Example syllabus only – exact content subject to change. Please see your instructor’s syllabus for the current term for your specific course’s guidelines.
Course Information
Course Code: RESECON 490AI
Number of Credits: 3
Days: T, Th
Time: 1:00-2:15
Location: LIBR1667
Mode: In person
Instructor Contact
Instructor: Dr. Christian Rojas
Email: @email
Office Hours: Email me to set up an in-person or Zoom meeting
Office: Stockbridge Hall, 205
Course Description
This course introduces students to two complementary dimensions of artificial intelligence. Part 1: AI Literacy emphasizes the historical development of AI, core concepts, technolo- gies, and terminology to understand capabilities and limitations. Part 2: AI Literacies focuses on practical application, providing hands-on opportunities to integrate AI into tasks related to economics, research, and professional development.
Throughout, we stress ethical and effective use: AI should be treated as a copilot, not a substitute; students are expected to iterate on outputs, verify sources, take ownership of the final product, and attribute AI assistance. Reflection on both the usefulness and limitations of AI is embedded in all activities.
Student Learning Objectives
Specific Objectives
- AI literacy: Understand the historical development of AI and become knowledgeable about the current technologies, terminology, algorithms, and capabilities of AI.
- AI literacies: Learn, through hands-on projects and exercises, how AI tools can augment productivity in settings related to daily life, school work, job seeking, and Economics.
Broader Objectives
- Base learning: Develop effective communication (oral, written, IT-based); synthesize, analyze, and evaluate information; and generate effective solutions.
- Life-long learning: Gain the ability to integrate new knowledge critically and assess source quality to make well-informed decisions.
- Experiential: Build collaborative skills through group activities and independent research.
Course Requirements and Grading Breakdown
- Daily hands-on experience and reflections (50%): At the end of most class sessions, students will submit short deliverables (e.g., outputs from exercises, brief reflections on AI’s usefulness/limitations).
- Bi-weekly quizzes (25%): Approximately 6 quizzes, focused on checking compre- hension of technical concepts. These are closed-book, paper-and-pencil assessments de- signed as checkpoints, not high-stakes evaluations. You will be able to re-submit/revise the quizzes so as to improve the grade.
- Final project (25%): A semester-long project developed in stages (topic, dataset, re- search question, draft, presentation). Students must document their process, including how AI was used and how outputs were critically assessed.
Grading scale (cutoffs)
A = 93, A- = 90, B+ = 87, B = 83, B- = 80, C+ = 77, C = 73, C- = 70, D+ = 67, D = 60, F < 60
Late or make-up work
Late or make-up work will not be permitted, unless the student provides valid documentation (e.g., doctor’s note).
Attendance Policy
Because in-class exercises and quizzes constitute most of the grade, consistent attendance is essential. Borrowing notes will not substitute for the experiential and reflective components.
Course Schedule
Part I: AI Literacy (Weeks 1–6)
Week 1: Introduction and history of AI. What is intelligence? What is artificial intelli- gence?
In-class: “Next word prediction” exercise to simulate how LLMs work.
Week 2: Symbolic vs. subsymbolic AI; AI vs. Generative AI.
In-class: Classify examples (calculator, Netflix, GPT) into categories.
Week 3: Machine learning basics: prediction as y = f (x), algorithms, maximization.
In-class: Regression as a supervised learning example.
Week 4: Neural networks and deep learning. What is a neuron, backpropagation, mile- stones (LLMs).
In-class: Identify AI behind familiar apps (spam filter, YouTube recs).
Week 5: Supervised, unsupervised, and reinforcement learning. Structured vs. unstruc- tured data.
In-class: Group exercise mapping tasks to ML types.
Week 6: Classifiers, random forests, image recognition.
In-class: Explore image recognition demo with AI tools. Final project check-in #1: topic area.
Part II: AI Literacies (Weeks 7–12)
Week 7: AI for coding, data visualization, and analysis.
In-class: Python interactive dashboard exercise using Colab and Plotly. Final project check-in #2: dataset identification.
Week 8: AI for text, audio, and video analysis. Prompting for summarization, transcrip- tion, synthesis.
In-class: NotebookLM or ChatGPT for literature review support. Final project check-in #3: research question.
Week 9: AI for research support: effective prompting, personas, temperature, RAGs.
In-class: Excel pricing strategy exercise. Final project check-in #4.
Week 10: Project development workshop.
In-class: 3-minute presentations (topic, dataset, research question).
Week 11: AI in professional tasks: GitHub portfolios and web presence.
In-class: Build GitHub Pages site for portfolio.
Week 12: AI in workplace productivity: job applications, cover letters, planning.
In-class: AI-assisted r´esum´e/job matching exercise (Colab). Final project work.
Week 13: Final project presentations.
Class Materials and Mode of Instruction
- No textbook required. Students must have access to ChatGPT Plus ($20/month, or shared with peers).
- Access to other AI tools (Gemini, Copilot, Anthropic, etc.) via free accounts.
- Classes held in a computer lab (devices available), though personal laptops are encour- aged.
- Students must maintain a Gmail account for tool access.
Required Syllabus Statements
See: https://www.umass.edu/senate/book/required-syllabus-statements
Accommodation: Notify me within two weeks to arrange accommodations. https://www. umass.edu/disability/
Academic Honesty: Integrity is expected. Quizzes are closed-book, while AI use is per- mitted (and expected) in projects, with proper attribution. See http://www.umass.edu/ dean_students/codeofconduct/acadhonesty/
Title IX: UMass is committed to a safe learning environment. Resources: https://www. umass.edu/titleix/resources.
Generative AI: We will specify where AI is permitted. Always disclose AI use.