Serena Sacks-Mandel, Global Chief Technology and Transformation Officer – Education, Microsoft

Ms. Sacks-Mandel is the Worldwide Chief Technology and Transformation Officer at Microsoft for the education industry. Prior to joining Microsoft, she was the Chief Information Officer at two unique, technically-advanced large public-school districts where she enabled student centric teaching and learning, which resulted in significant improvements in student outcomes. Prior to pivoting to Education, she has led innovation teams at IBM, Walt Disney World, and HMH, and provided management consulting at many others. Serena has won numerous state, national, and global awards for leadership, vision, technical excellence, and her commitment to supporting women in technology.


Personalized learning is a concept that has been around for a long time, but in the last several years, its relevance has been renewed in part, because technology enabled it to be more effective, accessible, and needed for students to regain momentum following the COVID pandemic. The basic concept of personalized learning is to provide students with an education that is tailored to meet each students’ individual needs, interests, and abilities, rather than using a one-size-fits-all approach.

The origins of personalized learning can be traced back to the early 20th century when educational psychologists such as John Dewey and Maria Montessori began advocating for a more student-centered approach to education. However, it wasn’t until the 1960s and 1970s that the concept gained momentum, thanks to advances in technology that made it easier to create individualized learning plans for students.

In the 1970s, the term “mastery learning” was coined by Benjamin Bloom, an educational psychologist at the University of Chicago. Mastery learning is a teaching method that involves breaking down complex skills into smaller, more manageable pieces and allowing students to progress at their own pace. This approach to learning is often cited as a precursor to personalized learning.

In the 1980s and 1990s, computer-based learning systems began to emerge, which enabled students to work through lessons and activities at their own pace. These systems also allowed teachers to track student progress and adjust their instruction accordingly. However, these early systems were often criticized for being too rigid and lacking the human touch of traditional teaching methods.

It wasn’t until the 2000s that personalized learning began to take off in earnest, thanks to the proliferation of online learning platforms and the development of adaptive learning technologies. These technologies use data and algorithms to create customized learning paths for each student, based on their performance and preferences.

Since the impacts of the COVID-19 pandemic on student achievement and outcomes around the world have become clearer, there has been demand for 1:1 tutoring which is unsustainable without technology and its ability to deliver personalized educational solutions. The pandemic required a sudden shift to remote and hybrid learning models which disrupted traditional modes of instruction and created new challenges for both students and teachers. Here are some of the ways in which the pandemic has affected student achievement and outcomes:

  1. Learning loss: Many students have experienced learning loss during the pandemic due to interrupted instruction, inconsistent attendance, and decreased engagement. Research shows that students have lost ground in math and reading, and that the impact has been particularly severe for disadvantaged students who may not have had access to the technology or support they needed to succeed in remote learning environments.
  2. Mental health and wellbeing: The pandemic has also taken a toll on students’ mental health and wellbeing, which can in turn affect their academic performance. Social isolation, anxiety, and stress related to the pandemic and other factors have all contributed to a rise in mental health concerns among students.
  3. Widening achievement gaps: The pandemic has highlighted existing achievement gaps and may have widened them in some cases. Students from low-income families and those who are English language learners or have disabilities have been particularly hard hit by the pandemic, as they may not have had the same access to resources and support as their peers.
  4. Changes in assessment and grading: The pandemic has also led to changes in assessment and grading policies in many schools and districts. Some schools have moved away from traditional grading systems in recognition of the challenges that students have faced during the pandemic.
  5. Increased use of technology: The pandemic has accelerated the adoption of educational technology in many schools and districts. While technology can help support learning and engagement, it can also exacerbate existing inequalities and may not be accessible to all students.

Generative artificial intelligence (AI) has the potential to transform personalized learning by enabling the creation of tailored content and resources for individual learners. Generative AI is a type of machine learning that uses algorithms to generate new content, such as text, images, or videos, based on patterns in existing data.

Some ways generative AI can contribute to personalized learning are:

  • Customized content: Generative AI can create personalized learning materials for students based on their individual learning styles, preferences, and needs. For example, a generative AI system could analyze a student’s performance data and generate new practice problems that target their specific areas of weakness.
  • Adaptive learning: Generative AI can be used to create adaptive learning systems that adjust their content and pace to suit each student’s level of understanding. These systems can use real-time data to personalize the learning experience and provide feedback and support that is tailored to each student.
  • Intelligent tutoring systems: Generative AI can also be used to create intelligent tutoring systems that provide one-on-one instruction and support to students. These systems can analyze student data and generate personalized feedback and guidance in real-time, helping students to stay on track and overcome any obstacles they may be facing.
  • Natural language processing: Generative AI can help personalize digital content learning by enabling natural language processing (NLP) systems that can interpret and respond to student inquiries in real-time. This technology can be used to create virtual tutors or chatbots that can answer questions, provide feedback, and offer guidance to students.

While generative AI has the potential to revolutionize personalized learning, it’s important to note that it is still a relatively new technology and there are concerns around privacy, truth, and ethics that need to be addressed. As with any new technology, it will be important to carefully consider the potential benefits and risks before implementing it in educational settings.

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