The latest CIDDL Office Hours brought together educators, researchers, and technology developers to discuss the growing importance of AI Literacy in education. Led by Dr. James Basham, Professor at the University of Kansas and Director of CIDDL, the session explored how educators can integrate AI responsibly into classrooms and prepare students for an AI-driven future.
Artificial Intelligence (AI) is no longer a distant concept; it is already embedded in the ways students learn and teachers work. For educators, AI has the potential to significantly reduce the burden of lesson planning and grading, allowing more time to focus on meaningful engagement with students (Goldman et al., 2024). For students, AI provides personalized support in reading, writing, studying, and research (CIDDL, 2024). Banning AI in schools is not only unrealistic but also unwise, because such restrictions prevent teachers from showing students how to use these tools responsibly and effectively. The question is not whether AI will appear in classrooms, but how educators can guide its appropriate and ethical use.
As the new school year begins, many special educators are once again juggling a familiar set of tasks: modifying assignments, preparing guided notes, breaking down complex presentations, and supporting students through homework completion. These tasks are essential, but they are also time-consuming. What if artificial intelligence (AI) could take on much of this workload, giving teachers back valuable time to focus on evidence-based practices and specially designed instruction? During a recent CIDDL Office Hour, Dr. Sean Smith highlighted practical ways AI can support special educators. His message was clear: AI is not here to replace teachers, but to reduce the time spent on routine accommodations so educators can invest more energy in effective instruction.
Artificial intelligence (AI) tools are becoming daily companions for students, assisting with writing, research, feedback, and more. For educators, these technologies offer exciting opportunities: more efficient workflows, personalized learning, and reduced cognitive burdens for students. But beneath these benefits lies an important question: Are these tools supporting real learning, or just better performance? In this post, we explore what Yan and colleagues (2025) tell us about generative AI’s impact on learning, and offer guidance on how to integrate AI into the classroom in ways that support students’ deeper thinking, motivation, and independence.
On August 19, 2025, CIDDL hosted an Office Hours session that brought together faculty, researchers, and practitioners to discuss one of the most pressing topics in education today: how to responsibly integrate artificial intelligence (AI) into teacher preparation and special education. Led by CIDDL Co-PI Dr. James Basham, the conversation highlighted both big-picture frameworks and low-lift classroom practices, making AI integration approachable for educators at all levels.
The third post in the three-part series provides practical AI integration ideas for immediate use in courses, including low-lift syllabus updates, in-class activities, and practicum tie-ins that help candidates apply AI tools responsibly and effectively in real-world teaching scenarios.
This second post in the three-part series will focus on department-level strategies for preparing future personnel, from faculty development and curriculum updates to research priorities and collaborative partnerships that position programs to lead in the AI era.
This three-part series will highlight technology integration with a focus on AI for the next generation of special education professionals. The first post will provide an overview of the changing technology landscape in special education, including how AI is reshaping instructional planning, accessibility tools, and professional expectations for both teachers and researchers.
The second post will focus on department-level strategies for preparing future personnel — from faculty development and curriculum updates to research priorities and collaborative partnerships that position programs to lead in the AI era.
The third post will provide practical AI integration ideas for immediate use in courses, including low-lift syllabus updates, in-class activities, and practicum tie-ins that help candidates apply AI tools responsibly and effectively in real-world teaching scenarios.
The second post will focus on department-level strategies for preparing future personnel — from faculty development and curriculum updates to research priorities and collaborative partnerships that position programs to lead in the AI era.
The third post will provide practical AI integration ideas for immediate use in courses, including low-lift syllabus updates, in-class activities, and practicum tie-ins that help candidates apply AI tools responsibly and effectively in real-world teaching scenarios.
In today’s classrooms, generative AI tools like ChatGPT are becoming ubiquitous. But while these tools may appear to be merely technical aids, students’ decisions about how and when to use them are deeply value-based. Recent studies suggest that student AI use is shaped by a complex set of psychological, academic, and social factors. Rather than being passive users, students weigh the benefits and costs of AI use, often in the context of academic pressure, peer influence, and personal confidence. This raises important implications for educators who want to support healthy and productive use of AI in learning. This blog post synthesizes three research articles by Acosta-Enriquez and colleagues (2025), Mariñas and colleagues (2025), and Zhang and colleagues (2024) on how students conduct cost-benefit analysis to decide AI use.
In today’s classrooms, generative AI tools like ChatGPT are becoming ubiquitous. But while these tools may appear to be merely technical aids, students’ decisions about how and when to use them are deeply value-based. Recent studies suggest that student AI use is shaped by a complex set of psychological, academic, and social factors. Rather than being passive users, students weigh the benefits and costs of AI use, often in the context of academic pressure, peer influence, and personal confidence. This raises important implications for educators who want to support healthy and productive use of AI in learning. This blog post synthesizes three research articles by Acosta-Enriquez and colleagues (2025), Mariñas and colleagues (2025), and Zhang and colleagues (2024) on how students conduct cost-benefit analysis to decide AI use.