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There is a nationwide shortage of special education teachers (SETs) due, in part, to unmanageable workload expectations, which has reached crisis level. SETs are expected to modify, adapt, and accommodate general education curriculum to meet the needs of their students, communicate and collaborate with parents and general education teachers, and progress monitor on IEP goals, to name a few. SETs, especially those in more restrictive self-contained settings, report spending almost half of their time completing non-teaching tasks. One emerging and innovative solution to help SETs accomplish these tasks is using Artificial Intelligence (AI). AI powers many popular educational tools, such as predictive text, adaptive learning platforms, and digital assistants. The launch of ChatGPT, Bard, and other generative pre-trained transformers (GPTs), provides an opportunity to support SETs with some of the paperwork requirements. This is due to the GPTs ability to craft human-like responses via drafting essays, emails, lists, and the like. In this article, we provide step-by-step directions to use ChatGPT. Additionally, we illustrate how GPTs can be used to operationalize, automate, and streamline many of the SET's non-teaching tasks through specific examples of its use in (1) collaboration; (2) adapting readings; and (3) developing progress monitoring assessments.

The purpose of this descriptive review of nine research studies was to explore the types of educational technologies used with SWDs to support written expression (WE), the implementation of the educational technology for WE, and the methods and domains WE outcomes are measured. This review focused on: (1) the technologies utilized in combination with WE outcomes; (2) student demographics, background experience and knowledge of technology, and the training they received for the educational technology in the intervention; (3) strategy use for WE and the accompanying student training; and (4) the instrumentation and WE criteria assessed. Based on the results of this review, we provide recommendations for future research around educational technologies to support WE.

Traction is growing for personalized learning (PL) as an educational innovation that aims to provide learning experiences to meet diverse learning needs. However, little research has investigated how PL is positioned in professional standards that provide guidance on teacher education across content-specific disciplines and for diverse learner populations. Using an alignment methodology, this study identified and analyzed 193 out of 654 educator preparation standard components in the U.S. education system for the presence of teaching practices that may better respond to the need for PL implementation. This research revealed different percentages of standard components reflecting expectations for educators to understand learner characteristics, tailor instruction to learner needs, and advocate for promoting learning outcomes for individual learners. Moreover, noticeable patterns exist among standards across disciplines and for diverse learner populations regarding how they focus on knowledge, competencies, and dispositions in relation to preparing educators for PL implementation.