As AI becomes more ingrained in learning and the workforce<\/a>, the conversation is no longer just about technology. \u201cIt\u2019s increasingly about how to use AI responsibly, effectively, and ethically in everyday work,\u201d says Sha\u2019Kia Riggins, Dean of Information Technology at Florida State College at Jacksonville.<\/p>\n More than 50% of higher ed instructors<\/a> agree that teaching students responsible and effective AI use should be a top priority. For computing<\/a> and IT students<\/a>, AI literacy now means going beyond using AI to evaluating AI-generated content, identifying bias, verifying information, and making informed decisions.<\/p>\n Sha\u2019Kia Riggins and Baffour Osei, Robotics Lab Manager at Princeton University, address these evolving needs in their new first edition, \u201cAI and Machine Learning: Concepts, Applications, and Ethical Considerations<\/a>.\u201d This approachable text combines conceptual understanding, ethical AI, Python programming, and authentic coding experiences to help students understand how AI and machine learning work, develop practical skills, and think critically about the technology they build and use.<\/p>\n AI and machine learning can be intimidating, particularly for students encountering the subjects for the first time. Both Sha\u2019Kia and Baffour wanted \u201cAI and Machine Learning<\/a>\u201d to be accessible, expanding who has the opportunity to understand, contribute to, and shape emerging technology.<\/p>\n \u201cMy passion for teaching grew from wanting to explain complex concepts,\u201d Sha\u2019Kia says. \u201cTopics like algorithms, big data, and neural networks can be broken down so anyone can learn and apply them.\u201d<\/p>\n Rather than beginning with advanced mathematics or highly technical theory, the text provides a scaffolded path that allows students to gradually build their knowledge and confidence. Students encounter straightforward explanations, real-world examples, and hands-on activities that progress from no-code and low-code experiences to Python programming. Students can first explore how a concept works without being overwhelmed by syntax. As their understanding develops, they can begin experimenting, customizing, and creating.<\/p>\n \u201cI\u2019ve worked hard towards building learning environments that can meet participants where they are and scaffold their entry into unfamiliar spaces with a discovery-based methodology,\u201d says Baffour.<\/p>\n AI and machine learning courses can easily become heavily focused on algorithms, mathematical theory, or technical exercises. This course takes a broader approach, treating AI as a practical, ethical, and career-relevant field.<\/p>\n By connecting technical skills, ethical decision-making, and hands-on practice, the text prepares students to use AI while understanding, evaluating, and building with it.<\/p>\n \u201cAI and Machine Learning<\/a>\u201d includes a fully integrated experience in MindTap, Cengage\u2019s learning platform<\/a>, to help students move from understanding concepts to applying them.<\/p>\n \u201cThe MindTap course experience brings the textbook to life by transforming concepts into interactive, hands-on learning experiences,\u201d says Sha\u2019Kia. \u201cWhile the textbook provides the foundational knowledge, ethical frameworks, and real-world context, MindTap allows students to immediately apply what they learn through guided activities, simulations, knowledge checks, and project-based exercises.\u201d<\/p>\n In MindTap, students progress to working in cloud-based environments used by professional developers, including Coding Labs powered by GitHub Codespaces. Students get multiple opportunities to work with tools they may encounter in future courses or their careers while developing practical experience with Python and AI systems.<\/p>\n As organizations deploy AI systems at greater scale, concerns around privacy, transparency, security, fairness, and accountability are becoming increasingly important. Students need opportunities to explore not only what AI can do, but also when and how it should be used.<\/p>\n That\u2019s why responsible and ethical AI is integrated throughout \u201cAI and Machine Learning<\/a>.\u201d Ethical questions accompany concepts, algorithms, and hands-on activities, helping students consider potential consequences as part of the development process.