Huge amounts of data fill today’s world, and that data is driving big breakthroughs in artificial intelligence (AI) research and the field of machine learning. As the volume of data surpasses humans’ ability to make sense of it manually, we’ll rely more on automated systems to process and learn from lots of data, and the changes in that data, to continue to make data useful in improving systems, services, products, and so on. We already see machine learning used in everyday technologies such as search, recommendations, tagging photos, and speech recognition. Myriad businesses are investing in using machine learning in their products, and the education sector is no different—according to the Artificial Intelligence Market in the US Education Sector report, artificial intelligence in U.S. education will grow by 47.5% from 2017-2021. Machine learning is a technology that uses data to answer questions or make predictions, and is a type of AI. AI is any task done by a machine that would require a human to apply intelligence to accomplish the same activity. Machine learning is valuable because, by processing huge amounts of data and finding meaningful patterns in it, it transforms data into useful knowledge. Machine learning enables applications that can benefit educational systems and tools. Learning analytics systems can build statistical models of students’ knowledge to provide feedback on their progress. Content analytics systems organize instructional content including assessments, lectures, and reading assignments. Scheduling algorithms help students learn more efficiently, and grading systems support teachers by assessing student responses, and tracking assessments. Active learning systems adaptively select content for each student to personalize learning. In creating educational materials, publishers face the challenge of quality issues with automated content conversion. Automated conversion from PDF to ePub is not 100% error free. It is low fidelity and the process is very slow. Manual testing of the content conversion is the biggest cost factor in the conversion process, and can lead to issues with complete content coverage due to the large volume of content. Magic EdTech offers a solution in an AI-driven process that finds and resolves these issues and eliminates the manual testing process. This improves cost benefits up to 50% and time efficiency by 70%. Most importantly, the AI process provides an end to end content coverage with near to 100% fidelity. AI and machine learning are impacting instructional practice in important ways in both the classroom and in professional education. In the corporate learning space, AI can address challenges with content creation. Organizations may find content creation tools too complex to use, so the rely on PowerPoint presentations and informal meetings for training. Content creation for digital training solutions can be slow and challenging due to the process of tagging content. Magic EdTech offers AI solutions to address these issues with content creation. An employee can convert any document into “trackable” training content. Content artifacts are automatically meta-tagged using voice recognition and NLP for searchability in order to optimize content recommendations and search results and eliminate the need for manual tagging. These solutions mean that the most appropriate content can be compiled for training. Magic EdTech solutions also address the need for different courses for different audiences. All related courses are created and managed as a single course. Different course elements can be assigned to different groups, effectively creating multiple courses, but with only a need to update once. In addition, these AI-powered content tools can capture users’ responses to predict their learning patterns and preferred content. Schools use machine learning applications to support teachers and allow them to do more than ever before. One major challenge with digital instructional content is the lack of an engaging user interface. To address this issue, Magic EdTech solutions use AI to enable individualized learning by allowing teachers to differentiate instruction, even for a large number of students. For example, large learning companies utilize Magic EdTech’s AI-based learning experience platform LE-AP and AI-powered facilitator, the Kea chatbot, to make their instructional interfaces more intuitive, engaging, and personalized. These technologies address problems such as low engagement and mismatches between content and a learner’s behavior by using machine learning to predict learner behavior, assesses learning needs, and show relevant content to the user. In addition, AI tools increase access and connectivity by powering virtual professional development conferences and global classrooms, including tools that connect students who speak different languages or who are differently abled. As AI and machine learning technologies continue to develop, efficient, data-driven, personalized learning will become the standard that students expect, just as people will increasingly expect personalization in personal technology, shopping, entertainment, and healthcare. When applied effectively, AI and machine learning are valuable tools for educators to enhance each student’s learning. And since our workforce and students are headed toward a future where many forms of AI will be a reality, it’s important for instructors and educational institutions to introduce the technology today.