple ethical and societal perspectives. The final submission should include a clear description of the system, stakeholder analysis, risk analysis, governance recommendations, and actionable responsible-use guidelines.
The project is completed through staged milestones so students receive feedback before the final submission.
| Letter Grade | Range |
|---|---|
| A | 100%–94% |
| A– | Below 94%–90% |
| B+ | Below 90%–87% |
| B | Below 87%–84% |
| B– | Below 84%–80% |
| C+ | Below 80%–77% |
| C | Below 77%–74% |
| C– | Below 74%–70% |
| D+ | Below 70%–67% |
| D | Below 67%–64% |
| D– | Below 64%–61% |
| F | Below 61%–0% |
Students are expected to engage respectfully and professionally with classmates, the instructor, course materials, and AI-related ethical issues. Because the course addresses contested social, ethical, legal, and professional questions, students should practice civil discourse, evidence-based reasoning, and charitable interpretation of others’ perspectives.
Professional communication includes timely participation, clear writing, constructive peer feedback, academic integrity, and responsible use of AI tools when permitted.
This course addresses generative AI as a core topic. Some use of generative AI tools may be permitted for designated assignments. Unless explicitly authorized in an assignment description:
Work must reflect the student’s own reasoning, analysis, writing, and judgment.
Any AI-assisted content, including ideas, text, code, summaries, outlines, figures, or editing, must be clearly attributed in the submission.
Students may be asked to include a brief “AI Tools and Attribution” statement explaining what tool was used, how it was used, and how the student verified or revised the output.
Using AI tools to generate uncredited or substantially unreviewed work for graded assignments constitutes academic misconduct.
Students are responsible for verifying the accuracy, reliability, originality, and appropriateness of any AI-assisted work they submit.
Assignments should be submitted by the deadlines posted on Canvas. Because this summer course is compressed into 10 weeks, timely submission is especially important for maintaining progress and participating meaningfully in discussions.
Students who anticipate difficulty meeting a deadline should contact the instructor as early as possible. Extensions may be granted at the instructor’s discretion, consistent with university policy and documented circumstances. The most current late-work policy and any assignment-specific rules will be posted on Canvas.
Since this is an asynchronous course, engagement is demonstrated through timely completion of readings, quizzes, discussion boards, reflection essays, case analyses, and project milestones. Students are responsible for reviewing all posted materials and announcements.
Ball State University is committed to ensuring that all members of the community are welcome, through valuing the various experiences and worldviews represented at Ball State and among those the university serves. The course promotes a culture of respect, inclusion, and civil discourse.
If you need course adaptations or accommodations because of a disability, please contact the instructor of record as soon as possible. Ball State’s Disability Services Office coordinates services for students with disabilities. Documentation of a disability needs to be on file with that office before accommodations can be provided. Disability Services can be contacted at 765-285-5293 or dsd@bsu.edu.
Honesty, trust, and personal responsibility are fundamental attributes of the university community. Academic dishonesty and other forms of academic misconduct threaten the foundation of an institution dedicated to the pursuit of knowledge and will not be tolerated.
Students are responsible for understanding Ball State University’s academic integrity expectations, citing sources properly, avoiding plagiarism, and using generative AI tools only as permitted by the instructor and assignment instructions.
If you believe you received a final course grade that does not reflect your performance due to fairness or procedural concerns, you have the right to file an appeal in accordance with Ball State University’s grade appeal policy and timeline. Students should review the official University Grade Appeal Policy and Process for details.
This syllabus is subject to change in the event of extenuating circumstances, changes in course needs, updated university policy, or improvements to the learning experience. The most current version of the course schedule, assignments, readings, due dates, and grading details will be available on Canvas.