<\/p>\n For Baffour, these conversations are inseparable from technical education because AI systems operate within larger social, economic, and environmental contexts. He recommends that students examine the broader concerns surrounding AI, from privacy and surveillance to environmental impact, intellectual property, and changes in the workforce. \u201cThese are very difficult conversations to have,\u201d Baffour says, \u201cbut they are conversations we bring up alongside the technical concepts because the pairing helps learners understand that these systems don\u2019t have to be developed in exploitative ways.\u201d<\/p>\n Integrating these discussions encourages students to think carefully about design choices, unintended consequences, and who may benefit or be harmed by the systems they develop.<\/p>\n For instructors preparing students<\/a> for an AI-enabled workforce, Sha\u2019Kia recommends focusing on skills that will remain useful even as technology changes. Encourage students to question AI-generated outputs, verify information, recognize bias, and understand the importance of ethical decision-making. Those habits can help students become more thoughtful AI users and developers regardless of which tools dominate in the future.<\/p>\n Baffour also encourages instructors to emphasize the learning process<\/a> itself, particularly through project-based work. \u201cIt is important that instructors state and constantly remind students that the process is more important than the outcome while emphasizing the important role failure plays,\u201d he says. For students developing a new skill, the purpose of a project isn\u2019t simply to produce a finished result using AI. The value lies in researching, experimenting, failing, iterating, and learning how to solve problems independently.<\/p>\n These goals reflect the central idea behind \u201cAI and Machine Learning<\/a>\u201d: Students must learn to work with AI thoughtfully, responsibly, and confidently.<\/p>\n \u00a0<\/p>\n Sha\u2019Kia Riggins is the Dean of Information Technology at Florida State College at Jacksonville. With over 35 years of expertise in business, management, analysis, and reporting, she brings a comprehensive understanding of artificial intelligence, application technologies, and systems infrastructure. She has a Master of Science in Information Technology, Bachelor of Science in Business Information Technology, and an Associate of Science in Computer Systems & Business Analysis. Sha\u2019Kia is a co-author of \u201cAI and Machine Learning: Concepts, Applications, and Ethical Considerations<\/a>,\u201d 1e.<\/p>\n \u00a0<\/p>\n Baffour Osei<\/strong><\/p>\n Baffour Osei is the Robotics Laboratory Manager at Princeton University. Baffour grew up in Ghana before attending Fisk University and Vanderbilt University, studying mechanical engineering through a dual degree program. He was the first engineering instructor at Landmark Christian School in Atlanta, GA, before moving to Princeton University. Baffour is a co-author of \u201cAI and Machine Learning: Concepts, Applications, and Ethical Considerations<\/a>,\u201d 1e.<\/p>\n \u00a0<\/p>\n \n <\/div>\nCreating an accessible path into AI and machine learning<\/strong><\/h2>\n
How \u201cAI and Machine Learning\u201d offers a new approach<\/strong><\/h2>\n
Key differentiators of \u201cAI and Machine Learning<\/a>\u201d<\/h3>\n
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Take students from learning to doing with \u201cAI and Machine Learning\u201d and MindTap<\/strong><\/h2>\n
Making responsible and ethical AI use central to learning<\/strong><\/h2>\n
How instructors can change their approach to AI and machine learning today<\/strong><\/h2>\n
About the authors<\/h3>\n
![\"Sha'Kia](\"https:\/\/blog.cengage.com\/wp-content\/uploads\/2026\/10\/ShaKia-riggins-150x150.png\")
Sha\u2019Kia Riggins<\/strong><\/p>\n![\"Baffour](\"https:\/\/blog.cengage.com\/wp-content\/uploads\/2026\/10\/Baffour-Pic-150x150.jpg\")
<\/strong><\/p>\nReady to introduce students to the rapidly evolving field of AI and machine learning through an accessible, application-focused curriculum? Explore \u201cAI and Machine Learning: Concepts, Applications, and Ethical Considerations<\/a>\u201d by signing in to your Cengage Instructor Center and adding the title to your library.<\/h2>\n