This interactive presentation will explore the potential and pitfalls of using GenAI to support UDL principles that could benefit all learners including those with learning disabilities and are English learners. The presentation will have three portions: UDL and GenAI exploration, exploratory research findings, and strategies for overcoming obstacles.

The full impact that generative artificial intelligence (GenAI) will have on teaching and learning is unclear. Despite GenAI’s limitations and threats to learning, initial explorations suggest that the integration of GenAI has the potential to improve instruction and provide personalized feedback and interactions for underserved populations such as learners with disabilities (Golinkoff & Wilson, 2023; Wilson, 2023) and English learners (Bin & Mandal, 2019; Haristiani, 2019; Ma, 2021). For instance, Javier and Moorhouse (2023) highlighted pedagogical benefits of using GenAI such as providing personalized feedback, writing assistance, and providing a conversation partner. GenAI-based tutors, AI-enabled chatbots, and AI assistants can use data inputs to provide personalized instruction, offer individualized feedback, and reduce cognitive load for students who may struggle to achieve learning outcomes in general education (Marino et al., 2023). Media will only meaningfully impact learning when it changes methods (Greenhow et al., 2022). As a result, regardless of its affordances and potential, generative AI will only have a meaningful impact on students if instructors use it to change their teaching practices. Instructional models can provide important frameworks for understanding the ways that generative AI can improve teaching and learning for students with disabilities and English learners. Specifically, we will focus on how generative AI can support the implementation of the Universal Design for Learning (UDL). Universal Design for Learning Guided by the UDL framework, generative AI has the power to support personalized learning experiences that address individual learner needs and preferences. The UDL framework reshapes curriculum development in order to provide opportunities for students with various abilities and needs to engage and succeed in general education activities (Meyer et al., 2014; Rose & Meyer, 2002). Specifically, the three UDL principles are multiple means of engagement, representation, and action/expression (CAST, 2018). To further guide educators, three UDL principles are broken into nine guidelines and 31 checkpoints. Those offer suggestions on how instruction can be developed to motivate learners in different ways, to present content in various formats, as well as to allow learners demonstrate their learning and understanding in a myriad of ways. As a result, personalized instruction removes barriers and ensures access and participation in an appropriately challenging curriculum for all students, decreasing the need to create individual modifications (Meyer et al., 2014). The main premise of UDL is that all learners can learn if the instruction follows the intentional and proactive design (Author, 2020). Even though UDL was originally developed to support students with disabilities, several more recent publications focused on using UDL to support multilingual learners. In 2017 Ralabate and Nelson suggested combining Culturally Responsive Teaching (CRT) and UDL frameworks to address the needs of English learners and ensure culturally responsive and inclusive instruction. A book by Rao and Torres (2019) introduced a myriad of UDL strategies and digital tools to support language learners in inclusive classrooms. Ideas for developing reading, writing, vocabulary, and integrated language across the curriculum in various content areas can benefit language learners. UDL can be especially beneficial when English for Speakers of Other Languages (ESOL) instructional materials and assessments may present challenges for multilingual learners with or without comorbid learning disabilities (Delaney & Hata, 2020). While there is no consensus between educators as to how this technology should be used by and/or with students, referring to the UDL Design Cycle (Meo & Rao, 2016) may provide some guidance. Just like with generative AI, instructors often ask if it is feasible to provide multiple options for their learners. UDL Design Cycle guides educators to carefully identify the goal and learning outcomes of the lesson. Considering the skills and knowledge required to achieve the goal/learning objective and aligning the formative and summative assessments as well as the instructional methods and materials with that goal/learning objective will allow removing the barriers in the curriculum while maintaining the academic rigor (Meo & Rao, 2016). Similarly, generative AI such as ChatGPT forces us to reconsider if demonstrating learning one way (e.g., in writing) is the best way to assess learning outcomes (Horwitz, 2022). Presentation Purpose and Structure While educators are still grappling with the future of education in the AI-enhanced world, the purpose of this proposed presentation is to better understand ways that GenAI can support UDL principles in ways that support all learners—including those with disabilities or are learning English. Specifically, the presentation will have three portions: UDL and GenAI exploration, research methods and findings, and strategies for overcoming obstacles. Portion 1: UDL and GenAI Exploration First, will introduce the UDL framework as well as the affordances of several specific GenAI tools. Together we will then explore ways that GenAI can be used to enable UDL. Participants will also be invited to share their own use of GenAI for teaching and learning and how their practice has intersected with UDL. Portion 2: Exploratory Research Methods and Findings At the end of June, 2023 during a professional development institute conducted with 137 general, special, and English for Speakers of Other Languages (ESOL) teachers working with multilingual learners with and without disabilities in suburban school districts in the Mid-Atlantic region. The summer institute focused on presenting and discussing practices for teaching content to multilingual English learners, including translanguaging, multiliteracies, UDL, and generative AI. Data were collected in one large-group plenary session and two workshops that were each offered to teachers twice to allow more small-group activities. Data were first collected using a series of whole group polls using the PollEverywhere program during the plenary session. The small group activities in the workshops also produced data that were analyzed. Specifically, during the workshop on UDL, participants were asked to (a) write one minute paper to reflect on how they were already removing barriers for their learners; (b) share web-based resources that they used to present content in multiple ways; and (c) identify and share a UDL guideline that they would want to improve on in the upcoming year. In the other workshop, participants used OpenAI’s ChatGPT to perform several common teaching and learning activities. Each group was then provided a large piece of paper and asked to make two lists: benefits and drawbacks. Once their lists were created, participants revisited the UDL framework and asked to categorize their benefits and drawbacks. Our analysis found that overall use of GenAI was low with 75% of participants reporting never using it. Participants also reported a 50%-50% split between the responses about ChatGPT and other generative AI being a friend or a foe in their teaching and their students’ learning. The following are some of the drawbacks and benefits to GenAI negative aspects of generative AI (themes that emerged from the brainstorming lists): • Multiple means of engagement: o Drawbacks: lack of effort, user error in input, humanness, develop more resistance to work that requires sustained effort, lack of empathy, and loss of stamina. o Benefits: use of different and native language, recruitment of learners’ interest, reduction of writer’s block, and generation of ideas for assessment, and complement to human interaction. • Multiple means of representation: o Drawbacks: incorrect information based on old data, lack of nonverbal cues and oral language, limited understanding of content (“just copy and paste”), and bias in responses. o Benefits: developing language and proficiency, access to different writing models and structures, help with grammar, support comprehension and expansion of vocabulary, and provide feedback for writing. • Multiple means of action and expression: o Drawbacks: cheating and plagiarism, limiting critical thought, inhibiting creativity, and encouraging laziness. o Benefits: having discussions with AI, providing learners with language practice, helping English learners express themselves, and helping students use grammar. A fuller list of participant statements will be provided during the presentation but it is important to note that teacher comments tended to be more positive following their exploration of ChatGPT. Portions 3: Strategies for Overcoming Obstacles In the final portion of the presentation, we will facilitate a discussion on additional drawbacks and benefits that attendees perceive when using GenAI for support multiple means of engagement, representation, and expression. We will also guide the discussion to specific strategies that may help to overcome those obstacles in ways so that GenAI can better enable UDL to empower all learners.