Summer 2025 Course Topic: Foundation in Artificial Intelligence Applications
This course offers students from all academic backgrounds a practical introduction to the foundations and applications of Artificial Intelligence (AI). You'll develop a solid understanding of AI's core principles and methodologies through a balanced approach of theory and hands-on experience. Through the course, you will:
The course is designed to be accessible regardless of your technical background, while still providing substantive knowledge you can apply across disciplines. Lab assignments provide opportunities to experiment with AI tools and develop skills that complement your existing studies and interests.
No specialized prerequisites required—this course welcomes students from all majors.
- Semester: Summer 2025
- Instructor: Dave Fusco
- Who: Students who can use this course as a Social and Behavioral Sciences (GS) General Education course or an elective for any major, as well as undergraduates in any major who are interested in learning about AI, Generative AI, and Agentic AI agents used to provide solutions in all disciplines
- When: Remote Asynchronous
- Credits: 3.0
- General Education: Social and Behavioral Sciences (GS)
- LionPATH Class Number: 8624
This introductory course explores how modern artificial intelligence is transforming business and professional work. Students from all majors will learn about enterprise AI systems, generative AI tools (like large language models and image generators), and AI agents with a focus on practical applications in their chosen fields.
Through hands-on experience with current AI tools, students will develop an understanding of AI's capabilities, limitations, and potential impact on their future careers. No prior programming or technical background required.
Major Assessments (60%)
- Final Individual Project (25%)
- Students will develop a detailed analysis of AI's impact on a specific social institution or behavioral domain
- Includes examination of stakeholders, power dynamics, behavioral changes, and ethical implications
- Culminates in a form of: a presentation and/or written report
- Midterm Exam (15%)
- Focus on understanding key social theories, research methodologies, and analytical frameworks
- Mix of multiple choice and short answer questions to assess comprehension
- Include case studies for social impact analysis
- Social Impact Case Studies (20%)
- 5-6 structured analyses throughout semester (about 3-4% each)
- Application of social and behavioral frameworks to real-world AI implementations
- Focus on multi-dimensional analysis of AI's societal effects
Ongoing Assessments (40%)
- AI & Society Reflection Journal (20%)
- Students document their observations of AI's social influences
- Format: "Social context observed" / "Behavioral patterns identified" / "Theoretical connections"
- Must include specific examples and critical analysis of social dynamics
- Emphasis on connecting personal observations to broader societal patterns
- Weekly "AI in Society" Reports (20%)
- Students analyze real-world examples of AI's social impact
- Required to use AI tools to help research but must write analysis themselves
- Include reflection on how power dynamics, institutional factors, and behavioral responses interact
- Short (500 words) but substantive analysis of social implications
- In these reports, students will:
- Use AI to help find relevant social and behavioral examples
- Use AI to help brainstorm analytical frameworks
- Students write their own analysis but can use AI for editing/refinement
- Students must include a brief description of how social and behavioral science approaches informed their analysis