CIS1530 Introduction to AI
3 Credit Hours
Student Level:
This course is open to students on the college level in either Freshman or Sophomore year.
Catalog Description:
CIS1530 Introduction to AI (3 hrs.)
This course provides a broad, hands-on introduction to the world of Artificial Intelligence. Students will explore how AI “thinks” by examining the project cycle from data collection to deployment. Beyond basic text generation, this course dives into the creation of images, video, and audio, and introduces the emerging field of autonomous AI agents. Learners will gain practical experience with “no-code” machine learning and develop the critical skills needed to navigate the ethical challenges of a world reshaped by AI.
KRSN: n/a
Course Classification:
Lecture
Prerequisites:
None
Controlling Purpose:
This course provides students with a foundational and comprehensive overview of Artificial Intelligence (AI) and its practical applications across various personal and professional landscapes. Moving beyond the mechanics of prompt engineering, this course explores the functional pillars of AI literacy, including machine learning basics, the project lifecycle, and the emerging field of agentic (autonomous) systems. Through a hands-on, multidisciplinary approach, students will develop “AI Thinking” skills-learning to scope complex problems, deploy multimodal tools for image, video, and audio creation, and configure autonomous agents to execute multi-step workflows. The course emphasizes critical evaluation and ethical literacy, preparing students to identify hallucinations and bias while maintaining the “human-in-the-loop” necessity in AI-driven environments. By the end of the course, students will be equipped to leverage AI as a strategic partner for innovation, productivity, and decision-making in an increasingly automated world.
Learner Outcomes:
Upon completion of the course, the student will have the ability to analyze the historical development and foundational paradigms of artificial intelligence, including distinctions between narrow AI, general AI, and agentic systems. The student will apply “AI Thinking” to the AI project lifecycle by scoping real‑world problems, identifying appropriate data sources, and selecting suitable AI approaches for deployment. Through hands‑on exploration, the student will design and produce multimodal content such as images, short‑form video, and synthetic audio, and construct and evaluate autonomous or semi‑autonomous AI workflows. The student will demonstrate basic proficiency in no‑code machine learning to understand how models are trained and evaluated, and critically appraise the ethical, legal, and security implications of AI adoption, including deepfakes, algorithmic bias, and the necessity of maintaining human‑in‑the‑loop oversight.
Unit Outcomes for Criterion Based Evaluation:
The following outline defines the minimum core content not including the final examination period. Instructors may add other material as time allows.
UNIT 1: Foundations of Artificial Intelligence
Outcomes: Students will develop a foundational understanding of artificial intelligence concepts, historical development, and system capabilities. Upon completion of this unit, students will be able to:
- Describe the evolution of artificial intelligence and key milestones in its development.
- Differentiate between narrow AI, general AI, and emerging autonomous AI systems.
- Explain how AI systems learn from data using supervised and unsupervised approaches.
- Identify the stages of the AI project lifecycle from problem definition to deployment.
UNIT 2: Multimodal AI Systems (Text, Image, Audio, and Video)
Outcomes: Students will explore how artificial intelligence generates and interprets multiple forms of media. Upon completion of this unit, students will be able to:
- Explain how AI systems generate and analyze images, audio, and video.
- Create and refine multimodal content using AI‑assisted tools.
- Describe how AI extracts information from visual and audio data.
- Apply multimodal AI tools to produce integrated media outputs.
UNIT 3: Agentic and Autonomous AI Systems
Outcomes: Students will examine the principles of agentic AI and autonomous task execution. Upon completion of this unit, students will be able to:
- Differentiate between reactive AI systems and autonomous AI agents.
- Describe how AI agents plan, reason, and execute multi‑step tasks.
- Design basic AI‑assisted workflows that support autonomous problem solving.
- Evaluate the benefits and risks of autonomous AI systems in real‑world applications.
UNIT 4: Machine Learning Concepts Using No‑Code Tools
Outcomes: Students will gain practical exposure to machine learning concepts through hands‑on experimentation. Upon completion of this unit, students will be able to:
- Explain fundamental machine learning concepts and model training processes.
- Train and test a basic machine learning model using no‑code platforms.
- Analyze training data to identify bias, limitations, and performance issues.
- Compare different model approaches for specific problem types.
UNIT 5: Ethical, Social, and Workforce Implications of AI
Outcomes: Students will critically evaluate the ethical and societal impacts of artificial intelligence. Upon completion of this unit, students will be able to:
- Identify ethical challenges associated with AI, including bias, misinformation, and privacy risks.
- Explain the importance of human oversight in AI‑supported decision‑making.
- Assess the impact of AI technologies on workforce roles and skill requirements.
- Apply ethical guidelines for responsible and informed use of AI systems.
Projects Required:
Varies, refer to syllabus.
Textbook:
Contact Bookstore for current textbook.
Materials/Equipment Required:
None
Attendance Policy:
Students should adhere to the attendance policy outlined by the instructor in the course syllabus.
Grading Policy:
The grading policy will be outlined by the instructor in the course syllabus.
Maximum class size:
Based on classroom occupancy
Course Time Frame:
The U.S. Department of Education, Higher Learning Commission, and the Kansas Board of Regents define credit hour and have specific regulations that the college must follow when developing, teaching, and assessing the educational aspects of the college. A credit hour is an amount of work represented in intended learning outcomes and verified by evidence of student achievement that is an institutionally-established equivalency that reasonably approximates not less than one hour of classroom or direct faculty instruction and a minimum of two hours of out-of-class student work for approximately fifteen weeks for one semester hour of credit or an equivalent amount of work over a different amount of time. The number of semester hours of credit allowed for each distance education or blended hybrid courses shall be assigned by the college based on the amount of time needed to achieve the same course outcomes in a purely face-to-face format.
Refer to the following policies on the Cowley Policies and Procedures webpage:
402.00 - Academic Code of Conduct
263.00 - Student Appeal of Course Grades
403.00 - Student Code of Conduct
Accessibility Services Program:
Cowley College, in recognition of state and federal laws, accommodates all students with a documented disability. If a student has a disability that will impact their ability to be successful in this course, please contact the Student Accessibility Coordinator for the needed accommodations.
DISCLAIMER: THIS INFORMATION IS SUBJECT TO CHANGE. FOR THE OFFICIAL COURSE PROCEDURE CONTACT ACADEMIC AFFAIRS.
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