3 Ch3: Personalized Learning Made Easy

• This chapter provides educators with a clear understanding of personalized learning and how to implement it effectively with AI for adaptive learning.

Personalized learning with Generative AI (GenAI) offers a transformative approach to education, tailored to meet the unique needs, preferences, and learning pace of each student. Chapter 2 provides several examples of using GenAI in chatbot format (i.e., conversational GenAI) to support and enhance personalized learning. While personalized learning has long been supported by AI in general—primarily through adaptive learning systems—GenAI introduces a new dimension. Although this book focuses on GenAI, it’s important to distinguish between AI for adaptive learning and GenAI in a more specific sense. Understanding the strengths and characteristics of each type of AI will help educators recognize how they can complement each other to provide more personalized learning experiences for students.

In a nutshell, GenAI creates new content (text, audio, video etc.) based on users’ input, while AI for adaptive learning adjusts its offering of rich preloaded content and feedback based on users’ input. The following is a brief comparison of the two.

GenAI and AI for Adaptive Learning: A Comparison

1. Purpose

• • Generative AI: Focused on creating new content, including text, images, audio, or code. Its main purpose is to generate human-like content based on input or prompts.
  • AI for Adaptive Learning: Tailored for education, its goal is to personalize learning experiences by adjusting content and learning paths according to an individual’s progress, learning needs, and preferences.

2. Core Functionality

• • Generative AI: Uses machine learning models to create new content from scratch. It can generate anything from creative writing and code to visual art or music based on user input.
  • AI for Adaptive Learning: Uses algorithms to track and analyze students’ performance and adapt the material in real-time. It can suggest next steps, remediate gaps, or challenge learners with more difficult tasks.

3. Technology Behind

• • Generative AI: Utilizes neural networks, particularly transformer-based architectures in models like GPT4o or Gemini, to learn patterns from large datasets and produce new content that mimics those patterns. It often employs unsupervised or semi-supervised learning techniques.
  • AI for Adaptive Learning: Often relies on decision trees, data-driven models, reinforcement learning, and predictive analytics to understand the learner’s needs. These models adapt dynamically, responding to changes in performance.

4. Data Requirements

• • Generative AI: Trained on vast datasets and fine-tuned for specific tasks. It doesn’t rely on continuous real-time feedback but on large-scale data patterns.
  • AI for Adaptive Learning: Requires ongoing student data such as responses, time taken, and performance metrics. The data is used to adjust learning paths in real-time.

5. Application Examples

• • Generative AI:
    • ChatGPT for generating text and stories, or DALL·E for generating images based on a prompt.
    • Automated code generation tools (e.g., GitHub Copilot) or AI-assisted creative design platforms (e.g., Canva).
  • AI for Adaptive Learning:
    • Adaptive learning platforms like DreamBox, Knewton, or Smart Sparrow that customize lessons based on student interaction.
    • Learning management systems (LMS) integrating AI to provide personalized curriculum paths.

6. Challenges

• • Generative AI: Can produce biased, incorrect, or inappropriate content depending on the quality of training data. There are also concerns about intellectual property and ethical use.
  • AI for Adaptive Learning: Requires accurate modeling of student behavior and learning processes, which can be complex and varied across individuals. It also can face challenges in balancing personalization with educational standards.

7. Ethical Considerations

• • Generative AI: Issues like deepfakes, misinformation, and content ownership are central concerns. Additionally, addressing the ethical implications of what AI generates is a growing challenge.
  • AI for Adaptive Learning: Privacy concerns regarding how student data is collected, stored, and used. There’s also the challenge of ensuring that AI doesn’t unintentionally disadvantage certain learners.

These distinctions highlight how these technologies complement each other in education. Generative AI can help personalize learning by creating tailored content and interactions based on student needs and input, while adaptive learning AI adjusts learning pathways in real time by analyzing ongoing student performance. Together, they enhance both the content and the delivery of personalized learning experiences.

Next, we will discuss different tools available that are powered by GenAI or AI for adaptive learning.

GenAI and AI for Adaptive Learning: Tools

GenAI for Personalized Learning

MagicSchool is a generative AI platform designed for educators, offering over 70 tools to assist with various aspects of teaching and learning. This platform facilitates the creation and delivery of original educational content, lesson plans, and assessments. It is engineered to be user-friendly and compatible with existing Learning Management Systems (LMS), providing export options for the content created. MagicSchool is particularly focused on aiding teachers in saving time on lesson planning, differentiation, writing assessments, Individualized Education Programs (IEPs), and enhancing communication. Additionally, it introduces MagicStudent AI, a tool aimed at helping students learn AI literacy skills and apply them to solve real-world problems​.

Poe (Platform for Open Exploration). Quora launched the Poe in December 2022, enabling users to interact with a range of Generative AI bots built on different large language models (LLMs), such as OpenAI’s GPT-4 and Anthropic’s Claude. Teachers can use Poe to ask questions, receive instant answers, and engage in conversations with AI-powered models. The platform supports various bots and is not tied to any single AI provider. Available as both a website and an app, Poe’s features and flexibility make it potentially useful for teachers  to create, store, and use AI chatbots from multiple sources.

