AI Microcredentials Foundational Non-Technical Bundle
Understand AI from a leadership and strategy lens. Courses cover AI policy, governance, ethics and storytelling. Learn to communicate, evaluate and guide AI adoption with confidence.
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Overview
Unlock the power of data with Purdue’s Artificial Intelligence Microcredentials bundle
Purdue University’s Artificial Intelligence Microcredentials offer quick and convenient online courses that cover the fundamentals of artificial intelligence and its applications. Every course functions as its own mini-credential. Students earn a certificate of completion and digital badge from Credly for every course they complete. Students can pick and choose what course to take and stack credentials in topics that interest them. Courses are also taught by the well-renowned faculty of Purdue.
AI is revolutionizing hundreds of industries, and AI skills are some of the most in-demand job skills in today’s tech-driven market. Learn essential AI skills including:
- Communicate AI concepts clearly to technical and nontechnical audiences.
- Analyze policy, ethics and governance frameworks shaping AI adoption.
- Lead organizational or classroom discussions on responsible and strategic AI use.
The cost of attending Purdue varies depending on where you choose to live, enrollment in a specific program or college, food and travel expenses, and other variables. The Office of the Bursar website shows estimated costs for the current aid year for students by semester and academic year. These amounts are used in determining a student’s estimated eligibility for financial aid. You can also use our tuition calculator to estimate tuition costs.
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AI is changing every industry, and leaders must understand how to guide its impact. This bundle is designed for professionals, educators and policymakers who want to explore AI’s opportunities without needing to code. Courses such as AI Policy and Governance, Demystifying AI and AI Data Storytelling Primer teach you how to communicate AI concepts, understand regulatory frameworks and make informed decisions about responsible innovation.
Foundational – Non-Technical Courses
Course Description: This course provides students with the real-world knowledge they need to navigate the risks of AI and how it’s changing the technology landscape. Students will break AI down into engaging, accessible concepts and explore the ethics of AI through real-world examples.
Prerequisites: None
Learning Outcome:
– Explain the history of AI research and why deep learning became the dominant approach
– Explain, in a non-technical way, how deep learning works
– Outline various risks of AI, including both speculative dangers and more tangible and immediate risks to the economy, the labor market, and civil society
| Module | Topic & Readings |
| Module 1 | History of Artificial Intelligence |
| Module 2 | A Crash Course on Deep Learning |
| Module 3 | The Singularity and Other Speculative Fears |
| Module 4 | The Immediate Risks of Artificial Intelligence |
Faculty: David Peterson
Course Description: This course explores the ethical and regulatory framework that underpin AI. Students will analyze real-world policy and governance strategies that seek to manage AI’s impacts and engage in debates that will shape the future of the field.
Prerequisites: None
Learning Outcome:
– Identify core concepts in the emerging AI policy domain including key actors, institutions, and governance strategies that have been proposed by or adopted in governments, firms, and civil society organizations
– Analyze and evaluate the social and ethical dimensions of AI, focusing on issue spotting and understanding the policy implications of these issues
– Examine current regulatory trends and policy frameworks for AI governance, including prominent debates and challenges
– Synthesize emerging best practices, issues, and debates in AI policy and governance, while developing strategies for continual monitoring and staying informed
| Module | Topic & Readings |
| Module 1 | Introduction to AI |
| Module 2 | Key Concepts in International Governance |
| Module 3 | Corporate Governance and Self-Regulation |
| Module 4 | Ethics and Impact Assessment |
| Module 5 | Getting Engaged and Building Expertise |
Faculty: Daniel Schiff
Course Description: This course covers the growing global demands for AI regulations and puts them in context so students can understand what risks these regulations seek to address and how companies and governments can anticipate and comply with them.
Prerequisites: None
Learning Outcome:
– Analyze the demand for global regulation of AI
– Identify key issues for AI regulation to address in a global context
– Explain leading hard and soft models for global AI regulation
| Module | Topic & Readings |
| Module 1 | Demand for Global AI Regulation |
| Module 2 | Core Issues for Global Regulation for AI |
| Module 3 | Hard Regulation: The European Model |
| Module 4 | Soft Regulation: Industry Standards |
Faculty: Dr. Swati Srivastava
Course Description: Explore the emerging field of prompt engineering with AI tools like large language models (LLMs) (ChatGPT, Claude, Gemini, etc.) and image models (Midjourney, DALL-E, etc.) and learn to harness the power of Generative AI to improve daily life, work, and learning experiences. This asynchronous, online course offers a practical introduction to AI and LLMs through interactive content, short videos, practical exercises, and self-assessments. This course will help non-technical learners to understand AI, use AI tools effectively, and craft prompts that enhance AI’s utility in various applications. By engaging with this course, learners will gain skills to “talk” to AI tools, effectively becoming programmers of their digital interactions.
Prerequisites: None
Learning Outcomes
-Apply in-demand prompt engineering skills for Generative AI tools.
-Create and design prompts using the cutting-edge AI tools like LLMs and Stable Diffusion models.
| Module | Topic & Readings |
| Module 1 | Introduction to AI and Language Models |
| Module 2 | Interacting with Language Models |
| Module 3 | Crafting Effective Prompts |
| Module 4 | Practical Applications of Prompt Engineering |
Faculty: Elsayed Issa
Course Description: In this course, students will learn how to create engaging data stories by contradicting common perception. They will use data analysis and AI prompts using Chat GPT to produce effective data stories and justify why AI makes these data stories more effective.
Prerequisites: None
Learning Outcome:
– Identify how data stories should surprise, provide a new, more convincing explanation for time-worn ideas
– Identify ways to tell memorable, teachable arguments in the form of stories
– Examine how AI tools may be used to satisfy these conditions of storytelling
| Module | Topic & Readings |
| Module 1 | Introduction |
| Module 2 | What is Research? |
| Module 3 | Where Do Stories Come From? |
| Module 4 | Pictures Are Not Stories |
| Module 5 | AI & Storytelling |
| Module 6 | Conclusion |
Faculty: Sorin Matei
Course Description: This transdisciplinary course demystifies artificial intelligence by exploring its technological evolution, system design principles, and practical applications—making complex AI concepts accessible to learners from any background. You’ll develop essential skills for understanding, conceptualizing, and communicating about AI systems, gaining critical knowledge that applies across industries and disciplines.
Prerequisites: None
Learning Outcome:
– Describe the foundations of artificial reasoning, knowledge, and trace the historical development of AI
– Explain the basic concepts, terminologies, and applications of different AI algorithms, developing a problem-solving mindset applicable to various domains
– Examine the diverse range of AI applications across varied industries
– Explain the role human factors and social context play in the development of artificial intelligence
| Module | Topic & Readings |
| Module 1 | Historical Development and Current Trends in AI |
| Module 2 | Role of Translators and Builders in AI – Knowledge, Reasoning & the Connection to Artificial Intelligence |
| Module 3 | AI Alignment and Domain Applications |
Faculty: Bill Watson, Xingyu Li, and Cherie Maestes
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