Section outline

  • Course Overview

    This course provides a solid introduction to using AI in a way that’s approachable and non-technical. You’ll learn about Gen AI and why it has become the most common way in which we interact with AI. You’ll gain strategies for fact-checking output and explore appropriate use cases. You’ll also be challenged to consider ethical, environmental, and legislative concerns in a world in which AI is constantly evolving.  

    Learning Outcomes

    This course will help you to:

    1. Recognize how AI is already integrated into daily life across various tools, platforms, and industries 
    2. Identify appropriate use cases for Gen AI across academic, creative, and professional contexts 
    3. Evaluate the trustworthiness of AI-generated outputs and check for bias, errors, and hallucinations 
    4. Reflect on the social, environmental, and labor impacts of the use and development of AI  
    5. Recognize the role of individuals and communities in shaping the future of AI 

    • Course Contributors

      • Course Advisor: Sarah Newman

        Sarah Newman is Director of Art & Education at Harvard University’s metaLAB at the Berkman Klein Center for Internet & Society. Her work explores the social and ethical dimensions of AI through research and teaching. Newman leads the AI Pedagogy Project, which provides guidance for educators to responsibly engage with AI, and she offers AI demystification workshops to broad audiences. Newman is Co-founder of the Data Nutrition Project, which aims to mitigate bias in AI through tools and educational practices. She holds a BA in Philosophy from Washington University in St. Louis and an MFA in Imaging Arts from the Rochester Institute of Technology. Previous honors have included: Harvard Assembly Fellow, Harvard Berkman Klein Fellow, Rockefeller Foundation Bellagio AI Resident, Artist-in-Residence at Northeastern School of Law, Notre Dame Tech Ethics grant recipient, National Endowment of the Arts grant recipient, and winner of the Ars Electronica Award for Digital Humanity.

        View Bio for Course Advisor: Sarah Newman

      • Course Lead Instructor: Sebastian Rodriguez

        Sebastian Rodriguez is a Researcher and Engineer at metaLAB within the Berkman Klein Center for Internet & Society at Harvard University. His research explores critical surveillance and security studies, critical data studies, digital humanities, and pedagogical development. Sebastian is the lead developer of the AI Pedagogy Project, which provides open educational resources that help educators critically and creatively engage with AI in the classroom. He is also a research collaborator with the Data Nutrition Project, and an affiliate of the University of Toronto’s Failure: Learning in Progress Project, the Society for Teaching & Learning in Higher Education’s REFLECT Project, and the Manchester-Melbourne-Toronto Beyond Disinformation Research Cluster. Sebastian holds a Bachelor of Information from the University of Toronto, and is currently completing an MSc in Social Science of the Internet from the Oxford Internet Institute at the University of Oxford.

        View Bio for Course Lead Instructor: Sebastian Rodriguez

      • Course Contributor (Video): Kasia Chmielinski

        Kasia Chmielinski is a Senior Advisor on Data & AI at the United Nations and Executive Director of the Data Nutrition Project, an initiative that builds tools to mitigate bias in artificial intelligence. They have extensive product management experience at organizations including Google, the MIT Media Lab, the US Digital Service (EOP / The White House) and McKinsey & Company. Kasia is also an affiliate at the Berkman Klein Center at Harvard University.

        View Bio for Course Contributor (Video): Kasia Chmielinski

      • Pre-Course Self Assessment

        Before you dive into this course, spend a few moments reflecting on your familiarity with the topic and your current level of skills confidence. 

        You will then re-visit the same questions in our Post-Course Self Assessment and reflect on how the course has helped you develop in confidence and grow your skills. 

        • Module One: What is AI?

          This module will help you to: 

          1. Define what AI is and describe how it differs from human capabilities 
          2. Identify different types of AI systems and explain how they work at a basic level  
          3. Trace key milestones in the development of AI, from early concepts to modern generative tools  
          4. Recognize how AI is already integrated into daily life across various tools, platforms, and industries

        • Module Two: How Do I Use Gen AI?

          This module will help you to: 

          1. Reflect on what Gen AI is, how it differs from other types of AI, and how it’s able to produce content  
          2. Identify appropriate use cases for Gen AI across academic, creative, and professional contexts 
          3. Recognize the limitations of current Gen AI systems, including what they can and cannot reliably do  
          4. Write effective prompts and provide helpful context that can improve output

        • Module Three: How Can I Engage Critically with AI?

          This module will help you to: 

          1. Evaluate the trustworthiness of AI-generated outputs and check for bias, errors, and hallucinations 
          2. Apply best practices for staying informed and critically engaged with evolving AI technologies 
          3. Identify the key ethical challenges in AI’s development and use, including bias, transparency, and accountability    
          4. Reflect on the social, environmental, and labor impacts of the use and development of AI 

        • Module Four: How Is AI Governed, and What Is My Role?

          This module will help you to: 

          1. Familiarize yourself with emerging legal and regulatory frameworks for AI in different regions 
          2. Distinguish between current AI capabilities and AI speculations, such as artificial general intelligence (AGI)   
          3. Recognize the role of individuals and communities in shaping the future of AI   
          4. Develop a grounded sense of responsible use of AI to prepare for academic, professional, and everyday contexts 

        • References and Glossary of Key Terms

          In addition to the glossary you’ll find woven throughout the course, you can find the full glossary collated in one place here.  

        • Post-Course Self Assessment

          Now you’ve completed the course, spend a few moments reflecting on where your familiarity with the topics and your confidence skills levels are at now.  

          Has the course helped you develop new skills and grow your confidence? 

          You'll need to complete the Post-Course Self Assessment in order to download your certificate. If you didn't do the Pre-Course Self Assessment before starting the course, please go to the top of the page and reflect on your familiarity with the topic and your level of skills confidence before you started the course.

          • Completion: Certificate

            Completing all modules (plus the pre and post-course assessments) will unlock the course certificate, which you can then download here. Your course certificate will only be made available once you have completed all these sections.

            If you have difficulty accessing your certificate, please contact the Sage support team at: onlinesupport@sagepub.co.uk. You can also check out this FAQ page which may be helpful.

            • Give Feedback About This Course

              Did you enjoy the course? Please take two minutes to share your feedback. We use learner feedback in future course updates and developments to provide an excellent learning experience. 

            • Accessibility

              We have high standards of accessibility on Sage Campus and as of May/June 2024 all activities within this course are keyboard and screen reader compatible. For more details on accessibility standards, please see the Sage Campus Accessibility Guide.

              For those using assistive technology, please note that within this course:

              • Tab components: JAWS and NVDA behave slightly differently. For NVDA to keep reading, it is best to exit focus mode and go back to browse mode. 
              • Matching: JAWS does not read out question label on dropdown focus.