Khanmigo is an AI-powered learning assistant developed by Khan Academy. It can serve as a virtual tutor for students and a teaching aid for teachers. Powered by a large language model (LLM) like GPT-4, Khanmigo interacts with users through conversations, providing personalized learning support, answering questions, and guiding students through complex topics across various subjects such as math, science, and literature. It can also support teachers’ lesson planning by creating quizzes, rubrics, or exit tickets.

MagicSchool, Poe, and Khanmigo are platforms designed to support education, but each focuses on different aspects. MagicSchool offers diverse GenAI tools to help teachers create content, lesson plans, assessments, and IEPs, with a focus on saving time and integrating with LMS systems. Poe enables interaction with multiple Generative AI bots (like ChatGPT and Claude), allowing educators to engage in AI-powered conversations and create chatbots from various sources. Khanmigo, developed by Khan Academy, serves as a personalized AI tutor for students and teaching aid for teachers, offering real-time tutoring, assistance with homework, and support across subjects. While all three platforms aim to enhance the learning process, MagicSchool emphasizes teaching efficiency, Poe focuses on flexible AI interactions, and Khanmigo provides personalized, subject-specific tutoring.

More ideas and tools for supporting personalized learning using GenAI will be discussed in Chapter 4 where suggestions and examples by subjects are also presented.

AI for Adaptive Learning

While AI for adaptive learning is not the main focus of this book, it has great value for educators and can complement GenAI to support educators. Therefore, the approaches and resources for leveraging AI for adaptive learning are also discussed in the following sections.

Here are some key points to help develop understanding and effectively implementing personalized learning with AI for adaptive learning in teaching practices.

Understanding Personalized Learning with AI

Personalized learning with AI involves using artificial intelligence to tailor educational experiences to the needs of individual students. It can adapt content, pacing, and learning strategies based on real-time feedback and data on a student’s performance and preferences. For educators, this means leveraging AI tools to create a more engaging, effective, and individualized learning environment. Personalized learning with AI represents a shift from one-size-fits-all education to an adaptive learning experience that is tailored to the unique needs of each student. For instance, AI-driven platforms like IXL or Aleks use algorithms to personalize learning paths based on individual performance and preferences. These platforms analyze student interactions to recommend the next best steps in their learning journey, ensuring that content is always aligned with their current knowledge level and learning goals.

Identifying Student Needs and Preferences

Beginning by gathering data on students’ interests and current knowledge levels, AI can analyze this data to provide insights into effective teaching strategies for each student. Educators can use surveys, quizzes, and AI analytics tools to collect and interpret this information, enabling them to design lessons that resonate with every learner. Adaptive learning platforms such as DreamBox Learning allow educators to understand and cater to diverse learning needs and preferences. For example, DreamBox Math adjusts mathematical problems in real-time based on student responses, offering a more personalized learning experience. Additionally, educators can use surveys from Google Forms or feedback tools like SurveyMonkey to gather insights into students’ interests and learning needs, enabling more targeted lesson planning.

Customizing Content and Pacing

Use AI to adapt curriculum content and pacing to match the learning speed and style of each student. This could mean providing more challenging materials for advanced learners or offering additional support and practice for those who need it. AI can also suggest alternative resources or activities that align with individual learning goals and preferences. Adaptive learning platforms, such as Smart Sparrow and Knewton Alta, enables educators to tailor content and pacing to individual students. Such platforms can adjust the difficulty of tasks, introduce new topics at the right time, and provide alternative resources, such as videos or interactive simulations, to support diverse learning needs. By customizing the learning experience, these tools help maintain student engagement and ensure that each learner progresses at an optimal pace.

Incorporating Interactive and Adaptive Learning Tools

Integrate AI-powered educational software and apps that offer interactive and adaptive learning experiences. These tools can adjust in real-time, presenting new challenges as students master concepts or revisiting topics that require more practice. Such tools can also introduce gamified elements to learning, increasing engagement and motivation. Interactive learning tools like Duolingo for language learning or Codecademy for coding skills use AI to adaptively respond to student progress and mistakes, offering a highly personalized learning experience. These platforms often incorporate gamification elements, such as points, levels, and badges, to motivate students and make learning more engaging.

Implementing personalized learning with GenAI and AI for adaptive learning requires a thoughtful and strategic approach, but the potential benefits for student engagement, understanding, and achievement can be significant. By leveraging strategies, resources, and AI tools, educators can enhance the personalized learning experience for their students, ensuring that education is more engaging, effective, and aligned with individual needs and goals, and creating more effective, responsive, and inclusive learning environments.

Ch3 Bibliographies

• What the heck is a deepfake? https://security.virginia.edu/deepfakes
• The future of education: How AI and Adaptive Learning are shaping the classrooms of the future. https://www.infosysbpm.com/blogs/education-technology-services/the-future-of-education-how-ai-and-adaptive-learning-are-shaping-the-classrooms-of-the-future.html
• IXL Adaptive Learning Platform. https://www.ixl.com/membership/administrators/curriculum
• MagicSchools. https://www.magicschool.ai/?gad_source=1
• Poe. https://poe.com/
• Khanmigo. https://www.khanmigo.ai/
• Artificial Intelligence Courses Online. https://www.coursera.org/courses?query=artificial%20intelligence