Hosts of the Tea for Teaching Podcast will ask panelists to grapple with the ways digital accessibility, universal design for learning, and artificial intelligence intersect in creating an inclusive environment for all online learners. Policy, pedagogy, and practical considerations for equitable online and mixed-modality learning environments will center the conversation.

The rapid development and proliferation of generative artificial-intelligence (AI) tools provides new opportunities to better support student learning in asynchronous and mixed-modality courses for our increasingly diverse student populations (Chandramouli, 2022). Course developers using a universal design for learning (UDL) approach can apply these tools to more efficiently create rich authentic learning experiences that are relevant for all of our students (Banes & Behnke, 2019). AI tools have also been found to provide assistance to neurodivergent students with assignment and assessment-related tasks in Higher Education (Jesse, 2024). AI tools also can be used by course developers and students to more efficiently create digitally accessible content (Chemnad & Othman, 2024). In this session, the hosts of a long-running popular weekly higher-education podcast series will facilitate a rich dialogue among experts in accessibility, universal design for learning, disability studies, and neurodivergence about strategies for building and maintaining equitable online and mixed-modality learning environments. This panel session will be moderated by two podcast co-hosts who come from different disciplines and have extensive experience facilitating conversations and workshops together. This session will consist of a facilitated panel discussion in the style of a typical podcast (but with a live audience). Session participants will be invited to ask questions of panelists through an online form during the session. We will also share a link to a Padlet (or similar tool) in which session participants can share their own practices related to the use of AI tools in support of the UDL framework and learner diversity. If organizers of the OLC Accelerate conference agree, we would like to record this session for later release as a podcast episode for wider distribution. Topical Outline During this session, panelists will address questions such as: In what ways can AI tools be used to more efficiently support the development of accessible digital content (Emvenova, Borup, & Shin, 2024)?' What barriers might students face in using AI tools in their online learning experiences (Mosley, 2023)? How can instructors and designers use AI tools to help create a more equitable and inclusive learning environment for our students (Botelho, 2021)? What roles do AI tools have in UDL-crafted online learning spaces, and how do those roles shift among live, asynchronous, and mixed-modality formats (Morgan, 2024)? How might AI tools be beneficial to maximize learning and assessment for neurodivergent students in online learning (Motti, 2019)? How can AI tools be used to provide learning support that is customized to the specific needs of each student (Kakish, Robertson, & Pollacia, 2022)? Might student use of AI tools allow students and instructors to focus more on higher-order skills rather than on spelling and grammar skills (Southworth, et al., 2023)? How can we design our learning activities so that AI tools are used as a complement to learning and not as a substitute for learning (Song, et al., 2024)? There are many conversations happening in departments and institutions about creating policies related to AI. What are some key features you’d want to see in policies that support students with disabilities? Takeaways Participants in this session will leave with a deeper understanding of the myriad ways in which AI tools can be used to support student learning in ways that center access and are inclusive in online and mixed modality contexts. The panelists will also share a curated list of recommended resources with session attendees at the end of the session References Banes, D. & Behnke, K. (2019). The Potential Evolution of Universal Design for Learning (UDL) Through the Lens of Technology Innovation. In Universal Access Through Inclusive Instructional Design. New York: Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9780429435515-43/potential-evolution-universal-design-learning-udl-lens-technology-innovation-david-banes-kirk-behnke. Botelho, F. H. F. (2021). Accessibility to digital technology: Virtual barriers, real opportunities. Assistive Technology, 33(sup1), 27–34. https://doi.org/10.1080/10400435.2021.1945705. Chandramouli, K. (2022). Role of AI in Promoting European Accessibility Policy. Communications in Computer and Information Science, 1(1655). https://doi.org/10.1007/978-3-031-19682-9_77. Chemnad, K. & Othman, A. (2024). Digital accessibility in the era of artificial intelligence—Bibliometric analysis and systematic review. Frontiers in Artificial Intelligence, 7(1349668). https://www.frontiersin.org/articles/10.3389/frai.2024.1349668/full. Evmenova, A. S., Borup, J. & Shin, J. K. (2024). Harnessing the Power of Generative AI to Support ALL Learners. TechTrends. https://doi.org/10.1007/s11528-024-00966-x. Jesse, T. (2024). Creating Neuro-Inclusive Learning Environments: Integrating Generative AI and Outcome-Led Selection of Teaching Methods. In Autism, Neurodiversity, and Equity in Professional Preparation Programs. Hershey, PA: IGI Global. https://www.igi-global.com/chapter/creating-neuro-inclusive-learning-environments/335213. Kakish, K., Robertson, C., & Pollacia, L. (2022). Advancing Adaptive Learning via Artificial Intelligence. Lecture Notes in Networks and Systems, 1(296). https://link.springer.com/chapter/10.1007/978-3-030-82199-9_47. Morgan, A. (2024). Leveraging Generative Artificial Intelligence to Expedite UDL Implementation in Online Courses. In Unlocking Learning Potential With Universal Design in Online Learning Environments. Hershey, PA: IGI Global. https://www.igi-global.com/chapter/leveraging-generative-artificial-intelligence-to-expedite-udl-implementation-in-online-courses/342194. Mosley, N. (2023). What's AI's impact on synchronous online learning? EdTech blog. https://www.neilmosley.com/blog/whats-ais-impact-on-synchronous-online-learning. Motti, V. G. (2019). Designing emerging technologies for and with neurodiverse users. In Proceedings of the 37th ACM International Conference on the Design of Communication (SIGDOC '19), Article 11, 1–10. New York, NY: Association for Computing Machinery. https://doi.org/10.1145/3328020.3353946. Song, Y., Weisberg, L. R., Zhang, S., Tian, X., Boyer, K. E., & Israel, M. (2024). A framework for inclusive AI learning design for diverse learners. Computers and Education: Artificial Intelligence, 6(1). https://doi.org/10.1016/j.caeai.2024.100212. Southworth, J., Migliaccio, K., Glover, J., Glover, J. N., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4. https://doi.org/10.1016/j.caeai.2023.100127.