Abstract
This chapter explores how generative artificial intelligence (GenAI) can expedite universal design for learning (UDL) in online courses, addressing challenges like time constraints and complex instructional development. It highlights GenAI tools, such as ChatGPT and Otter.ai, for their role in enhancing empathy, comprehension, and executive functions, thereby meeting diverse learning needs. The chapter describes GenAI's instructional design applications, focusing on personalizing course materials and assessments. It also presents a case story about an online graduate course to illustrate practical GenAI applications in UDL enhancement. Additionally, it examines the benefits, challenges, and ethical considerations of GenAI-enhanced UDL, stressing the need for human oversight and continuous educator adaptation to meet student needs effectively.
TopLearner Variability, Udl, And Personalized Learning In Online Environments
In online learning, it is pivotal to recognize and address learner variability to ensure engagement and achievement. This includes considering not only technical access like devices and internet connectivity but also cognitive access related to students' familiarity with online tools and platforms. Vasinda and Pilgrim (2023) highlight that the rise in blended and fully online K–12 instruction has made online learning a practical option for all students, including those with disabilities, to accommodate learner variability. Asynchronous learning, common in online settings, requires students to have vital executive function and self-regulation skills, elements younger students may lack, thus necessitating guidance from adults or guardians (Rao, 2021). Implementing UDL significantly aids in tackling these challenges. UDL-driven online education could incorporate features like a clear Welcome or Start page, orientation videos for course navigation, and captions and transcripts for videos (Singleton et al., 2019). Additionally, as Smith and Basham (2014) suggest, designing online courses with UDL principles ensures that all students, especially those with specific needs, fully access, and benefit from the curriculum, making UDL an invaluable pedagogical approach.
Personalized learning involves continuously identifying and responding to each student's learning needs, creating individualized learning paths tailored to each unique learner (Taylor et al., 2021). Personalized learning, situated within the UDL framework, is a systematic learning design approach that customizes instruction to individual student's strengths, preferences, needs, and goals, aiming to deliver comprehensive educational experiences that encompass enhanced access to various disciplines and the development of 21st-century skills (Zhang et al., 2020b). Personalized learning offers flexibility and support in what, how, when, and where students learn and demonstrate mastery of learning. This flexibility extends to instructional approaches, content, activities, learning objectives, outcomes, pace of learning, and alternative pathways toward college and career (Zhang et al., 2022a). Importantly, personalized learning empowers students by allowing them to have a voice and make choices in their learning, enabling them to shape their educational journey (Basham et al., 2016), and frequently harnesses data and technology to improve access to high-quality learning experiences, assist teachers in implementing effective practices, and reinforce the technological infrastructure at the school level (Zhang et al., 2020a).
Personalized learning aligns with UDL principles by adhering to three overarching principles within the UDL framework (Zhang et al., 2022b):
· Providing Multiple Means of Engagement: Personalized learning engages students by tailoring instruction to their interests and preferences, making learning more motivating and relevant.
· Providing Multiple Means of Representation: It offers various ways to present information, accommodating diverse learning preferences and abilities.
· Providing Multiple Means of Action and Expression: Personalized learning allows students to demonstrate their understanding in multiple ways, giving them choices in expressing their knowledge and skills.
Key Terms in this Chapter
Data Privacy: The aspect of information technology that deals with the ability an organization or individual has to determine what data in a computer system can be shared with third parties.
Machine Learning Models: Algorithms used in AI that allow computers to learn from and make decisions based on data, playing a significant role in the functionality of GenAI.
Generative Artificial Intelligence (GenAI): A type of AI that uses algorithms to generate new content, including text, images, or videos, that mimics human expression. It's characterized by its ability to automate decisions and create content based on data patterns.
Learner Variability: The concept that recognizes a diverse range of learners with varying abilities, backgrounds, and learning preferences in educational settings.
Executive Functions: These are a set of cognitive processes that include working memory, flexible thinking, and self-control, crucial for learning and managing tasks.
Algorithmic Bias: The systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.
Artificial Intelligence (AI): A field of computer science focused on creating machines capable of performing tasks that require human intelligence. AI systems can learn from data, make decisions, and solve problems autonomously, encompassing applications like automation, problem-solving, and language understanding.
Cognitive Access: Refers to the ease with which learners can process and understand information, often considered in the design of educational materials to ensure they are comprehensible to all learners.
Personalized Learning: A systematic learning design approach that customizes instruction to individual student's strengths, preferences, needs, and goals, aiming to deliver comprehensive educational experiences that encompass enhanced access to various disciplines and the development of 21st-century skills ( Zhang et al., 2020b ).
Adaptive Learning: An educational method that uses AI algorithms to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner.